Lecture 08: Localization and Mapping II
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Lecture 10: Summary
This document provides a summary of the key topics covered in an introduction to robotics course over 15 weeks, including:
- Locomotion and kinematics concepts like dynamic and static stability, forward and inverse kinematics, and differential drive constraints.
- Sensors like laser range scanners, their properties and how to model uncertainty. Visual sensors, odometry, and localization using Bayes' rule and Kalman filters.
- Midterm and final exam preparation advice focusing on understanding core concepts rather than derivations, and practicing examples like kinematics and localization problems.
- The final exam is scheduled for Wednesday, December 15 from 7:30-10:00 pm in the CAETE classroom.
Lecture 07: Localization and Mapping I
The document discusses a midterm exam for a robotics localization and mapping course that will be harder than the final exam but require less time. It provides an overview of the last two weeks' exercises including tasks related to vision, mapping, navigation, and planning. The document then summarizes the previous lecture on the Gaussian distribution and error propagation before covering today's topics of sensors for localization, error propagation for localization, position representation, and planning. Localization, differential drive robot odometry, belief representation, environment representation, and reactive versus deliberative planning are all discussed. Students will work in groups on an exercise involving navigation algorithms.
Lecture 05
This document discusses various computer vision techniques for obtaining range and depth information from images, including depth from focus, depth from stereo, and optical filters. It introduces the pinhole camera model and explains how depth can be estimated from a single image by analyzing focus versus from multiple images using stereo vision. Examples of image processing techniques like convolution and morphological operations are also provided. The challenges of object recognition are discussed, noting that perception depends on assumptions about lighting, geometry, frequency, color and other factors that can be ambiguous.
Lecture 02: Locomotion
This document provides an introduction to robot locomotion. It defines locomotion as moving from place to place and discusses various forms of locomotion including crawling, sliding, running, jumping, walking and rolling. It also covers locomotion in nature like swimming, gliding and flying. Key relationships between different forms of locomotion are examined. The document discusses walking versus rolling locomotion and how to characterize different locomotion schemes. Designing robots that can crawl, slide, gallop, jump, walk and roll is proposed as an exercise.
Lecture 04
This document discusses robot sensors and perception. It begins with a review of robot kinematics and control concepts like forward and inverse kinematics, feedback control, and proportional control. It then covers specific sensors like laser rangefinders, infrared sensors, and cameras. Examples of sensors on robots like Roomba, PrairieDog, Nao, and PR2 are provided. The performance aspects of sensors like range, resolution, linearity, and accuracy are defined. Case studies of laser rangefinders and infrared distance sensors are described in detail relating their physics and working principles to their performance capabilities and limitations. An exercise is proposed to design robots for vacuuming, lawn mowing, and ball collecting giving consideration to necessary sensors, algorithms, and mechanisms
Lecture 09: SLAM
1) Probabilistic state estimation techniques like the Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filters allow estimating the state of a system using probabilistic models and sensor measurements with uncertainty.
2) The Kalman filter provides a recursive solution to the linear filtering problem by estimating the current state as a weighted average of the prior state and new measurements, with the weights based on their uncertainties.
3) Extensions to the Kalman filter like the extended and unscented Kalman filters allow handling nonlinear systems by linearizing around the current estimate, while particle filters represent the state distribution with random samples and are more flexible for nonlinear and non-Gaussian problems like SLAM.
Lecture 03 - Kinematics and Control
This document provides an introduction to robotics kinematics including forward kinematics, inverse kinematics, and maneuverability. It discusses calculating a robot's speed given its wheel speeds and geometry using forward kinematics, and determining required wheel speeds to achieve a desired speed using inverse kinematics. It also covers a robot's degree of mobility based on the number of independently controllable wheels and degree of steerability based on the number of independently steered wheels to determine its overall maneuverability. Examples are provided and the document concludes with assigning homework to read about forward kinematics and program a waypoint following robot.
Lecture 06: Features and Uncertainty
1. The document discusses various topics related to robotics including vision, midterm exams, locomotion, control, mapping, and homework.
2. Key concepts that are reviewed include line detection, calculating uncertainty from sensor measurements, propagating uncertainty in features, and different algorithms for mapping like watershed segmentation.
