# EE-562

(Difference between revisions)
 Revision as of 09:32, 3 April 2014 (view source) (→Schedule (2014))← Previous diff Revision as of 09:44, 3 April 2014 (view source) (→Schedule (2014))Next diff → Line 193: Line 193: [[Media:EE562-Hw5.pdf|Homework 5]] [[Media:EE562-Hw5.pdf|Homework 5]] - | align ="left" | Choset Chapter 4; + | align ="left" | Choset Chapter 4, 6; [http://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf Textbook Slides - Potentials] [http://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf Textbook Slides - Potentials] [http://www.cs.cmu.edu/~./motionplanning/lecture/Chap6-CellDecomp_howie.pdf Textbook Slides - Cell decomposition] [http://www.cs.cmu.edu/~./motionplanning/lecture/Chap6-CellDecomp_howie.pdf Textbook Slides - Cell decomposition] + |- + | align ="left" | Week 10. March 24 + | align ="left" | '''Midterm.''' [[Media:EE562-Midterm.pdf|Exam Problems]] + + '''Lec 18'''. Introduction to roadmaps in configuration spaces; visibility maps; visibility graph construction; convexity revisited; Voronoi partitions; Generalized voronoi diagrams; GVD roadmaps; + + | align ="left" | Choset Chapter 5; + + [http://www.cs.cmu.edu/~./motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf Textbook Slides - Roadmaps] |- |- | align ="left" | Week 11. March 31 | align ="left" | Week 11. March 31 - | align ="left" | '''Lec 18'''. Sampling based planners; Probabilistic roadmaps; PRM algorithm introduction; construction phase; Algorithmic and analytical challenges in roadmap construction using sampling; Distances in non-Euclidean CSpaces; Formal introduction to Metric spaces; + | align ="left" | '''Lec 19'''. Deterministic Vs. Probabilistic methods of roadmap construction; Sampling based planners; Probabilistic roadmaps; PRM algorithm introduction; construction phase; Algorithmic and analytical challenges in roadmap construction using sampling; Distances in non-Euclidean CSpaces; Formal introduction to Metric spaces; - '''Lec 19.''' Configuration spaces as metric spaces (contd.); distance metrics on S^1, SO(2), SE(2), T^n; composition of metrics on product spaces; Quaternion algebra; topology of SO(3) as RP^3; distance metrics on SO(3), SE(3) using quaternions; generating random elements of R^n, S^1, SO(2), SE(3); Lebesgue and Haar measures; A reciep to generate random rigid body orientations using quaternion algebra; + '''Lec 20.''' Configuration spaces as metric spaces (contd.); distance metrics on S^1, SO(2), SE(2), T^n; composition of metrics on product spaces; Quaternion algebra; topology of SO(3) as RP^3; distance metrics on SO(3), SE(3) using quaternions; generating random elements of R^n, S^1, SO(2), SE(3); Lebesgue and Haar measures; A reciep to generate random rigid body orientations using quaternion algebra; | align ="left" | Choset Chapter 7; | align ="left" | Choset Chapter 7;

## Revision as of 09:44, 3 April 2014

EE-562/CS-5610: Robot Motion Planning

## Instructor

Dr Abubakr Muhammad, Assistant Professor of Electrical Engineering

Email: abubakr [at] lums.edu.pk

Office: Room 9-351A, Right Wing, 3rd Floor, SSE Bldg

Office Hours:

Teaching assistant.

## Course Details

Year: 2013-14

Semester: Spring

Credits: 3

Elective course for electrical engineering, computer engineering and computer science majors

Course Website: http://cyphynets.lums.edu.pk/index.php/EE-562

### Course Description

Motion planning is the study of models and algorithms that reason about the movement of physical bodies such as humans, robots, and animals. This course focuses on motion planning for industrial manipulators and autonomous mobile robots such as unmanned aerial and ground vehicles. Topics include representations of state and movement, potential functions, roadmaps, cell decomposition, sampling based probabilistic planners, robot dynamics, sensor limitations and environmental mapping. Students will implement motion planning algorithms in open-source robotics frameworks such as ROS and OMPL, read recent literature in the field and complete a project that draws on the course material. The course bridges the gap between low-level regulatory control and higher-level AI in robots.

### Objectives

• Introduce fundamental principles in robot motion planning.
• Use of geometric and dynamical models acquired from sensory data.
• Using sensor-based information to determine robot’s own state and of the world
• Setting up and running planning algorithms on robotic platforms
• Control theoretic issues in trajectory planning and sensory feedback.

• Mechatronics or robot building.
• Higher-level perception and AI.

### Learning Outcomes

Students will be able to:

• To model and simulate robotic mechanisms and vehicles.
• To analyze limitations of motion and sensing in motion planning.
• To integrate control, planning and reasoning problems in autonomous systems.
• To implement motion planning methods in complex uncertain environment.

### Pre-requisites

Courses. EE-361. Feedback control systems OR CS-310. Algorithms OR By permission of instructor.

Topics/Skills. Programming proficiency in C or MATLAB; multi-variable calculus, linear algebra, probability

### Text book

The course will be taught from a combination of the following textbooks.

Primary Texts

Secondary Texts

• Home Works ( 5 x 4% ) : 20%
• Midterm Examination: 25%
• Final Examination: 30 %
• Group Project: 25 %
• Proposal. 3%
• Code / demo. 8%
• