Lecture 20 Motion Planning II - Publish.illinois.edu
Lecture 20Motion Planning IIKatie DCModern Robotics Ch 10.2-10.5
Administrivia Upcoming homework due dates: HW6 due 11/14 at 8pm HW7 due 11/19 at 8pm HW8 (bonus) due 12/10 at 8pm Note that it will likely be a good review for the exam! Last few lectures: 11/16 will be a guest lecture – attendance is required! 11/18 will be a review session 11/30 and 12/2 will be project presentations Exam 2 is on 12/7 during lecture
all deadlines and submission instructions are on the website
Who is NancyAmato? Head of the CS department and expert in motion planning Her paper on probabilistic planning is one of the most importantpapers in PRM, the first to not use uniform sampling in theconfiguration space She and her team wrote a seminal paper that shows how robotplanning can be applied to protein motions (folding)à This line of work started a new research area in comp. biology
Motion Planning Overview
Graphs and Trees Motion planners often represent C-space as a graph A graph is a collection of nodes 𝒩 and edges ℰ, where edge 𝑒 connects two nodes A tree is a directed graph with no cycles and each node has at least one parent
Graphs and Trees
Grid-World Example
Graph Search MethodsA* search algorithm.Dijkstra’s algorithm.Credit: Subh83 on Wikipedia
A simple roadmap: visibility graph
A simple roadmap: visibility graph
Sampling Based Planners: Probabilistic Roadmaps
Reachability Tree for Dubin’s CarCredit: Steven LaValle, Planning Algorithms
Rapidly Exploring Random Trees (RRT)
Rapidly Exploring Random Trees (RRT)
RRT: Lunar LanderCheck out Steven Lavalle’s RRT Gallery: http://msl.cs.uiuc.edu/rrt/gallery.html
Summary Given an initial state and a desired final state, motion planning providesus with tools to find a time horizon and a sequence of actions to find atrajectory that reaches the goal without collisions A roadmap path planner uses a graph representation of free space,which can then provide a trajectory using search algorithms The basic RRT algorithm is a sampling-based method that grows a singlesearch tree from start to find a motion to goal Uses a local planner to find a motion from the nearest node to the sampled node
A few things that might be useful to know What are some key properties of planners? Think about what applications some properties or types of plannersmight be needed. If given a very simple graph, can you find the shortest path? Be somewhat familiar with the pros and cons of the planners we justdiscussed.
Course Recapsensethinkact1.2.3.4.5.6.7.8.9.Linear algebra and diff. eq. reviewDoF, configuration spaceRigid body motion & transformationsScrew theoryForward KinematicsVelocity KinematicsInverse KinematicsDynamicsMotion Planning
SensorsPerceptionDecision-MakingSimulation & ValidationTrajectory PlanningLow-level ControlEnvironment& Agent ModelsCompute Platform
If you liked Try this!Everything!ABE 424 Principles of Mobile RoboticsECE 484 Principles of Safe AutonomyLinear AlgebraMATH 415 Applied Linear AlgebraECE 515 / ME 540 Control System Theory and DesignSensing and State EstimationECE 310 / 417 Signal ProcessingECE 437 Sensors and InstrumentationABE 424 Principles of Mobile RoboticsRobot KinematicsECE 489 / ME 446 / SE 422 Robot Dynamics and ControlCS 498 Robot Manipulation and PlanningRigid Body MotionSE 598 Soft RoboticsECE 549 Computer VisionControlECE 486 Control Systems (or equivalent in your department)ECE 515 / ME 540 Control System Theory and DesignDecision-MakingECE 448 Introduction to AICS 446 Machine LearningPlanningCS 498 Robot Manipulation and PlanningLabsSE 423 Introduction to MechatronicsGraduate-Level Topics CoursesECE 598SG Learning-Based RoboticsECE 598HCR Human-Robot InteractionECE 598JK Humanoid RoboticsCS 598 Advanced Computational Robotics
