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This document discusses the application of robotics for path planning. It begins by defining robotics and describing some common applications of robots, such as jobs that are dirty, dull or dangerous. It then focuses on path planning, which allows robots to find optimal paths between two points using a map of the environment. Several path planning algorithms are described, including Dijkstra's algorithm, A*, D* and RRT. Map representations like occupancy grids and topological maps are also discussed.

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Path Planning And Navigation

The document discusses various path planning techniques for mobile robots to navigate between a starting point and destination while avoiding collisions. It describes methods like visibility graphs, roadmaps, cell decomposition, and potential fields. It also covers implementing techniques like breadth-first search on visibility graphs and optimizing robot trajectories using factors like travel time, distance and sensor information.

Path Planning for Mobile Robots

An undergraduate thesis documenting my investigations about path planning techniques for mobile robots.

Chapter 2 robot kinematics

This document discusses robot kinematics and position analysis. It covers forward and inverse kinematics, including determining the position of a robot's hand given joint variables or calculating joint variables for a desired hand position. Different coordinate systems for representing robot positions are described, including Cartesian, cylindrical and spherical coordinates. The Denavit-Hartenberg representation for modeling robot kinematics is introduced, allowing the modeling of any robot configuration using transformation matrices.

Robotics: Introduction to Kinematics

The document discusses robot kinematics and control. It covers topics like coordinate frames, homogeneous transformations, forward and inverse kinematics, joint space trajectories, and cubic polynomial path planning. Specifically:
1) Kinematics is the study of robot motion without regard to forces or moments. It describes the spatial configuration using coordinate frames and homogeneous transformations.
2) Forward kinematics determines end effector position from joint angles. Inverse kinematics determines joint angles for a desired end effector position.
3) Joint space trajectories plan motion by describing joint angle profiles over time using functions like cubic polynomials and splines.
4) Cubic polynomials satisfy constraints like initial/final position and velocity to generate smooth motion profiles for a single revol

Robotics

This document summarizes robot motion analysis and kinematics. It discusses the historical perspective of robots, definitions of robots, basic robot components, robot configurations, types of joints and kinematics. It also covers topics such as transformations, rotation matrices, homogeneous transformations, and inverse kinematics of one and two link manipulators. The document provides examples and references on these topics.

Differential kinematics robotic

1. The document discusses differential kinematics and how it can be used to examine the pose velocities of robot frames. It also covers Jacobians and how they map between joint and Cartesian space velocities.
2. Specific topics covered include differential transformations, screw velocity matrices, relating velocities in different frames, determining Jacobians for serial and parallel robots, and examining robot singularities.
3. Singularities occur when the mechanism loses mobility in certain directions and joint rates will increase near these configurations. Workarounds include tooling design, changing to joint space control near singularities, and carefully planning paths.

Robotics position and orientation

The document discusses representing position and orientation of robotic systems using coordinate frames and homogeneous transformations. It introduces coordinate frames and describes how to represent position as a point and orientation as a set of axes. Rotations between frames can be represented by rotation matrices, and transformations between frames are described using homogeneous coordinates. Euler angles provide a method to represent orientation using three angles but require careful consideration of axis sequences due to non-commutativity of rotations.

Chapter 8 - Robot Control System

The document discusses control systems for robot manipulators. It covers open-loop and closed-loop control systems, with closed-loop being preferred using feedback. It describes using linear control techniques to approximate manipulator dynamics and designing controllers to meet stability and performance specifications. Common control techniques for manipulators are also summarized like PD, PID, state space control and adaptive/intelligent methods.

Path Planning And Navigation

The document discusses various path planning techniques for mobile robots to navigate between a starting point and destination while avoiding collisions. It describes methods like visibility graphs, roadmaps, cell decomposition, and potential fields. It also covers implementing techniques like breadth-first search on visibility graphs and optimizing robot trajectories using factors like travel time, distance and sensor information.

Path Planning for Mobile Robots

An undergraduate thesis documenting my investigations about path planning techniques for mobile robots.

Chapter 2 robot kinematics

This document discusses robot kinematics and position analysis. It covers forward and inverse kinematics, including determining the position of a robot's hand given joint variables or calculating joint variables for a desired hand position. Different coordinate systems for representing robot positions are described, including Cartesian, cylindrical and spherical coordinates. The Denavit-Hartenberg representation for modeling robot kinematics is introduced, allowing the modeling of any robot configuration using transformation matrices.

Robotics: Introduction to Kinematics

The document discusses robot kinematics and control. It covers topics like coordinate frames, homogeneous transformations, forward and inverse kinematics, joint space trajectories, and cubic polynomial path planning. Specifically:
1) Kinematics is the study of robot motion without regard to forces or moments. It describes the spatial configuration using coordinate frames and homogeneous transformations.
2) Forward kinematics determines end effector position from joint angles. Inverse kinematics determines joint angles for a desired end effector position.
3) Joint space trajectories plan motion by describing joint angle profiles over time using functions like cubic polynomials and splines.
4) Cubic polynomials satisfy constraints like initial/final position and velocity to generate smooth motion profiles for a single revol

Robotics

This document summarizes robot motion analysis and kinematics. It discusses the historical perspective of robots, definitions of robots, basic robot components, robot configurations, types of joints and kinematics. It also covers topics such as transformations, rotation matrices, homogeneous transformations, and inverse kinematics of one and two link manipulators. The document provides examples and references on these topics.

Differential kinematics robotic

1. The document discusses differential kinematics and how it can be used to examine the pose velocities of robot frames. It also covers Jacobians and how they map between joint and Cartesian space velocities.
2. Specific topics covered include differential transformations, screw velocity matrices, relating velocities in different frames, determining Jacobians for serial and parallel robots, and examining robot singularities.
3. Singularities occur when the mechanism loses mobility in certain directions and joint rates will increase near these configurations. Workarounds include tooling design, changing to joint space control near singularities, and carefully planning paths.