3. Exercises are assigned related to writing controllers for different robots, dealing with obstacles, and improving control parameters. Students are divided into groups to work on a final project to implement a controller.
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Lecture 10: Summary
This document provides a summary of the key topics covered in an introduction to robotics course over 15 weeks, including:
- Locomotion and kinematics concepts like dynamic and static stability, forward and inverse kinematics, and differential drive constraints.
- Sensors like laser range scanners, their properties and how to model uncertainty. Visual sensors, odometry, and localization using Bayes' rule and Kalman filters.
- Midterm and final exam preparation advice focusing on understanding core concepts rather than derivations, and practicing examples like kinematics and localization problems.
- The final exam is scheduled for Wednesday, December 15 from 7:30-10:00 pm in the CAETE classroom.
Lecture 07: Localization and Mapping I
The document discusses a midterm exam for a robotics localization and mapping course that will be harder than the final exam but require less time. It provides an overview of the last two weeks' exercises including tasks related to vision, mapping, navigation, and planning. The document then summarizes the previous lecture on the Gaussian distribution and error propagation before covering today's topics of sensors for localization, error propagation for localization, position representation, and planning. Localization, differential drive robot odometry, belief representation, environment representation, and reactive versus deliberative planning are all discussed. Students will work in groups on an exercise involving navigation algorithms.
Lecture 05
This document discusses various computer vision techniques for obtaining range and depth information from images, including depth from focus, depth from stereo, and optical filters. It introduces the pinhole camera model and explains how depth can be estimated from a single image by analyzing focus versus from multiple images using stereo vision. Examples of image processing techniques like convolution and morphological operations are also provided. The challenges of object recognition are discussed, noting that perception depends on assumptions about lighting, geometry, frequency, color and other factors that can be ambiguous.
Lecture 02: Locomotion
This document provides an introduction to robot locomotion. It defines locomotion as moving from place to place and discusses various forms of locomotion including crawling, sliding, running, jumping, walking and rolling. It also covers locomotion in nature like swimming, gliding and flying. Key relationships between different forms of locomotion are examined. The document discusses walking versus rolling locomotion and how to characterize different locomotion schemes. Designing robots that can crawl, slide, gallop, jump, walk and roll is proposed as an exercise.
Lecture 04
This document discusses robot sensors and perception. It begins with a review of robot kinematics and control concepts like forward and inverse kinematics, feedback control, and proportional control. It then covers specific sensors like laser rangefinders, infrared sensors, and cameras. Examples of sensors on robots like Roomba, PrairieDog, Nao, and PR2 are provided. The performance aspects of sensors like range, resolution, linearity, and accuracy are defined. Case studies of laser rangefinders and infrared distance sensors are described in detail relating their physics and working principles to their performance capabilities and limitations. An exercise is proposed to design robots for vacuuming, lawn mowing, and ball collecting giving consideration to necessary sensors, algorithms, and mechanisms
Lecture 09: SLAM
1) Probabilistic state estimation techniques like the Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filters allow estimating the state of a system using probabilistic models and sensor measurements with uncertainty.
2) The Kalman filter provides a recursive solution to the linear filtering problem by estimating the current state as a weighted average of the prior state and new measurements, with the weights based on their uncertainties.
3) Extensions to the Kalman filter like the extended and unscented Kalman filters allow handling nonlinear systems by linearizing around the current estimate, while particle filters represent the state distribution with random samples and are more flexible for nonlinear and non-Gaussian problems like SLAM.
Lecture 03 - Kinematics and Control
This document provides an introduction to robotics kinematics including forward kinematics, inverse kinematics, and maneuverability. It discusses calculating a robot's speed given its wheel speeds and geometry using forward kinematics, and determining required wheel speeds to achieve a desired speed using inverse kinematics. It also covers a robot's degree of mobility based on the number of independently controllable wheels and degree of steerability based on the number of independently steered wheels to determine its overall maneuverability. Examples are provided and the document concludes with assigning homework to read about forward kinematics and program a waypoint following robot.
Lecture 06: Features and Uncertainty
1. The document discusses various topics related to robotics including vision, midterm exams, locomotion, control, mapping, and homework.