If you liked Try this!Everything!ABE 424 Principles of Mobile RoboticsECE 484 Principles of Safe AutonomyLinear AlgebraMATH 415 Applied Linear AlgebraECE 515 / ME 540 Control System Theory and DesignSensing and State EstimationECE 310 / 417 Signal ProcessingECE 437 Sensors and InstrumentationABE 424 Principles of Mobile RoboticsRobot KinematicsECE 489 / ME 446 / SE 422 Robot Dynamics and ControlCS 498 Robot Manipulation and PlanningRigid Body MotionSE 598 Soft RoboticsECE 549 Computer VisionControlECE 486 Control Systems (or equivalent in your department)ECE 515 / ME 540 Control System Theory and DesignDecision-MakingECE 448 Introduction to AICS 446 Machine LearningPlanningCS 498 Robot Manipulation and PlanningLabsSE 423 Introduction to MechatronicsGraduate-Level Topics CoursesECE 598SG Learning-Based RoboticsECE 598HCR Human-Robot InteractionECE 598JK Humanoid RoboticsCS 598 Advanced Computational Robotics
Linear Algebra /DifferentialEquationsECE486(or equivalent)Control SystemsECE313(or equivalent)Intro to ProbabilityABE424Principles ofMobile RoboticsECE484Principles ofSafe AutonomyCS598AdvancedComp. RoboticsABE 524Autonomous Decisionmaking (Field Robotics)CS588Autonomous VehicleSystems EngineeringECE550Robot PlanningECE598Human-RobotInteractionIntroductionto RoboticsRobot Dynamics& ControlLegendManipulatorsGeneral RoboticsMobile Robotics AutonomyRobotSuggested PrereqsSE423Introductionto E598Humanoid RoboticsFor full list of recommendedand related courses, check outrobotics.Illinois.edu/educationCS 498Robot Manipulation& PlanningSE598Soft RoboticsECE598Learning-BasedRoboticsFamiliarity withML/AI/MDPsex: ECE448,CS446,ECE586
robotics.Illinois.edu/education ECE470/ME445/AE482 Introduction to Robotics ECE489/ME446/SE422 Robot Dynamics & Control ECE550 Robot Planning ABE424 Principles of Mobile Robotics CS 498 Robot Manipulation & Planning SE598 Soft Robotics ECE598 Humanoid Robotics ECE484 Principles of Safe Autonomy ECE313 (or equivalent) Intro to
Introduction of Chemical Reaction Engineering Introduction about Chemical Engineering 0:31:15 0:31:09. Lecture 14 Lecture 15 Lecture 16 Lecture 17 Lecture 18 Lecture 19 Lecture 20 Lecture 21 Lecture 22 Lecture 23 Lecture 24 Lecture 25 Lecture 26 Lecture 27 Lecture 28 Lecture
Lecture 1: A Beginner's Guide Lecture 2: Introduction to Programming Lecture 3: Introduction to C, structure of C programming Lecture 4: Elements of C Lecture 5: Variables, Statements, Expressions Lecture 6: Input-Output in C Lecture 7: Formatted Input-Output Lecture 8: Operators Lecture 9: Operators continued
Lecture 1: Introduction and Orientation. Lecture 2: Overview of Electronic Materials . Lecture 3: Free electron Fermi gas . Lecture 4: Energy bands . Lecture 5: Carrier Concentration in Semiconductors . Lecture 6: Shallow dopants and Deep -level traps . Lecture 7: Silicon Materials . Lecture 8: Oxidation. Lecture
TOEFL Listening Lecture 35 184 TOEFL Listening Lecture 36 189 TOEFL Listening Lecture 37 194 TOEFL Listening Lecture 38 199 TOEFL Listening Lecture 39 204 TOEFL Listening Lecture 40 209 TOEFL Listening Lecture 41 214 TOEFL Listening Lecture 42 219 TOEFL Listening Lecture 43 225 COPYRIGHT 2016
Partial Di erential Equations MSO-203-B T. Muthukumar tmk@iitk.ac.in November 14, 2019 T. Muthukumar tmk@iitk.ac.in Partial Di erential EquationsMSO-203-B November 14, 2019 1/193 1 First Week Lecture One Lecture Two Lecture Three Lecture Four 2 Second Week Lecture Five Lecture Six 3 Third Week Lecture Seven Lecture Eight 4 Fourth Week Lecture .
Lesson 14: Simple harmonic motion, Waves (Sections 10.6-11.9) Lesson 14, page 1 Circular Motion and Simple Harmonic Motion The projection of uniform circular motion along any axis (the x-axis here) is the same as simple harmonic motion. We use our understanding of uniform circular motion to arrive at the equations of simple harmonic motion.
Simple Harmonic Motion The motion of a vibrating mass-spring system is an example of simple harmonic motion. Simple harmonic motion describes any periodic motion that is the result of a restoring force that is proportional to displacement. Because simple harmonic motion involves a restoring force, every simple harmonic motion is a back-
Motion Capture, Motion Edition - lionel.reveret@inria.fr 38 Motion capture, Motion edition References – "Motion Warping,“, Zoran Popovic, Andy Witkin in Com puter Graphics (SIGGRAPH) 1995. – Michael Gleicher. “Retargetting Motion to New Chara cters”, Proceedings of SIGGRAPH 98. In Computer Graphics Annual Conferance Series. 1998.
