The document describes the Follow the Gap Method (FGM) for dynamic path planning of mobile robots. FGM constructs a gap array between obstacles based on a point robot approach. It determines the maximum gap considering the goal point location and calculates the angle to the center of the maximum gap for the robot to proceed towards. FGM provides a purely reactive path that avoids obstacles with maximum distance while considering measurement constraints and nonholonomic constraints of robots. It has advantages over other methods like APF in avoiding local minima problems and generating safer paths with similar travel lengths. The only limitation is its inability to escape dead-end scenarios but that can be remedied through hybridization with other local planning techniques.
DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM NITISH K
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.
Knowledge Based Genetic Algorithm for Robot Path PlanningTarundeep Dhot
This document summarizes a research paper that proposes a knowledge-based genetic algorithm for mobile robot path planning. The algorithm uses a grid-based representation and specialized genetic operators informed by domain knowledge. Simulation results show the algorithm can find optimal or near-optimal paths in static and dynamic environments. Comparisons demonstrate the specialized operators improve GA performance over standard operators. Future work could better utilize domain knowledge and handle changes in dynamic environments.
A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...CSCJournals
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.
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.
Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...Waqas Tariq
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.
This document discusses navigation techniques for robots. It begins by reviewing localization methods like Markov localization and Kalman filters. It then discusses two approaches for navigation - local planning, which involves moving towards a goal while avoiding obstacles, and global planning, which calculates an offline shortest path. Various global planning techniques are described, including graph-based methods like Dijkstra's and A* algorithms, as well as cell decomposition and potential field planning. Potential field planning calculates a virtual force pulling the robot based on distance to obstacles and goal direction. The document concludes by discussing debates around social and technical topics related to robotics.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM NITISH K
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.
Knowledge Based Genetic Algorithm for Robot Path PlanningTarundeep Dhot
This document summarizes a research paper that proposes a knowledge-based genetic algorithm for mobile robot path planning. The algorithm uses a grid-based representation and specialized genetic operators informed by domain knowledge. Simulation results show the algorithm can find optimal or near-optimal paths in static and dynamic environments. Comparisons demonstrate the specialized operators improve GA performance over standard operators. Future work could better utilize domain knowledge and handle changes in dynamic environments.
A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...CSCJournals
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.
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.
Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...Waqas Tariq
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.
This document discusses navigation techniques for robots. It begins by reviewing localization methods like Markov localization and Kalman filters. It then discusses two approaches for navigation - local planning, which involves moving towards a goal while avoiding obstacles, and global planning, which calculates an offline shortest path. Various global planning techniques are described, including graph-based methods like Dijkstra's and A* algorithms, as well as cell decomposition and potential field planning. Potential field planning calculates a virtual force pulling the robot based on distance to obstacles and goal direction. The document concludes by discussing debates around social and technical topics related to robotics.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Exact Cell Decomposition of Arrangements used for Path Planning in RoboticsUmair Amjad
This is short overview of research paper.
We present a practical algorithm for the automatic generation of a map that describes the operation environment of an indoor mobile service robot. The input is a CAD description of a building consisting of line segments that represent the walls. The algorithm is based on the exact cell decomposition obtained when these segments are extended to infinite lines, resulting in a line arrangement. The cells are represented by nodes in a connectivity graph. The map consists of the connectivity graph and additional environmental information that is calculated for each cell. The method takes into account both the path planning and position verification requirements of the robot and has been implemented.
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.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
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.
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
Pedestrian behavior/intention modeling for autonomous driving IIYu Huang
The document discusses several papers related to modeling pedestrian behavior and predicting pedestrian trajectories for autonomous vehicles. It begins with an outline listing the paper titles and authors. It then provides more detailed summaries of three papers:
1) "Social LSTM: Human Trajectory Prediction in Crowded Spaces" which uses an LSTM model and social pooling layer to jointly predict paths of all people in a scene by taking into account social conventions.
2) "A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments" which uses an LSTM model incorporating static obstacles and surrounding pedestrians to forecast trajectories.
3) "Social GAN: Socially Acceptable Trajectories with Generative
Camera-Based Road Lane Detection by Deep Learning IIYu Huang
lane detection, deep learning, autonomous driving, CNN, RNN, LSTM, GRU, lane localization, lane fitting, ego lane, end-to-end, vanishing point, segmentation, FCN, regression, classification
This document describes visual servo control of a mobile robot using homography-based image feedback. A camera is mounted on the robot to track features in the environment. Homography is estimated from matched features to relate the current and target camera views. The robot's motion model and a control law are derived to drive the robot from its initial position to the target position defined by the target image. Experimental results are presented to validate the visual servo control approach. The high-level goal is to navigate the robot to the target position using only image feedback from the mounted camera.