2. Key concepts that are reviewed include line detection, calculating uncertainty from sensor measurements, propagating uncertainty in features, and different algorithms for mapping like watershed segmentation.
3. Exercises are assigned related to writing controllers for different robots, dealing with obstacles, and improving control parameters. Students are divided into groups to work on a final project to implement a controller.
Lecture 01
This document outlines the syllabus for an introduction to robotics course. It will cover topics like locomotion, kinematics, perception, localization, planning and navigation. Students will gain hands-on experience programming robots in simulation and debates. Assessments include weekly reading assignments, a midterm, debate and class participation. The course will not cover components building or software engineering, focusing instead on simulation. Students will design controllers for robots in a competition called Ratslife to practice perception, navigation and planning skills.
Lecture 05: Vision
Vision is a challenging task for robots due to various ambiguities, including: depth discontinuities, surface orientation changes, reflectance changes, illumination changes, and different perspectives. Basic image processing techniques can help with tasks like edge detection, thresholding, morphological operations, and stereo vision calculations, but object recognition still requires combining these techniques with filters tuned to the specific objects. For homework, students should read about these topics and complete a questionnaire.
Lecture 06: Features
This document provides an introduction to robotics features for computer vision. It discusses low-level image features like edges, colors and corners that can be extracted from sensors. Higher level features allow reasoning about the environment like object detection and segmentation. Examples demonstrate visual servoing using image features to grasp objects as well as fruit detection using filtering, segmentation and the Hough transform. The document also covers corner detection methods like Harris corners and scale-invariant feature transform (SIFT). It concludes with discussing using features to implement a robot controller and assigning project groups to work on different aspects.
Lecture 04: Sensors
This document discusses robotics perception and sensors. It provides an overview of forward and inverse kinematics, feedback control concepts, and different types of sensors used in robotics like laser rangefinders, infrared sensors, and their performance characteristics. It also outlines an exercise for designing a robot capable of various tasks like vacuuming, lawn mowing, or ball collection and discusses the necessary sensors, algorithms, and mechanisms to address. Homework assigned is to read a section on perception and complete a questionnaire.
robotlocalization_02
This document discusses robot localization. It introduces localization and its importance for autonomous robots and vehicles. It then describes two localization algorithms: one using a simple binary map and sensed landmarks, and another using a histogram to represent probabilistic location beliefs. It also discusses using rotary encoders combined with a specialized "histogram encoder disk" to localize based on wheel movements. The document concludes with a planned demonstration of a localization system using ultrasonic sensors, switches, and a histogram rotary encoder.
Lecture 01: Introduction
This document provides an introduction and syllabus for a robotics course. It will cover topics like locomotion, perception, planning and navigation. Students will gain hands-on experience through simulation exercises and programming robots. They will also participate in an online robot soccer competition. The course aims to teach the science and challenges of robotics through both theoretical lectures and practical assignments using a robot simulation program called Webots.
Lectures 11+12: Debates
What are the safety risks with autonomous robots? Are autonomous robot a threat to human workers? How can robots achieve intelligent behavior? What mechanisms are needed to reach human agility?
Lecture 02: Locomotion
This document discusses various forms of locomotion including crawling, sliding, running, jumping, walking and rolling. It covers relationships between different types of locomotion like swimming to walking. Forms of locomotion found in nature like a horse's gallop are discussed as well as how robots are starting to imitate natural locomotion. Characteristics of locomotion like stability and contact points are examined. Specific locomotion types like walking, wheeled locomotion, and climbing are described in detail. The document concludes by discussing actuators and encoders used for locomotion and providing homework on further exploring locomotion concepts.
December 7, Projects
The document describes using threshold-based agent models to optimize plant placement in a landscape. It proposes an agent-based algorithm where individual "plants" search the landscape for optimal locations based on their light and water requirements. A genetic algorithm approach is also mentioned. The goal is to maximize overall plant growth by finding placements where each plant meets a 70% threshold of its ideal growth conditions. Future work could include formal analysis and comparisons to determine how well the approach works at finding the optimal plant collection for a given landscape.
Lecture 09: Localization and Mapping III
This document provides an overview of localization and mapping techniques for robotics, including:
- Markov localization and particle filters for estimating robot location as a probability distribution.