Robotics position and orientation

The document discusses representing position and orientation of robotic systems using coordinate frames and homogeneous transformations. It introduces coordinate frames and describes how to represent position as a point and orientation as a set of axes. Rotations between frames can be represented by rotation matrices, and transformations between frames are described using homogeneous coordinates. Euler angles provide a method to represent orientation using three angles but require careful consideration of axis sequences due to non-commutativity of rotations.

Chapter 8 - Robot Control System

The document discusses control systems for robot manipulators. It covers open-loop and closed-loop control systems, with closed-loop being preferred using feedback. It describes using linear control techniques to approximate manipulator dynamics and designing controllers to meet stability and performance specifications. Common control techniques for manipulators are also summarized like PD, PID, state space control and adaptive/intelligent methods.

Robot motion planning

This document discusses different path planning techniques for robot motion. It describes how configuration space represents all possible robot positions and orientations. Combinatorial planning methods decompose this space into cells to find obstacle-free paths, while sampling-based planning uses techniques like rapidly exploring random trees to quickly explore the space and find solutions without fully mapping obstacles. The document provides examples of how these methods are applied to problems in 2D and 3D worlds.

Robotics: 3D Movements

The document discusses different methods for representing 3D rotations and orientations, including rotation matrices, Euler angles, and quaternions. It explains that quaternions represent a rotation as a combination of a scalar and vector, and describe how to perform operations like rotation, composition, and normalization using quaternions. Quaternions use fewer parameters than rotation matrices but more easily represent arbitrary rotations and can be interpolated for smooth animation.

Robot manipulator

This document discusses the design and applications of industrial robot manipulators. It describes how a robotic arm is composed of rigid links connected by joints, and defines important robot terms like degrees of freedom, joint types, link parameters, and work volume. It also categorizes common robot system configurations and explains robot kinematics, dynamics, motion types, and trajectory planning.

Introduction to Mobile Robotics

The advent of Mobile Robotics changed the definition of robotics and brought in some very interesting technologies paving the way for cutting edge sciences like AI, Behaviour Based Systems, etc

Robot vision

This document discusses robot vision systems. It covers topics like industrial robotics, medical robotics, computer vision capabilities for robotics like object recognition and registration, vision sensors, issues with vision systems, and visual servoing techniques. Application examples discussed include using vision for accurate robot positioning, laparoscopic surgery, and tracking instruments.

Robot Arm Kinematics

The document discusses various kinematic problems in robotics including forward kinematics, inverse kinematics, and Denavit-Hartenberg notation. Forward kinematics determines the pose of the end-effector given the joint angles, while inverse kinematics determines the required joint angles to achieve a desired end-effector pose. Denavit-Hartenberg notation provides a systematic way to describe the geometry of robot links and joints. The document also covers numerical solutions to inverse kinematics for robots without closed-form solutions and kinematics of special robot types like under-actuated and redundant manipulators.

ROBOTICS-ROBOT KINEMATICS AND ROBOT PROGRAMMING

Forward Kinematics, Inverse Kinematics and Difference; Forward Kinematics and Reverse Kinematics of manipulators with Two, Three Degrees of Freedom (in 2 Dimension), Four Degrees of freedom (in 3 Dimension) Jacobians, Velocity and Forces-Manipulator Dynamics, Trajectory Generator, Manipulator Mechanism Design-Derivations and problems. Lead through Programming, Robot programming Languages-VAL Programming-Motion Commands, Sensor Commands, End Effector commands and simple Programs

Robots one day presentation

Introduction, Robot system, Operation control, Geometric configuration, Advantages, Robot Manipulator, Joints, Degree of freedom, Classification of Manipulator, End Effectors, Grippers, Design & working of Grippers, Applications of Robots, Robot Manufacturing systems, Robot Selection criteria,

Introduction to Robotics

This document provides information about an introduction to robotics course offered at Manipal University Jaipur. The course is titled MC2080 Introduction to Robotics and will be taught by Dr. Princy Randhawa. Topics covered in the course include robot sensors, actuators, motion analysis, dynamics, control, and applications. The course aims to help students understand robot structure, sensors, motion analysis, dynamics, and various robot applications. Assessment will include assignments, quizzes, seminars, and an end of semester exam.

Robotics: Forward and Inverse Kinematics

The document discusses forward kinematics, which is finding the position and orientation of the end effector given the joint angles of a robot. It covers different types of robot joints and configurations. It introduces the Denavit-Hartenberg coordinate system for defining the relationship between successive links of a robot. The document also discusses forward kinematic calculations, inverse kinematics, robot workspaces, and trajectory planning.

Dh parameters robotics

This document discusses key parameters and specifications for industrial robots. It describes six key parameters: (i) number of axes, (ii) load carrying capacity, (iii) maximum speed, (iv) reach and stroke, (v) tool orientation, and (vi) precision and accuracy. It provides details on each parameter, including defining major and minor axes, how load capacity depends on weight of the end effector, how speed is measured, differences between reach and stroke, and how tool orientation is determined by the robot's axes.

Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...

For navigation in complex environments, a robot needs to reach a compromise between the need for having efficient and optimized trajectories and the need for reacting to unexpected events. This paper presents a new sensor-based Path Planner which results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Voronoi Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted areas in order to obtain the path. The method combines map-based and sensor-based planning operations to provide a reliable motion plan, while it operates at the sensor frequency. The main characteristics are speed and reliability, since the map dimensions are reduced to an almost unidimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments where other methods of planning based on Voronoi do not work.

Pick & place robot ppt

Slide show demonstrating pick and place robot and its parts.
Also effects are implanted in the slide.
It can be helpful for students for academic projects.

Swarm ROBOTICS

This document provides an overview of swarm robotics. It begins with examples of decentralized control and self-organization in natural swarms like ants and bees. It then discusses how swarm robotics takes inspiration from these systems, using local control methods, local communication, and self-organization to complete collective tasks without centralized control. The rest of the document focuses on a proposed system for gesture recognition to allow human control of swarm robots. It describes hand detection, feature extraction, and hardware implementation using three foot-bot robots. It concludes with potential applications of swarm robotics and areas for future work.