Simulation for autonomous driving at uber atgYu Huang
Testing Safety of SDVs by Simulating Perception and Prediction
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Recovering and Simulating Pedestrians in the Wild
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
SceneGen: Learning to Generate Realistic Traffic Scenes
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Appendix: (Waymo)
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
GENERATION AND DEPARTABILITY OF GVG FOR CAR-LIKE ROBOTcscpconf
This paper presents an algorithm, based on conventional GVG that enables a car-like robot find a collision free path from depart configuration to some goal position in an environment
containing some convex obstacles. Prior research on GVG prescribed path for a circular robot. The circular robot is holonomic system, but this time GVG is used in nonholonomic system. The
proposed algorithm enables the car-like robot depart the GVG to the goal position with the nonholonomic path.
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.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
License plate recognition system is one of the core technologies in intelligent traffic control. In this paper, a new and tunable algorithm which can detect multiple license plates in high resolution applications is proposed. The algorithm aims at investigation into and identification of the novel Iranian and some European countries plate, characterized by both inclusion of blue area on it and its geometric shape. Obviously, the suggested algorithm contains suitable velocity due to not making use of heavy pre-processing operation such as image-improving filters, edge-detection operation and omission of noise at the beginning stages. So, the recommended method of ours is compatible with model-adaptation, i.e., the very blue section of the plate so that the present method indicated the fact that if several plates are included in the image, the method can successfully manage to detect it. We evaluated our method on the two Persian single vehicle license plate data set that we obtained 99.33, 99% correct recognition rate respectively. Further we tested our algorithm on the Persian multiple vehicle license plate data set and we achieved 98% accuracy rate. Also we obtained approximately 99% accuracy in character recognition stage.
Localization and navigation are important tasks for mobile robots. Localization involves determining a robot's position and orientation, which can be done using global positioning systems outdoors or local sensor networks indoors. Navigation involves planning a path to reach a goal destination. Common navigation algorithms include Dijkstra's algorithm, A* algorithm, potential field method, wandering standpoint algorithm, and DistBug algorithm. Each algorithm has different requirements and approaches to planning paths between a starting point and goal.
Robot path planning, navigation and localization.pptxshohel rana
Robot path planning involves generating a collision-free path for a robot to follow between an initial and goal configuration. Key aspects of path planning include:
1) Representing the robot and obstacles in a configuration space (C-space) that accounts for all possible robot states. This allows the path planning problem to be generalized across different robots.
2) Using roadmap approaches like visibility graphs or Voronoi diagrams to reduce the high-dimensional C-space to a graph that can be searched to find paths.
3) Cell decomposition methods like trapezoidal decomposition or quadtree decomposition that partition the C-space into simple cells and connect adjacent free cells to form a connectivity graph.
4) Prob
PRM-RL: Long-range Robotics Navigation Tasks by Combining Reinforcement Learn...Dongmin Lee
I reviewed the PRM-RL paper.
PRM-RL (Probabilistic Roadmap-Reinforcement Learning) is a hierarchical method that combines sampling-based path planning with RL. It uses feature-based and deep neural net policies (DDPG) in continuous state and action spaces. In experiment, authors evaluate PRM- RL, both in simulation and on-robot, on two navigation tasks: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments.
Outline
- Abstract
- Introduction
- Reinforcement Learning
- Methods
- Results
Thank you.
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.
Exact Cell Decomposition of Arrangements used for Path Planning in RoboticsUmair Amjad
This is short overview of research paper.
We present a practical algorithm for the automatic generation of a map that describes the operation environment of an indoor mobile service robot. The input is a CAD description of a building consisting of line segments that represent the walls. The algorithm is based on the exact cell decomposition obtained when these segments are extended to infinite lines, resulting in a line arrangement. The cells are represented by nodes in a connectivity graph. The map consists of the connectivity graph and additional environmental information that is calculated for each cell. The method takes into account both the path planning and position verification requirements of the robot and has been implemented.
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.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
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.
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
Pedestrian behavior/intention modeling for autonomous driving IIYu Huang
The document discusses several papers related to modeling pedestrian behavior and predicting pedestrian trajectories for autonomous vehicles. It begins with an outline listing the paper titles and authors. It then provides more detailed summaries of three papers:
1) "Social LSTM: Human Trajectory Prediction in Crowded Spaces" which uses an LSTM model and social pooling layer to jointly predict paths of all people in a scene by taking into account social conventions.