- The Kalman filter for optimally fusing uncertain sensor measurements and updating location estimates.
- Simultaneous localization and mapping (SLAM) and the "hen-egg" problem of needing a map to localize and a location to build a map.
- Feature-based SLAM approaches that build maps from distinct environmental features.
- FastSLAM which uses a particle filter to estimate robot location and build maps from sensor measurements.
- Key challenges in SLAM like recognizing previously visited places and handling dynamic environments.
Three-dimensional construction with mobile robots and modular blocks
Three-dimensional construction with mobile robots and modular blocksUniversity of Colorado at Boulder
Three-Dimensional Construction with Mobile Robots and Modular Blocks presents a system for constructing 3D structures using mobile robots and modular blocks. Passive blocks contain self-aligning connectors and communicate locally to maintain a "shape map" for construction. Active robots manipulate blocks and do not communicate. The system uses simple rules for block placement to reliably build any structure meeting criteria of no loops and limited overhangs. Various robot algorithms are evaluated for building structures with the lowest costs.Lecture 07: Localization and Mapping I
This document discusses localization and mapping for robotics. It introduces topics like gyroscopes, odometry, GPS, and landmarks for localization. It discusses uncertainty models using Gaussian distributions and error propagation. Methods for belief representation are presented, including parametric single/multi hypothesis and non-parametric particle filters. Environment representations like continuous, discrete, and topological maps are described. The document provides an example of Google Maps and discusses belief representation in topological maps. It also covers multi-hypothesis belief representation, sensor data to topological maps using exact and Voronoi decompositions, and adaptive cell-size. The document assigns homework on navigation algorithms and reactive vs. deliberative planning.
Lecture 10: Navigation
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.
Lecture 03: Kinematics
The document provides an introduction to robotics kinematics. It discusses forward and inverse kinematics, which involve determining speed and position from wheel speeds and desired speeds respectively. It also covers feedback position control to calculate motion from position errors. Additionally, it defines concepts like mobility, steerability and maneuverability which relate to a robot's degrees of freedom based on wheel constraints and configurations. As homework, students are asked to read a chapter on kinematics and program a waypoint following robot.
November 16, Learning
This document discusses using genetic algorithms and particle swarm optimization for learning in multi-robot systems. These population-based algorithms are well-suited for distributed multi-robot systems as they allow for parallel evaluation of controllers. However, selecting an appropriate fitness function is difficult. Additionally, genetic algorithms and particle swarm optimization are heuristics without guarantees and highly sensitive to parameter and model choices.
Lecture 08: Localization and Mapping II
This document discusses algorithms for localization and mapping in robotics, including Dijkstra's algorithm, A* search, odometry, GPS, landmarks, and gyroscopes. It introduces probabilistic localization using Markov localization on discrete state spaces like grids or topological maps. Bayes' rule is used to update the probability of a robot's location based on sensor readings and motion. Particle filters are presented as a way to reduce complexity by random sampling rather than maintaining a full probability distribution over all locations. Examples show mapping and localization in a grid world using a particle filter approach. The homework assignment is to read about Markov localization and particle filters, and next week will include design reviews from student groups.
Introduction to ROBOTICS
1. The document discusses different types of wheels used in mobile robots including fixed wheels, centered orientable wheels, off-centered orientable wheels, and Swedish wheels.
2. It also covers various locomotion methods for mobile wheeled robots including differential drive, tricycle drive, synchronous drive, and Ackerman steering.
3. Kinematics models are presented for different robot configurations to describe the relationship between the robot's motion and control inputs.
November 30, Projects
The document discusses using a particle filter and physics simulator to localize objects seen by a robot. It proposes generating possible object configurations from particle filter hypotheses and using a physics simulator to reject configurations where objects overlap or are unstable. The goal is to find a world model that is physically valid according to the simulator. Examples show invalid configurations being rejected and the best valid configuration being output.
Template classes and ROS messages
Template functions and classes in C++ allow functions and classes to operate on multiple data types rather than a single data type. The document discusses how template functions define a type parameter to replace the data type, and template classes define member functions and variables as templates. It provides examples of template functions used in ROS for message passing, including advertise and subscribe, which allow publishing and subscribing to messages of different types.