Manipulator kinematics

1. The document discusses forward kinematics of robot manipulators. It defines key concepts like links, joints, Denavit-Hartenberg parameters, and homogeneous transformation matrices.
2. The forward kinematics problem is solved by assigning coordinate frames to each link and determining the transformation between frames using link variables and homogeneous transformations.
3. The position and orientation of the end effector is determined by multiplying the homogeneous transformation matrices representing each link transformation.

RMV robot programming

Methods of robot programming
Leadthrough programming methods
A robot program as a path in space
Motion interpolation
WAIT, SIGNAL and DELAY commands
Branching

Practical Swarm Optimization (PSO)

The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.

Inverse kinematics

This document discusses inverse kinematics, which is finding the joint parameters of a robot given the desired end effector position and orientation. There are three main solutions to inverse kinematics problems: geometric, algebraic, and numerical. Geometric methods use geometry to directly calculate joint angles. Algebraic methods use transformation matrices from forward kinematics. Numerical methods use iterative techniques like pseudo-inverse or Jacobian transpose to approximate solutions.

Mapping mobile robotics

This document describes a project that uses SLAM (Simultaneous Localization and Mapping) to create maps of an unknown environment using a Turtlebot mobile robot equipped with a Kinect sensor. The GMapping ROS package is used to implement SLAM and build occupancy grid maps while simultaneously localizing the robot. Issues arose from using the Kinect's RGB sensor instead of a laser scanner for mapping. The resulting maps showed the robot's trajectory and obstacles but were less precise than if a laser scanner was used. Autonomous navigation on the created maps demonstrated the basic SLAM approach.

An introduction to robotics classification, kinematics and hardware

Introduction to robots, classification of robots, Kinematics of robot manipulator, Introduction to a mobile robot, kinematics of mobile robot, sensors used in robots, microcontrollers for robots

artifical intelligence final paper

This document compares three popular path planning algorithms: A*, greedy best first search, and jump point search. It implements the algorithms in MATLAB using grid-based maps with random start/goal points and static obstacles. The algorithms are evaluated based on computational complexity, time complexity, and space complexity. Jump point search generally has the best performance out of the three algorithms as it can make long jumps along straight lines in the grid, exploring fewer nodes than A*.

A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...

This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.

Robot motion planning

This document discusses different path planning techniques for robot motion. It describes how configuration space represents all possible robot positions and orientations. Combinatorial planning methods decompose this space into cells to find obstacle-free paths, while sampling-based planning uses techniques like rapidly exploring random trees to quickly explore the space and find solutions without fully mapping obstacles. The document provides examples of how these methods are applied to problems in 2D and 3D worlds.

Robotics: 3D Movements

The document discusses different methods for representing 3D rotations and orientations, including rotation matrices, Euler angles, and quaternions. It explains that quaternions represent a rotation as a combination of a scalar and vector, and describe how to perform operations like rotation, composition, and normalization using quaternions. Quaternions use fewer parameters than rotation matrices but more easily represent arbitrary rotations and can be interpolated for smooth animation.

Robot manipulator

This document discusses the design and applications of industrial robot manipulators. It describes how a robotic arm is composed of rigid links connected by joints, and defines important robot terms like degrees of freedom, joint types, link parameters, and work volume. It also categorizes common robot system configurations and explains robot kinematics, dynamics, motion types, and trajectory planning.

Introduction to Mobile Robotics

The advent of Mobile Robotics changed the definition of robotics and brought in some very interesting technologies paving the way for cutting edge sciences like AI, Behaviour Based Systems, etc

Robot vision

This document discusses robot vision systems. It covers topics like industrial robotics, medical robotics, computer vision capabilities for robotics like object recognition and registration, vision sensors, issues with vision systems, and visual servoing techniques. Application examples discussed include using vision for accurate robot positioning, laparoscopic surgery, and tracking instruments.

Robot Arm Kinematics

The document discusses various kinematic problems in robotics including forward kinematics, inverse kinematics, and Denavit-Hartenberg notation. Forward kinematics determines the pose of the end-effector given the joint angles, while inverse kinematics determines the required joint angles to achieve a desired end-effector pose. Denavit-Hartenberg notation provides a systematic way to describe the geometry of robot links and joints. The document also covers numerical solutions to inverse kinematics for robots without closed-form solutions and kinematics of special robot types like under-actuated and redundant manipulators.

ROBOTICS-ROBOT KINEMATICS AND ROBOT PROGRAMMING

Forward Kinematics, Inverse Kinematics and Difference; Forward Kinematics and Reverse Kinematics of manipulators with Two, Three Degrees of Freedom (in 2 Dimension), Four Degrees of freedom (in 3 Dimension) Jacobians, Velocity and Forces-Manipulator Dynamics, Trajectory Generator, Manipulator Mechanism Design-Derivations and problems. Lead through Programming, Robot programming Languages-VAL Programming-Motion Commands, Sensor Commands, End Effector commands and simple Programs

Robots one day presentation

Introduction, Robot system, Operation control, Geometric configuration, Advantages, Robot Manipulator, Joints, Degree of freedom, Classification of Manipulator, End Effectors, Grippers, Design & working of Grippers, Applications of Robots, Robot Manufacturing systems, Robot Selection criteria,

Introduction to Robotics

This document provides information about an introduction to robotics course offered at Manipal University Jaipur. The course is titled MC2080 Introduction to Robotics and will be taught by Dr. Princy Randhawa. Topics covered in the course include robot sensors, actuators, motion analysis, dynamics, control, and applications. The course aims to help students understand robot structure, sensors, motion analysis, dynamics, and various robot applications. Assessment will include assignments, quizzes, seminars, and an end of semester exam.

Robotics: Forward and Inverse Kinematics

The document discusses forward kinematics, which is finding the position and orientation of the end effector given the joint angles of a robot. It covers different types of robot joints and configurations. It introduces the Denavit-Hartenberg coordinate system for defining the relationship between successive links of a robot. The document also discusses forward kinematic calculations, inverse kinematics, robot workspaces, and trajectory planning.

Dh parameters robotics

This document discusses key parameters and specifications for industrial robots. It describes six key parameters: (i) number of axes, (ii) load carrying capacity, (iii) maximum speed, (iv) reach and stroke, (v) tool orientation, and (vi) precision and accuracy. It provides details on each parameter, including defining major and minor axes, how load capacity depends on weight of the end effector, how speed is measured, differences between reach and stroke, and how tool orientation is determined by the robot's axes.

Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...

For navigation in complex environments, a robot needs to reach a compromise between the need for having efficient and optimized trajectories and the need for reacting to unexpected events. This paper presents a new sensor-based Path Planner which results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Voronoi Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted areas in order to obtain the path. The method combines map-based and sensor-based planning operations to provide a reliable motion plan, while it operates at the sensor frequency. The main characteristics are speed and reliability, since the map dimensions are reduced to an almost unidimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments where other methods of planning based on Voronoi do not work.

Pick & place robot ppt

Slide show demonstrating pick and place robot and its parts.
Also effects are implanted in the slide.
It can be helpful for students for academic projects.

Swarm ROBOTICS

This document provides an overview of swarm robotics. It begins with examples of decentralized control and self-organization in natural swarms like ants and bees. It then discusses how swarm robotics takes inspiration from these systems, using local control methods, local communication, and self-organization to complete collective tasks without centralized control. The rest of the document focuses on a proposed system for gesture recognition to allow human control of swarm robots. It describes hand detection, feature extraction, and hardware implementation using three foot-bot robots. It concludes with potential applications of swarm robotics and areas for future work.

Manipulator kinematics

1. The document discusses forward kinematics of robot manipulators. It defines key concepts like links, joints, Denavit-Hartenberg parameters, and homogeneous transformation matrices.
2. The forward kinematics problem is solved by assigning coordinate frames to each link and determining the transformation between frames using link variables and homogeneous transformations.
3. The position and orientation of the end effector is determined by multiplying the homogeneous transformation matrices representing each link transformation.

RMV robot programming

Methods of robot programming
Leadthrough programming methods
A robot program as a path in space
Motion interpolation
WAIT, SIGNAL and DELAY commands
Branching

Practical Swarm Optimization (PSO)

The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.

Inverse kinematics

This document discusses inverse kinematics, which is finding the joint parameters of a robot given the desired end effector position and orientation. There are three main solutions to inverse kinematics problems: geometric, algebraic, and numerical. Geometric methods use geometry to directly calculate joint angles. Algebraic methods use transformation matrices from forward kinematics. Numerical methods use iterative techniques like pseudo-inverse or Jacobian transpose to approximate solutions.

Mapping mobile robotics

This document describes a project that uses SLAM (Simultaneous Localization and Mapping) to create maps of an unknown environment using a Turtlebot mobile robot equipped with a Kinect sensor. The GMapping ROS package is used to implement SLAM and build occupancy grid maps while simultaneously localizing the robot. Issues arose from using the Kinect's RGB sensor instead of a laser scanner for mapping. The resulting maps showed the robot's trajectory and obstacles but were less precise than if a laser scanner was used. Autonomous navigation on the created maps demonstrated the basic SLAM approach.

An introduction to robotics classification, kinematics and hardware

Introduction to robots, classification of robots, Kinematics of robot manipulator, Introduction to a mobile robot, kinematics of mobile robot, sensors used in robots, microcontrollers for robots

Robot motion planning

Robot motion planning

Robotics: 3D Movements

Robotics: 3D Movements

Robot manipulator

Robot manipulator

Introduction to Mobile Robotics

Introduction to Mobile Robotics

Robot vision

Robot vision

Robot Arm Kinematics

Robot Arm Kinematics

ROBOTICS-ROBOT KINEMATICS AND ROBOT PROGRAMMING

ROBOTICS-ROBOT KINEMATICS AND ROBOT PROGRAMMING

Robots one day presentation

Robots one day presentation

Introduction to Robotics

Introduction to Robotics

Robotics: Forward and Inverse Kinematics

Robotics: Forward and Inverse Kinematics

Dh parameters robotics

Dh parameters robotics

Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...

Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...

Pick & place robot ppt

Pick & place robot ppt

Swarm ROBOTICS

Swarm ROBOTICS

Manipulator kinematics

Manipulator kinematics

RMV robot programming

RMV robot programming

Practical Swarm Optimization (PSO)

Practical Swarm Optimization (PSO)

Inverse kinematics

Inverse kinematics

Mapping mobile robotics

Mapping mobile robotics

An introduction to robotics classification, kinematics and hardware

An introduction to robotics classification, kinematics and hardware

artifical intelligence final paper

This document compares three popular path planning algorithms: A*, greedy best first search, and jump point search. It implements the algorithms in MATLAB using grid-based maps with random start/goal points and static obstacles. The algorithms are evaluated based on computational complexity, time complexity, and space complexity. Jump point search generally has the best performance out of the three algorithms as it can make long jumps along straight lines in the grid, exploring fewer nodes than A*.

A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...

This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.

DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM

The document discusses the design and implementation of a path planning algorithm for a wheeled mobile robot in a known dynamic environment. It describes using an A* algorithm at a central control station to calculate the shortest path for the robot. If obstacles are detected, the robot's location and obstacle information is sent to update the environment map. The control station then recalculates the new shortest path for the robot. The system was tested experimentally and in simulation, showing it can effectively calculate the shortest path in a dynamic environment.

A simulated motion planning algorithm in 2 d

This paper describes a computer simulated artificial intelligence (AI) agent moving in 2D and 3D
environments. In the presented algorithm, the agent can take two operating modes: Manual Mode and Map
or Autopilot mode. The user can control the agent fully in a manual mode by moving it in all possible
directions depending on the environment. Obstacles are sensed by the agent from a certain distance and
are avoided accordingly. Another important mode is the Map mode. In this mode the user create a custom
map where initial position and a goal position are set. The user is able also to assign sudden and
predefined obstacles. By finding the shortest path, the agent moves to the goal position avoiding any
obstacles on its path.
The paper documents a set of algorithms that can help the agent to find the shortest path to a predefined
target location in a complex 3D environment, such as cities and mountains, avoiding all predefined and
sudden obstacles. These obstacles are avoided also in manual mode and the agent moves automatically to a
safe location. The implementation is based on the Hill Climbing algorithm (descent version), where the
agent finds its path to the global minimum (target goal). The Map generation algorithm, which is used to
assign costs to every location in the map, avoids a lot of the limitations of Hill Climbing.

Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...

An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.

AbstractWe design an software to find optimal(shortest) path .docx

The document discusses using the A* algorithm to find the optimal shortest path between two locations on a university campus map. It describes implementing the A* algorithm in software to visualize the shortest path on a graphical map. The software imports necessary libraries, defines data structures to represent points and positions on the map, and uses a min heap data structure to efficiently find the lowest cost node during searching. It provides pseudocode and descriptions of the key steps in applying the A* algorithm to solve the route planning problem between two points on the map.

Path planning all algos

The document discusses path planning for mobile robots to navigate around stationary obstacles. It describes the goal of path planning as finding an optimal collision-free path between two points. Common map representations for path planning include discrete approximations using grids or graphs and continuous approximations using polygons. Popular path planning algorithms for discrete environments include Dijkstra's algorithm, A*, and RRT, while potential fields are often used for continuous environments.

Iaetsd modified artificial potential fields algorithm for mobile robot path ...

This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.

Urban Bus Route Planning Using Reverse Labeling Dijkstra Algorithm for Tempor...

The document discusses using the Reverse Labeling Dijkstra Algorithm (RLDA) to optimize urban bus route planning by considering real-time traffic conditions. RLDA is an adaptation of Dijkstra's algorithm that finds the shortest path from the destination node backwards towards the source node. This allows it to consider arc attributes representing traffic congestion levels. The document presents the mathematical model and steps of the RLDA algorithm. It then discusses simulating the RLDA approach on a real-time road network to determine efficient bus routes.

Trajectory Planning Through Polynomial Equation

Geometrical Significance Of Trajectory Planning Through
Polynomial Equation – Customized RPPPRR Model Robot

What is an occupancy grid and how can it be used What are some of t.pdf

Occupancy grid mapping refers to algorithms that generate maps from noisy sensor data, representing the environment as a grid of binary variables indicating obstacle presence. It computes estimates of these variables while accounting for uncertainty in robot pose. Major components include interpreting sensor data, integrating measurements over time, estimating robot position, and strategies for exploration. Common issues include representing the continuous environment with a discrete grid and dealing with noise in sensor data and pose estimates.

Optimizing GIS based Systems

GIS based systems can be optimized to work better than ever. And this PowerPoint presentation has a few of the ways to show the same

Mobile robot path planning using ant colony optimization

IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology

Shortest route finding using an object oriented database approach

This document presents an object-oriented road network database model for finding the shortest route. It divides the road network into hierarchical levels based on administrative divisions, and divides roads into segments. Common shortest path algorithms like Dijkstra's algorithm are inefficient for complex road networks. The proposed model associates geographic and hierarchical knowledge with the road network data to more efficiently find routes. It describes designing the data model by identifying classes like containers for regions and non-containers for roads, and defining their relationships and responsibilities. The implementation of this model aims to overcome limitations of traditional algorithms by leveraging the object-oriented representation of the road network.

Design and implementation of path planning algorithm for wheeled mobile robot...

Abstract Path planning in mobile robots must ensure optimality of the path. The optimality achieved may be in path, time, energy consumed etc. Path planning in robots also depends on the environment in which it operates like, static or dynamic, known or unknown etc. Global path planning using A* algorithm and genetic algorithm is investigated in this paper. A known dynamic environment, in which a control station will compute the shortest path and communicate to the mobile robot and the mobile robot, will traverse through this path to reach the goal. The control station will keep track of the path traversed by the robot. The mobile robot navigates through the shortest path and if the robot detects any obstacle in the destined path, the mobile robot will update the information about the environment and this information together with the current location will be communicated to the control station. Then the control station, with the updated map of the environment and new starting location and destination recalculates the new shortest path, if any, and will communicate to the mobile robot so that it can reach the destination. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results demonstrate that the technique effectively calculates the shortest path in known dynamic environment and allows the robot to quickly accomplish the mission.

Design and implementation of path planning algorithm for wheeled mobile robot...

Design and implementation of path planning algorithm for wheeled mobile robot...eSAT Publishing House

IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.Robotics Navigation

This document discusses various strategies for robot navigation, including reactive navigation using Braitenberg vehicles and simple automata, as well as map-based planning algorithms. Reactive navigation relies on direct sensor-motor connections to navigate without an internal world model, while map-based planning uses a map representation and algorithms like the distance transform or D* to find optimal paths between points. The document provides examples and explanations of different navigation techniques.

Motion of Robots in a Non Rectangular Workspace

Motion of Robots in a Non Rectangular WorkspaceInternational Journal of Engineering Inventions www.ijeijournal.com

This document summarizes a research paper about motion planning for multiple robots in a non-rectangular workspace. The paper aims to utilize advantages of both centralized and decentralized planning approaches to minimize limitations. Collision detection is performed by checking if new robot positions overlap with other robot areas. Path planning ensures robots avoid boundaries while reaching destinations. Simulation results show robots reaching targets over time. Adding robots or changing boundaries has minimal effect on planning time. The research is limited to geometric aspects rather than physical robot interaction dynamics.Implementation of D* Path Planning Algorithm with NXT LEGO Mindstorms Kit for...

Autonomous Robots use various Path Planning
algorithms to navigate, to the target point. In the real world
situation robot may not have a complete picture of the obstacles
in its environment. The classical path planning algorithms
such as A*, D* are cost based where the shortest path to the
target is calculated based on the distance to be travelled. In
order to provide real time shortest path solutions, cost
computation has to be redone whenever new obstacles are
identified. D* is a potential search algorithm, capable of
planning shortest path in unknown, partially known and
changing environments. This paper brings out the simulation
of D* algorithm in C++ and the results for different test cases.
It also elucidates the implementation of the algorithm with
NXT LEGO Mindstorms kit using RobotC language and
evaluation in real time scenario.