2) "A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments" which uses an LSTM model incorporating static obstacles and surrounding pedestrians to forecast trajectories.
3) "Social GAN: Socially Acceptable Trajectories with Generative
Camera-Based Road Lane Detection by Deep Learning IIYu Huang
lane detection, deep learning, autonomous driving, CNN, RNN, LSTM, GRU, lane localization, lane fitting, ego lane, end-to-end, vanishing point, segmentation, FCN, regression, classification
This document describes visual servo control of a mobile robot using homography-based image feedback. A camera is mounted on the robot to track features in the environment. Homography is estimated from matched features to relate the current and target camera views. The robot's motion model and a control law are derived to drive the robot from its initial position to the target position defined by the target image. Experimental results are presented to validate the visual servo control approach. The high-level goal is to navigate the robot to the target position using only image feedback from the mounted camera.
Simulation for autonomous driving at uber atgYu Huang
Testing Safety of SDVs by Simulating Perception and Prediction
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Recovering and Simulating Pedestrians in the Wild
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
SceneGen: Learning to Generate Realistic Traffic Scenes
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Appendix: (Waymo)
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
GENERATION AND DEPARTABILITY OF GVG FOR CAR-LIKE ROBOTcscpconf
This paper presents an algorithm, based on conventional GVG that enables a car-like robot find a collision free path from depart configuration to some goal position in an environment
containing some convex obstacles. Prior research on GVG prescribed path for a circular robot. The circular robot is holonomic system, but this time GVG is used in nonholonomic system. The
proposed algorithm enables the car-like robot depart the GVG to the goal position with the nonholonomic path.
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.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
License plate recognition system is one of the core technologies in intelligent traffic control. In this paper, a new and tunable algorithm which can detect multiple license plates in high resolution applications is proposed. The algorithm aims at investigation into and identification of the novel Iranian and some European countries plate, characterized by both inclusion of blue area on it and its geometric shape. Obviously, the suggested algorithm contains suitable velocity due to not making use of heavy pre-processing operation such as image-improving filters, edge-detection operation and omission of noise at the beginning stages. So, the recommended method of ours is compatible with model-adaptation, i.e., the very blue section of the plate so that the present method indicated the fact that if several plates are included in the image, the method can successfully manage to detect it. We evaluated our method on the two Persian single vehicle license plate data set that we obtained 99.33, 99% correct recognition rate respectively. Further we tested our algorithm on the Persian multiple vehicle license plate data set and we achieved 98% accuracy rate. Also we obtained approximately 99% accuracy in character recognition stage.
Localization and navigation are important tasks for mobile robots. Localization involves determining a robot's position and orientation, which can be done using global positioning systems outdoors or local sensor networks indoors. Navigation involves planning a path to reach a goal destination. Common navigation algorithms include Dijkstra's algorithm, A* algorithm, potential field method, wandering standpoint algorithm, and DistBug algorithm. Each algorithm has different requirements and approaches to planning paths between a starting point and goal.
Robot path planning, navigation and localization.pptxshohel rana
Robot path planning involves generating a collision-free path for a robot to follow between an initial and goal configuration. Key aspects of path planning include:
1) Representing the robot and obstacles in a configuration space (C-space) that accounts for all possible robot states. This allows the path planning problem to be generalized across different robots.
2) Using roadmap approaches like visibility graphs or Voronoi diagrams to reduce the high-dimensional C-space to a graph that can be searched to find paths.
3) Cell decomposition methods like trapezoidal decomposition or quadtree decomposition that partition the C-space into simple cells and connect adjacent free cells to form a connectivity graph.
4) Prob
PRM-RL: Long-range Robotics Navigation Tasks by Combining Reinforcement Learn...Dongmin Lee
I reviewed the PRM-RL paper.
PRM-RL (Probabilistic Roadmap-Reinforcement Learning) is a hierarchical method that combines sampling-based path planning with RL. It uses feature-based and deep neural net policies (DDPG) in continuous state and action spaces. In experiment, authors evaluate PRM- RL, both in simulation and on-robot, on two navigation tasks: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments.
Outline
- Abstract
- Introduction
- Reinforcement Learning
- Methods
- Results
Thank you.