NLP for Robotics
This document discusses processing natural language commands in robotics applications. It outlines goals of implementing sentence recognition from text to pick up objects. It reviews existing tools for text processing, speech processing, and robot actions. It also covers parsing orders by mapping verbs to targets and using parts-of-speech tagging to determine word categories. Further work includes generating robot actions from mapped commands and controlling a robot's movement.
Indoor Localization Systems
The document discusses designing an indoor positioning system as an alternative to the existing costly and power-hungry StarGazer localization system. It surveys different wireless technologies like Wi-Fi, Zigbee, and ultrasonic options before proposing an RSSI-based triangulation solution using TI's low-power CC2431 SoC. Some implementation issues discussed include the non-linear relationship between RSSI and distance, needing additional algorithms and an RSSI map survey, and methods for orientation and multi-level positioning detection.
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Lecture 01
This document outlines the syllabus for an introduction to robotics course. It will cover topics like locomotion, kinematics, perception, localization, planning and navigation. Students will gain hands-on experience programming robots in simulation and debates. Assessments include weekly reading assignments, a midterm, debate and class participation. The course will not cover components building or software engineering, focusing instead on simulation. Students will design controllers for robots in a competition called Ratslife to practice perception, navigation and planning skills.
Lecture 05: Vision
Vision is a challenging task for robots due to various ambiguities, including: depth discontinuities, surface orientation changes, reflectance changes, illumination changes, and different perspectives. Basic image processing techniques can help with tasks like edge detection, thresholding, morphological operations, and stereo vision calculations, but object recognition still requires combining these techniques with filters tuned to the specific objects. For homework, students should read about these topics and complete a questionnaire.
Lecture 06: Features
This document provides an introduction to robotics features for computer vision. It discusses low-level image features like edges, colors and corners that can be extracted from sensors. Higher level features allow reasoning about the environment like object detection and segmentation. Examples demonstrate visual servoing using image features to grasp objects as well as fruit detection using filtering, segmentation and the Hough transform. The document also covers corner detection methods like Harris corners and scale-invariant feature transform (SIFT). It concludes with discussing using features to implement a robot controller and assigning project groups to work on different aspects.
Lecture 04: Sensors
This document discusses robotics perception and sensors. It provides an overview of forward and inverse kinematics, feedback control concepts, and different types of sensors used in robotics like laser rangefinders, infrared sensors, and their performance characteristics. It also outlines an exercise for designing a robot capable of various tasks like vacuuming, lawn mowing, or ball collection and discusses the necessary sensors, algorithms, and mechanisms to address. Homework assigned is to read a section on perception and complete a questionnaire.
robotlocalization_02
This document discusses robot localization. It introduces localization and its importance for autonomous robots and vehicles. It then describes two localization algorithms: one using a simple binary map and sensed landmarks, and another using a histogram to represent probabilistic location beliefs. It also discusses using rotary encoders combined with a specialized "histogram encoder disk" to localize based on wheel movements. The document concludes with a planned demonstration of a localization system using ultrasonic sensors, switches, and a histogram rotary encoder.
Lecture 01: Introduction
This document provides an introduction and syllabus for a robotics course. It will cover topics like locomotion, perception, planning and navigation. Students will gain hands-on experience through simulation exercises and programming robots. They will also participate in an online robot soccer competition. The course aims to teach the science and challenges of robotics through both theoretical lectures and practical assignments using a robot simulation program called Webots.
Lectures 11+12: Debates
What are the safety risks with autonomous robots? Are autonomous robot a threat to human workers? How can robots achieve intelligent behavior? What mechanisms are needed to reach human agility?
Lecture 02: Locomotion
This document discusses various forms of locomotion including crawling, sliding, running, jumping, walking and rolling. It covers relationships between different types of locomotion like swimming to walking. Forms of locomotion found in nature like a horse's gallop are discussed as well as how robots are starting to imitate natural locomotion. Characteristics of locomotion like stability and contact points are examined. Specific locomotion types like walking, wheeled locomotion, and climbing are described in detail. The document concludes by discussing actuators and encoders used for locomotion and providing homework on further exploring locomotion concepts.