A survey of geographic routing protocols for Vehicular Ad Hoc Networks (VANETs)

This document summarizes and classifies several geographic routing protocols for vehicular ad hoc networks (VANETs). It discusses protocols for unicast routing like GPSR, GSR, GPCR, and A-STAR, which use location information and restrictions like street maps to select optimal paths between vehicles. It also covers protocols for broadcast, like BROADCOMM and UMB, which use techniques like clustering and selective forwarding to disseminate messages efficiently. Finally, it discusses geocast protocols like IVG for alerting vehicles in a specified area, often using controlled flooding restricted to a forwarding zone.

artifical intelligence final paper

artifical intelligence final paper

A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...

A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...

DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM

DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM

A simulated motion planning algorithm in 2 d

A simulated motion planning algorithm in 2 d

Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...

Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...

AbstractWe design an software to find optimal(shortest) path .docx

AbstractWe design an software to find optimal(shortest) path .docx

Path planning all algos

Path planning all algos

Iaetsd modified artificial potential fields algorithm for mobile robot path ...

Iaetsd modified artificial potential fields algorithm for mobile robot path ...

Urban Bus Route Planning Using Reverse Labeling Dijkstra Algorithm for Tempor...

Urban Bus Route Planning Using Reverse Labeling Dijkstra Algorithm for Tempor...

Trajectory Planning Through Polynomial Equation

Trajectory Planning Through Polynomial Equation

What is an occupancy grid and how can it be used What are some of t.pdf

What is an occupancy grid and how can it be used What are some of t.pdf

Optimizing GIS based Systems

Optimizing GIS based Systems

Mobile robot path planning using ant colony optimization

Mobile robot path planning using ant colony optimization

Shortest route finding using an object oriented database approach

Shortest route finding using an object oriented database approach

Design and implementation of path planning algorithm for wheeled mobile robot...

Design and implementation of path planning algorithm for wheeled mobile robot...

Design and implementation of path planning algorithm for wheeled mobile robot...

Design and implementation of path planning algorithm for wheeled mobile robot...

Robotics Navigation

Robotics Navigation

Motion of Robots in a Non Rectangular Workspace

Motion of Robots in a Non Rectangular Workspace

Implementation of D* Path Planning Algorithm with NXT LEGO Mindstorms Kit for...

Implementation of D* Path Planning Algorithm with NXT LEGO Mindstorms Kit for...

A survey of geographic routing protocols for Vehicular Ad Hoc Networks (VANETs)

A survey of geographic routing protocols for Vehicular Ad Hoc Networks (VANETs)

Generative AI leverages algorithms to create various forms of content

What is Generative AI?

Virtualization: A Key to Efficient Cloud Computing

The document discusses various types of virtualization used in cloud computing. It describes virtualization as a technique that allows sharing of physical resources among multiple customers. There are two main types of hypervisors - Type 1 hypervisors run directly on hardware while Type 2 hypervisors run on a host operating system. The document also summarizes different types of virtualization including hardware, software, memory, storage, network, and desktop virtualization. Benefits of virtualization include improved efficiency, outsourcing of hardware costs, testing software in isolated environments, and emulating machines beyond physical availability.

Automating the Cloud: A Deep Dive into Virtual Machine Provisioning

Virtual machine provisioning allows users to quickly provision new virtual machines through a self-service interface in minutes, rather than the days it previously took to provision physical servers. Virtual machine migration also allows live migration of virtual machines between physical hosts in milliseconds for maintenance or upgrades. Standards like OVF and OCCI help ensure interoperability and portability of virtual machines across platforms. The virtual machine lifecycle includes provisioning, serving requests, and deprovisioning resources when the service is ended.

Harnessing the Power of Google Cloud Platform: Strategies and Applications

The document discusses Google Cloud Platform (GCP), a suite of cloud computing services provided by Google. It provides infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). GCP allows users to access computing power, storage, databases, and other applications through remote servers on the internet. It offers advantages like scalability, security, redundancy, and cost-effectiveness compared to traditional data centers. Example applications of GCP include enabling collaborative document editing in real-time.

Scheduling in Cloud Computing

Scheduling refers to allocating computing resources like processor time and memory to processes. In cloud computing, scheduling maps jobs to virtual machines. There are two levels of scheduling - at the host level to distribute VMs, and at the VM level to distribute tasks. Common scheduling algorithms include first-come first-served (FCFS), shortest job first (SJF), round robin, and max-min. FCFS prioritizes older jobs but has high wait times. SJF prioritizes shorter jobs but can starve longer ones. Max-min prioritizes longer jobs to optimize resource use. The choice depends on goals like throughput, latency, and fairness.

Cloud-Case study

This document provides a template for submitting case studies to a case study compendium on cloud computing solutions. The template requests information on the customer organization, industry, location, the cloud solution provider, area of application of the cloud solution, challenges addressed, objectives, timeline of implementation, solution approach, challenges during implementation, benefits to the customer, innovation enabled, partnerships involved, and a customer testimonial. It requests details on the cloud solution type (IaaS, PaaS, or SaaS), quantitative and qualitative benefits realized by the customer, and how the solution helped boost innovation. Contact details of the submitter are also requested. The focus is on how cloud platforms and solutions enabled customer enterprises to innovate and

RAID

RAID (Redundant Array of Independent Disks) uses multiple hard disks or solid-state drives to protect data by storing it across the drives in a way that if one drive fails, the data can still be accessed from the other drives. There are different RAID levels that provide varying levels of data protection and performance. A RAID controller manages the drives in an array, presenting them as a single logical drive and improving performance and reliability. Common RAID levels include RAID 0 for performance without redundancy, RAID 1 for disk mirroring, and RAID 5 for striping with parity data distributed across drives. [/SUMMARY]

Load balancing in cloud computing.pptx

Cloud load balancing distributes workloads and network traffic across computing resources in a cloud environment to improve performance and availability. It routes incoming traffic to multiple servers or other resources while balancing the load. Load balancing in the cloud is typically software-based and offers benefits like scalability, reliability, reduced costs, and flexibility compared to traditional hardware-based load balancing. Common cloud providers like AWS, Google Cloud, and Microsoft Azure offer multiple load balancing options that vary based on needs and network layers.