This document provides an overview of concepts in artificial intelligence robotics, including definitions of robots, tasks robots can perform, components of robots like effectors and sensors, and approaches to agent architectures, localization, mapping, planning, control and reactive control. Key points discussed include defining robots as programmable manipulators that perform tasks, the nondeterministic and dynamic nature of the real world environment, and methods like probabilistic roadmaps and potential fields for planning robot movements.
The document discusses planning and navigation for robots. It covers competencies for navigation including localization, mapping, and motion planning. It outlines topics like motion planning in state-space and configuration space, global motion planning algorithms, and local collision avoidance methods. It describes representations for maps like topological maps, grid maps, and kd-trees. It also summarizes various path planning algorithms including potential fields, graph searches, sampling-based approaches, and obstacle avoidance strategies.
This document presents a method for mobile robot path planning using artificial neural networks and fuzzy logic. It introduces the problem of planning a collision-free path for a robot from an initial to goal location amidst obstacles. An artificial neural network is trained to choose a path from multiple options, while a fuzzy logic system is used for obstacle avoidance when all paths are blocked. The combination of neural networks and fuzzy logic provides a computationally efficient solution that overcomes limitations of individual approaches. The results demonstrate increased performance over traditional computational geometry methods.
This document discusses techniques for hidden line removal (HLR) in 3D modeling. HLR involves determining which lines or edges of a 3D model are hidden from a given viewpoint and not drawing them. The document introduces various HLR techniques such as the minimax test, containment test, surface test, computing silhouettes, edge intersection, segment comparison, and homogeneity test. It also describes common HLR algorithms like the priority, area-oriented, and overlay algorithms. The priority algorithm assigns depth priorities to surfaces and removes hidden lines based on depth. The area-oriented algorithm identifies silhouette polygons and uses quantitative hiding to determine line visibility. The overlay algorithm approximates curved surfaces with planar grids to enable standard HLR techniques.
Traversing Notes |surveying II | Sudip khadka Sudip khadka
Traverse is a method in the field of surveying to establish control networks. It is also used in geodesy. Traverse networks involve placing survey stations along a line or path of travel, and then using the previously surveyed points as a base for observing the next point
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.
Computational geometry is the study of algorithms for manipulating geometric objects. It deals with problems involving geometric input and digital output. The goal is to provide basic geometric tools for applications in computer graphics, computer vision, robotics, and more. While it primarily focuses on flat 2D objects, discrete computational geometry bridges the gap between continuous phenomena and computer representation by approximating geometry discretely. Key applications of discrete computational geometry include convex hulls, triangulations, and Voronoi diagrams. Common algorithms for finding the convex hull include Jarvis march, Graham's scan, and divide and conquer approaches.
Artificial Neural Network based Mobile Robot NavigationMithun Chowdhury
This document presents a neural network based navigation system for mobile robots. It uses an artificial neural network (ANN) trained with Backpropagation Through Time (BPTT) to plan paths and navigate around obstacles. The input to the ANN is the state of the robot described using polar coordinates relative to the target position and orientation. Obstacles are also included as inputs by dividing the area in front of the robot into regions. The cost function for training is extended with a potential field to repel the robot from obstacles. Simulation results showed the robot could successfully navigate a maze and reach the target while avoiding multiple obstacles.
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 trajectories using factors like travel time, distance and sensor information.
1. The document introduces various types of industrial robots including Cartesian, cylindrical, spherical, and articulated robots. It describes their different configurations and work envelopes.
2. Robot components like manipulators, end effectors, actuators, sensors, and controllers are defined. Reference frames and work envelopes are also explained.
3. Robot programming methods including teach pendants, lead-through programming, and programming languages are outlined. Different control methods like point-to-point and continuous path control are also introduced.
1. The document introduces industrial robots, including their classification, components, reference frames, work volumes, and programming.
2. Robots are re-programmable manipulators that can move parts and tools through variable programmed motion to perform tasks.
3. Common robot configurations include Cartesian, cylindrical, spherical, articulated, and SCARA robots. Reference frames and work volumes depend on the robot's configuration and reach.
In terms of robotic movement capabilities, there are several common robotic configurations: vertically articulated, cartesian, SCARA, cylindrical, polar and delta.
The document provides an introduction to robotics, including classifications of different robot types, common robot components and accessories, different robot configurations and their work envelopes, reference frames used for robot motion, and overview of robot programming methods including teach pendants and programming languages. It also discusses industrial applications of robots in manufacturing.