December 7, Projects
The document describes using threshold-based agent models to optimize plant placement in a landscape. It proposes an agent-based algorithm where individual "plants" search the landscape for optimal locations based on their light and water requirements. A genetic algorithm approach is also mentioned. The goal is to maximize overall plant growth by finding placements where each plant meets a 70% threshold of its ideal growth conditions. Future work could include formal analysis and comparisons to determine how well the approach works at finding the optimal plant collection for a given landscape.
Lecture 09: Localization and Mapping III
This document provides an overview of localization and mapping techniques for robotics, including:
- Markov localization and particle filters for estimating robot location as a probability distribution.
- The Kalman filter for optimally fusing uncertain sensor measurements and updating location estimates.
- Simultaneous localization and mapping (SLAM) and the "hen-egg" problem of needing a map to localize and a location to build a map.
- Feature-based SLAM approaches that build maps from distinct environmental features.
- FastSLAM which uses a particle filter to estimate robot location and build maps from sensor measurements.
- Key challenges in SLAM like recognizing previously visited places and handling dynamic environments.
Three-dimensional construction with mobile robots and modular blocks
Three-dimensional construction with mobile robots and modular blocksUniversity of Colorado at Boulder
Three-Dimensional Construction with Mobile Robots and Modular Blocks presents a system for constructing 3D structures using mobile robots and modular blocks. Passive blocks contain self-aligning connectors and communicate locally to maintain a "shape map" for construction. Active robots manipulate blocks and do not communicate. The system uses simple rules for block placement to reliably build any structure meeting criteria of no loops and limited overhangs. Various robot algorithms are evaluated for building structures with the lowest costs.Lecture 07: Localization and Mapping I
This document discusses localization and mapping for robotics. It introduces topics like gyroscopes, odometry, GPS, and landmarks for localization. It discusses uncertainty models using Gaussian distributions and error propagation. Methods for belief representation are presented, including parametric single/multi hypothesis and non-parametric particle filters. Environment representations like continuous, discrete, and topological maps are described. The document provides an example of Google Maps and discusses belief representation in topological maps. It also covers multi-hypothesis belief representation, sensor data to topological maps using exact and Voronoi decompositions, and adaptive cell-size. The document assigns homework on navigation algorithms and reactive vs. deliberative planning.
Lecture 10: Navigation
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.
Lecture 03: Kinematics
The document provides an introduction to robotics kinematics. It discusses forward and inverse kinematics, which involve determining speed and position from wheel speeds and desired speeds respectively. It also covers feedback position control to calculate motion from position errors. Additionally, it defines concepts like mobility, steerability and maneuverability which relate to a robot's degrees of freedom based on wheel constraints and configurations. As homework, students are asked to read a chapter on kinematics and program a waypoint following robot.
November 16, Learning
This document discusses using genetic algorithms and particle swarm optimization for learning in multi-robot systems. These population-based algorithms are well-suited for distributed multi-robot systems as they allow for parallel evaluation of controllers. However, selecting an appropriate fitness function is difficult. Additionally, genetic algorithms and particle swarm optimization are heuristics without guarantees and highly sensitive to parameter and model choices.
Lecture 08: Localization and Mapping II
This document discusses algorithms for localization and mapping in robotics, including Dijkstra's algorithm, A* search, odometry, GPS, landmarks, and gyroscopes. It introduces probabilistic localization using Markov localization on discrete state spaces like grids or topological maps. Bayes' rule is used to update the probability of a robot's location based on sensor readings and motion. Particle filters are presented as a way to reduce complexity by random sampling rather than maintaining a full probability distribution over all locations. Examples show mapping and localization in a grid world using a particle filter approach. The homework assignment is to read about Markov localization and particle filters, and next week will include design reviews from student groups.
Introduction to ROBOTICS
1. The document discusses different types of wheels used in mobile robots including fixed wheels, centered orientable wheels, off-centered orientable wheels, and Swedish wheels.
2. It also covers various locomotion methods for mobile wheeled robots including differential drive, tricycle drive, synchronous drive, and Ackerman steering.
3. Kinematics models are presented for different robot configurations to describe the relationship between the robot's motion and control inputs.