Cluster Computing

A computer cluster is a set of computers that work together so that they can be viewed as a single system.

ITU-T requirement for cloud and cloud deployment model

List and explain the functional requirements for networking as per the ITU-T technical report. List and explain cloud deployment models and list relative strengths and weaknesses of the deployment models with neat diagram.

Leetcode Problem Solution

The document contains descriptions of several LeetCode problems ranging from Medium to Hard difficulty. It provides details about the Maximum Level Sum of a Binary Tree, Jump Game III, Minesweeper, Binary Tree Level Order Traversal, Number of Operations to Make Network Connected, Open the Lock, Sliding Puzzle, and Trapping Rain Water II problems. It also includes pseudocode and explanations for solving the Number of Operations to Make Network Connected and Open the Lock problems.

Leetcode Problem Solution

The document discusses three problems: (1) finding the cheapest flight route between two cities with at most k stops using DFS and pruning; (2) merging k sorted linked lists into one sorted list using a priority queue; (3) using a sequence of acceleration (A) and reversing (R) instructions to reach a target position in the shortest number of steps for a car that can move to negative positions.

Trie Data Structure

Trie Data Structure
LINK: https://leetcode.com/tag/trie/
Easy:
1. Longest Word in Dictionary
Medium:
1. Count Substrings That Differ by One Character
2. Replace Words
3. Top K Frequent Words
4. Maximum XOR of Two Numbers in an Array
5. Map Sum Pairs
Hard:
1. Concatenated Words
2. Word Search II

Reviewing basic concepts of relational database

The document discusses the basics of relational databases. It defines what a database is, the advantages it provides over file-based data storage, and some disadvantages. It also covers relational database concepts like tables, records, fields, keys, and normalization. The document explains how to design a relational database by determining the purpose and entities, modeling relationships with E-R diagrams, and following steps to normalize the data.

Reviewing SQL Concepts

https://youtu.be/0l83FZfrerg
What is SQL?
What Can SQL do?
SQL Syntax
Semicolon after SQL Statements?

Advanced database protocols

https://youtu.be/yP14a2Qzx8c
DATABASE PROTOCOLS OVERVIEW
Oracle Two-Tier
Two-Tier and Three-Tier Computing Models

Measures of query cost

The document discusses measures of query cost in database management systems. It explains that query cost can be measured by factors like the number of disk accesses, size of the table, and time taken by the CPU. It further breaks down disk access time into components like seek time, rotational latency, and sequential vs. random I/O. The document then provides an example formula to calculate estimated query cost based on these components.

Involvement of WSN in Smart Cities

This document discusses how wireless sensor networks (WSNs) can be used in smart city applications. It first defines WSNs as self-configured, infrastructure-less networks that use sensors to monitor conditions like temperature, sound, and pollution. It then discusses how WSNs can influence lifestyle by enabling applications in areas like healthcare, transportation, the environment and more. Finally, it discusses how WSNs are a primary strength for smart cities by allowing remote and cost-effective monitoring of infrastructure and resources across applications like smart water, smart grid, and smart transportation.

Data Structure and its Fundamentals

The document provides an overview and syllabus for a course on fundamentals of data structures. It covers topics such as linear and non-linear data structures including arrays, stacks, queues, linked lists, trees and graphs. It describes various data types in C like integers, floating-point numbers, characters and enumerated types. It also discusses operations on different data structures and analyzing algorithm complexity.

WORKING WITH FILE AND PIPELINE PARAMETER BINDING

EXPORTING USER INFORMATION TO A FILE
SEND OUTPUT CONSISTING OF PIPELINE DATA
PREDICTING PIPELINE BEHAVIOR

Generative AI leverages algorithms to create various forms of content

Generative AI leverages algorithms to create various forms of content

Virtualization: A Key to Efficient Cloud Computing

Virtualization: A Key to Efficient Cloud Computing

Automating the Cloud: A Deep Dive into Virtual Machine Provisioning

Automating the Cloud: A Deep Dive into Virtual Machine Provisioning

Harnessing the Power of Google Cloud Platform: Strategies and Applications

Harnessing the Power of Google Cloud Platform: Strategies and Applications

Scheduling in Cloud Computing

Scheduling in Cloud Computing

Cloud-Case study

Cloud-Case study

RAID

RAID

Load balancing in cloud computing.pptx

Load balancing in cloud computing.pptx

Cluster Computing

Cluster Computing

ITU-T requirement for cloud and cloud deployment model

ITU-T requirement for cloud and cloud deployment model

Leetcode Problem Solution

Leetcode Problem Solution

Leetcode Problem Solution

Leetcode Problem Solution

Trie Data Structure

Trie Data Structure

Reviewing basic concepts of relational database

Reviewing basic concepts of relational database

Reviewing SQL Concepts

Reviewing SQL Concepts

Advanced database protocols

Advanced database protocols

Measures of query cost

Measures of query cost

Involvement of WSN in Smart Cities

Involvement of WSN in Smart Cities

Data Structure and its Fundamentals

Data Structure and its Fundamentals

WORKING WITH FILE AND PIPELINE PARAMETER BINDING

WORKING WITH FILE AND PIPELINE PARAMETER BINDING

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...Paris Salesforce Developer Group

Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.Transformers design and coooling methods

Transformer Design

NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT

NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT

Call For Paper -3rd International Conference on Artificial Intelligence Advan...

* Registration is currently open *
Call for Research Papers!!!
Free – Extended Paper will be published as free of cost.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)
July 13 ~ 14, 2024, Virtual Conference
Webpage URL: https://aiad2024.org/index
Submission Deadline: June 22, 2024
Submission System URL:
https://aiad2024.org/submission/index.php
Contact Us:
Here's where you can reach us : aiad@aiad2024.org (or) aiadconference@yahoo.com
WikiCFP URL: http://wikicfp.com/cfp/servlet/event.showcfp?eventid=180509©ownerid=171656
#artificialintelligence #softcomputing #machinelearning #technology #datascience #python #deeplearning #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #engineering #robot #datascientist #software #automation #analytics #ml #pythonprogramming #programmer #digitaltransformation #developer #promptengineering #generativeai #genai #chatgpt #artificial #intelligence #datamining #networkscommunications #robotics #callforsubmission #submissionsopen #deadline #opencall #virtual #conference

Design and optimization of ion propulsion drone

Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.