1. Dynamic Path Planning
FOLLOW THE GAP METHOD [FGM] FOR MOBILE ROBOTS
Presented by: Vikrant Kumar M. Tech. MED CIM 133569
2. Robotics – Control & Intelligence – Path
Planning – Dynamic Path Planning
Robotics &
Automation
Programming and
Intelligence
Control &
Intelligence
Controller Design Sensors for Robot
Motion Planning
and Control
Path Planning
Static Path
Planning
Dynamic Path
Planning
Mechanical
Design
3. Mobile Robot Navigation
• Global Navigation – from knowledge of goal point
• Local Navigation – from knowledge of near by objects
in path
• Personal Navigation – continuous updating of current
position
Robot’s ability to safely move towards the Goal using its knowledge and
sensorial information of the surrounding environment.
Three terms important in navigation are:
4. Static Path Planning
• Probabilistic Roadmap (PRM) - Two phase navigation:
• Learning phase
• Query phase
• Visibility Graph – navigating at the boundary of obstacles,
turning at corners only, finding shortest straight line path.
Based on a map and goal location, finding a geometric path.
Methods
5. Dynamic Path Planning
• Bug Algorithms
• Artificial Potential Field (APF) Algorithm
• Harmonic Potential Field (HPF) Algorithm
• Virtual Force Field (VFF) method
• Virtual Field Histogram (VFH) method
• Follow the Gap Method (FGM)
Aim is of avoiding unexpected obstacles along the robot’s trajectory to reach the goal.
Methods
6. Some terms of concern
• Point Robot Approach
• Field of view of Robot
• Non-holonomic constraints
7. Point Robot Approach
• Robot and Obstacles are assumed circular.
• Radius of robot is added to radius of obstacles
• The Robot is reduced to a point, while Obstacles are equally enlarged.
8. Field of view
• The sector region within the range of robot’s sensors to get
information of environment.
• Two quantitative measures of field of view:
• End angles of the sector on right and left sides.
• Radius of the sector.
9. Nonholonomic Constraints
• If the vector space of the possible motion directions of a mechanical
system is restricted
• And the restriction can not be converted into an algebraic relation
between configuration variables.
• Can be visualized as, inability of a car like vehicle to move sideways, it
is bound to follow an arc to reach a lateral co-ordinate.
11. Bug Algorithms
• Common sense approach of moving directly to goal.
• Contour the obstacle when found, until moving straight to goal is
possible again.
• Path chosen – often too long
• Robot prone to move close to obstacles
15. APF contd..
• Main drawback –
• Robot gets trapped in local minima.
• The Method Ignores nonholonomic constraints
16. Harmonic Potential Field (HPF)
• An HPF is generated using a Laplace boundary value problem (BVP).
• HPF approach may be configured to operate in a model-based and/or
sensor-based mode
• It can also be made to accommodate a variety of constraints.
• the robot must know the map of the whole environment .
• contradicts reactiveness and local planning properties of obstacle
avoidance.
17. Virtual Force Field method (VFF)
• 2D Cartesian histogram grid for obstacle representation.
• Each cell has certainty value of confidence, that an obstacle is present there.
• Then APF is applied.
• Problems of APF method still exist in VFF
19. Virtual Field Histogram (VFH)
• Uses a 2D Cartesian histogram grid like in VFF.
• Reduces it to a one dimensional polar histogram around the robot's
momentary location.
• Selects lowest polar obstacle density sector
• steers the robot in that direction
• very much goal oriented since it always selects the sector which is in the
same direction as the goal.
• selected sector can be the wrong one in some cases.
• does not consider nonholonomic constraints of robots
21. Follow the Gap Method (FGM)
• Point Robot Approach
• Obstacle representation
• Construction a gap array among obstacles.
• Determination of maximum gap, considering the Goal point location.
• Calculation of angle to Center of Maximum gap
• Robot proceeds to center of maximum gap.
22. Problem Definition
• The Algorithm
• Should find a purely reactive heading to achieve goal co-ordinates
• Should avoiding obstacles with as large distance as possible
• Should consider measurement and nonholonomic constraints
• for obstacle avoidance must collaborate with global planner
• Goal point – obtained from the global planner
• Obstacle co-ordinates - change with time
23. Point Robot Approach
Xrob = Abscissa of robot point
Yrob = Ordinate of robot point
Rrob = Robot circle’s radius
Xobsn = Abscissa of nth obstacle
Yobsn = Ordinate of nth obstacle
Robsn = nth obstacle’s circle’s radius
24. Distance to Obstacle
Distance of nth obstacle from robot
d = ((Xobsn – Xrob)2 + (Yobsn – Yrob)2)1/2
Using Pythogoras theorem
dn2 + (Robsn + Rrob)2 = d2
Or, dn = ((Xobsn – Xrob)2 + (Yobsn – Yrob)2 – (Robsn + Rrob)2)1/2
25. Obstacle Representation
• Two parameter representation
• Φ obs_l_1 – Border left angle of obstacle 1
• Φ obs_r_1 -- Border right angle of obstacle 1
• Φ obs_l_1 – Border left angle of obstacle 2
• Φ obs_l_1 – Border right angle of obstacle 2
Φobs_l_1
Φobs_r_1
Φobs_l_2
Φobs_r_2
Obst.