November 30, Projects
The document discusses using a particle filter and physics simulator to localize objects seen by a robot. It proposes generating possible object configurations from particle filter hypotheses and using a physics simulator to reject configurations where objects overlap or are unstable. The goal is to find a world model that is physically valid according to the simulator. Examples show invalid configurations being rejected and the best valid configuration being output.
Template classes and ROS messages
Template functions and classes in C++ allow functions and classes to operate on multiple data types rather than a single data type. The document discusses how template functions define a type parameter to replace the data type, and template classes define member functions and variables as templates. It provides examples of template functions used in ROS for message passing, including advertise and subscribe, which allow publishing and subscribing to messages of different types.
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Three-dimensional construction with mobile robots and modular blocks
Three-dimensional construction with mobile robots and modular blocks
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NLP for Robotics
This document discusses processing natural language commands in robotics applications. It outlines goals of implementing sentence recognition from text to pick up objects. It reviews existing tools for text processing, speech processing, and robot actions. It also covers parsing orders by mapping verbs to targets and using parts-of-speech tagging to determine word categories. Further work includes generating robot actions from mapped commands and controlling a robot's movement.
Indoor Localization Systems
The document discusses designing an indoor positioning system as an alternative to the existing costly and power-hungry StarGazer localization system. It surveys different wireless technologies like Wi-Fi, Zigbee, and ultrasonic options before proposing an RSSI-based triangulation solution using TI's low-power CC2431 SoC. Some implementation issues discussed include the non-linear relationship between RSSI and distance, needing additional algorithms and an RSSI map survey, and methods for orientation and multi-level positioning detection.
Vishal Verma: Rapidly Exploring Random Trees
The document discusses rapidly exploring random trees (RRTs), an algorithm for motion planning in robotics. RRTs are used to search high-dimensional spaces and consider constraints like obstacles, differential constraints of motion, and non-holonomic constraints. The RRT algorithm works by growing a search tree randomly throughout the configuration space to explore it efficiently. It is shown to work well for problems with obstacles, differential constraints, and non-holonomic constraints compared to other randomized planning methods. Examples are provided to demonstrate RRT planning for holonomic and non-holonomic systems.
December 4, Project
This presentation discusses using reinforcement learning to teach modular robots locomotion. The key challenges are the high-dimensional state and action spaces, as well as the lack of domain knowledge. The presentation proposes using policy gradient reinforcement learning with finite differences to learn locomotion policies from raw sensor data. It suggests that incorporating domain knowledge through task manifolds and curriculum learning could help address the "curse of dimensionality" and speed up the learning process. The goals are to apply these techniques to learn locomotion, map tasks to policies, and develop a "robot school" curriculum.
December 2, Projects
The document discusses using a mothership-daughtership system to address coverage control problems with multiple robots. It proposes that a hierarchical system with motherships commanding daughterships could more efficiently and scalably cover a designated area compared to teams of only motherships or only daughterships. The document outlines the concept, including how daughterships would have basic hardware and states while motherships perform look-ahead simulation. It then describes several test cases to simulate different mothership-daughtership configurations and coordination approaches. Issues running the full simulation in ROS are also noted for future work.
November 9, Planning and Control of Unmanned Aircraft Systems in Realistic C...
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The document summarizes research on developing planning and control frameworks for communication-aware coordination of unmanned vehicle networks. It describes using an information-theoretic approach to optimize robot motion to maximize information gain over noisy communication links. Experimental results show decentralized algorithms allow vehicles to form optimal communication chains and relay networks by considering communication constraints. Field experiments demonstrate these approaches can improve tracking performance for heterogeneous teams of unmanned aircraft and vehicles operating in realistic communication environments.More from University of Colorado at Boulder (6)
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We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
AppSec PNW: Android and iOS Application Security with MobSF
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
What is an RPA CoE? Session 2 – CoE Roles
In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
What is an RPA CoE? Session 1 – CoE Vision
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
"Choosing proper type of scaling", Olena Syrota
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Day 2 - Intro to UiPath Studio Fundamentals
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
Gursev Pirge, PhD
Senior Data Scientist - JohnSnowLabs
Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Tomaz Bratanic
Graph ML and GenAI Expert - Neo4j
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
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