Supermarket Management System Project Report.pdf

Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.

Generative AI Use cases applications solutions and implementation.pdf

Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

原版一模一样【微信：741003700 】【(uofo毕业证书)美国俄勒冈大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
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二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
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校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理

原版一模一样【微信：741003700 】【(osu毕业证书)美国俄勒冈州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
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校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

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Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...

Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.

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Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.

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原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样

原件一模一样【微信：bwp0011】《(Humboldt毕业证书)柏林大学毕业证学位证》【微信：bwp0011】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
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4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

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一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

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- 1. APPLICATION OF ROBOTICS FOR PATH PLANNING Hitesh Mohapatra https://www.linkedin.com/in/hiteshmohapatra/
- 2. WHAT IS ROBOTICS? Robotics is the engineering science and technology which involves the conception , design , operation and manufacture of robots and computer system for their control , sensory feedback, and information processing . Electronics , mechanics and software are brought together by robotics .
- 3. APPLICATION OF ROBOTICS Robots are used for jobs that are dirty ,dull and dangerous. Today robotics have many different application areas. some of those are Outer space applications Military applications Intelligent home applications Industry Health service
- 4. Robotics of path planning Path planning is an important primitive for autonomous mobile robots that lets robots find the shortest or otherwise optimal path between two points . Path planning requires a map of the environment and the robot to be aware of its location with respect the map. The goals of the path planning are Introduce suitable map representation Explain basic path-planning algorithms ranging from Dijkstra, to A*, D* and RRT Introduce variation of the path planning problem, such as coverage path planning .
- 5. Map representation Map representation have two complementary approaches: discrete and continuous approximations. In a discrete approximation, a map is sub-divided into chunks of equal (e.g., a grid or hexagonal map) or differing sizes (e.g., rooms in a building). The latter maps are also known as topological maps. Discrete maps lend themselves well to a graph representation. Here, every chunk of the map corresponds to a vertex (also known as “node”), which are connected by edges, if a robot can navigate from one vertex to the other. For example a road-map is a topological map, with intersections as vertices and roads as edges. Computationally, a graph might be stored as an adjacency or incidence list/matrix.
- 6. A continuous approximation requires the definition of inner (obstacles) and outer boundaries, typically in the form of a polygon, whereas paths can be encoded as sequences of real numbers. Discrete maps are the dominant representation in robotics. The most common map is the occupancy grid map. In a grid map, the environment is discretized into squares of arbitrary resolution, e.g. 1cm x 1cm, on which obstacles are marked. In a probabilistic occupancy grid, grid cells can also be marked with the probability that they contain an obstacle. This is particularly important when the position of the robot that senses an obstacle is uncertain. Disadvantages of grid maps are their large memory requirements as well as computational time to traverse data structures with large numbers of vertices. A solution to the latter problem are topological maps that encode entire rooms as vertices and use edges to indicate navigable connections between them. There is no silver bullet, and each application might require a different solution that could be a combination of different map types.
- 7. Path-Planning Algorithms The problem to find a “shortest” path from one vertex to another through a connected graph is of interest in multiple domains, most prominently in the internet, where it is used to find an optimal route for a data packet. The term “shortest” refers here to the minimum cumulative edge cost, which could be physical distance (in a robotic application), delay (in a networking application) or any other metric that is important for a specific application.
- 8. One of the earliest and simplest algorithms is Dijkstra’s algorithm. Starting from the initial vertex where the path should start, the algorithm marks all direct neighbours of the initial vertex with the cost to get there. It then proceeds from the vertex with the lowest cost to all of its adjacent vertices and marks them with the cost to get to them via itself if this cost is lower. Once all neighbours of a vertex have been checked, the algorithm proceeds to the vertex with the next lowest cost. A more detailed description of this algorithm is provided here . Once the algorithm reaches the goal vertex, it terminates and the robot can follow the edges pointing towards the lowest edge cost.
- 9. Dijkstra’s algorithm for robotic path planning,
- 10. Illustration of A* planning,
- 11. A* and D* become computationally expensive when either the search space is large, e.g., due to a fine-grain resolution required for the task, or the dimensions of the search problem are high, e.g. when planning for an arm with multiple degrees of freedom. A more recent development known as Rapidly-Exploring Random Trees (RRT) addresses this problem by using a randomized approach that aims at quickly exploring a large area of the search space with iterative refinement. For this, the algorithm selects a random point in the environment and connects it to the initial vertex. Subsequent random points are then connected to the closest vertex in the emerging graph. The graph is then connected to the goal node, whenever a point in the tree comes close enough given some threshold. Although generally a coverage algorithm (see also below), RRT can be used for path- planning by maintaining the cost-to-start on each added point, and biasing the selection of points to occasionally falling close to the goal. RRT can also be used to take into account non-holonomic contraints of a specific platform when generating the next random way-point. One way to implement this is to calculate the next random point by applying random values to the robot’s actuators and use the forward kinematics to calculate the next point. Most recently, variations of RRT have been proposed that will eventually find an optimal solution. Nevertheless, although RRT quickly finds some solution, smooth paths usually require additional search algorithms that start from an initial estimate provided by RRT.
- 12. Example of RRT
- 13. Robot embodiment In order to deal with the physical embodiment of the robot, which complicates the path-planning process, the robot is reduced to a point-mass and all the obstacles in the environment are grown by half of the longest extension of the robot from its centre. This representation is known as configuration space as it reduces the representation of the robot to its x and y coordinates in the plane. Most planning algorithms also do not consider the orientation of the robot. If this is desired, an additional dimension needs to be introduced into the configuration space.
- 14. Other path-planning applications Once the environment has been discretized into a graph we can employ other algorithms from graph theory to plan desirable robot trajectories For example, floor coverage can be achieved by performing a depth-first search (DFS) or a breadth-first-search (BFS) on a graph where each vertex has the size of the coverage tool of the robot.
- 15. THANK YOU