1
Obst.
2
26. Gap Border Evaluation
If, 𝑑𝑛ℎ𝑜𝑙 < 𝑑𝑓𝑜𝑣 => 𝛷𝑙𝑖𝑚 = 𝛷𝑛ℎ𝑜𝑙
Else if, 𝑑𝑛ℎ𝑜𝑙 ≥ 𝑑𝑓𝑜𝑣 => 𝛷𝑙𝑖𝑚 = 𝛷𝑓𝑜𝑣
In order to understand which boundary is active for a
boundary obstacle, decision rule are illustrated as
follows:
27. Gap boarder parameters
• 1. Φlim: Gap border angle
• 2. Φnhol: Border angle coming from nonholonomic constraint
• 3. Φfov: Border angle coming from field of view
• 4. dnhol: Nearest distance between nonholonomic constraint arc and
obstacle border
• 5. dfov: Nearest distance between field of view line and obstacle border
29. Construction of gap array
Robot
Goal
Gap 4
Gap 2
Gap 3
Field of View
Gap 1
Gap 5
N + 1 gaps for N obstacles
30. Gap array and Maximum Gap
• Gap[N+1] = [(Φlim_l – Φobs1_l)(Φobs1_r – Φobs2_l)……(Φobs(n-
1)_r –Φobs(n-1)_l)(Φobsn_r – Φlim_r)]
• Maximum gap is determined with a sorting algorithm in program.
33. Gap center angle
• The gap center angle (φgap_c ) is found in terms of the measurable d1,
d2, φ1, φ2 parameters
34. Calculation of final heading angle
• Final angle is Combination of angle of center of maximum gap and
Goal point angle.
• Determined by fusing weighted average function of gap center angle
and goal angle.
• α is the weight to obstacle gap.
• α acts as tuning parameter for FGM.
• ß weight to goal point (assumed 1 for simplicity)
• dmin is minimum distance to the approaching obstacle.
36. Role of α value
• Weightage to gap angle is α/dmin
• α makes the path goal oriented or gap oriented.
• For α= 0, φfinal is equal to φgoal
• Increasing values of alpha brings φfinal closer to φgap_c and vice
versa
39. FGM and APF on local minima
• FGM the robot can reach goal point while avoiding obstacles
• In APF method, robot gets stuck because of the local minimum where
all vectors from the obstacles and goal point zero each other
• FGM selects the first calculated gap value if there are equal maximum
gaps.
• This provides FGM to move if at least one gap exists.
41. Comparison of Safety and Travel length
• From table below, FGM is 23% safer than the FGM-basic and 40%
safer than the APF in terms of the norm of the defined metric while
the total distance traveled values are almost the same
42. Dead end Scenario
• A dead-end scenario of U-shaped obstacles is a problem for FGM as it
is for APF as both are more sort of local planners.
• It needs upper level of intelligence.
• Can be solved by approaches like Virtual Obstacle Method, Multiple
Goal Point method etc.
43. Advantages of FGM
• Single tuning parameter (α) in weightage to gap center angle
(α/dmin)
• No local minima problem like earlier algorithms
• Considers nonholonomic constraints for the robot.
• Only feasible trajectories are generated, lesser ambiguity to decision,
lesser computation time.
• Field of view of robot is taken into account.
• Robot does not move in unmeasured directions.
• Passage through maximum gap center – Safest path.
44. Limitation of FGM
Remedy
• Unable to come out of dead-end-scenario
• Hybridizing FGM with local planner techniques like virtual
obstacles, virtual goal point method etc.
45. Conclusion
• Dynamic path planning literature and algorithms were explained.
• Follow the Gap Method(FGM) was explained in detail.
• Major Contribution from FGM:
• Single tuning parameter
• No local minima problem
• Consideration to field of view and nonholonomic constraints.
• Consideration to safety in trajectory planning.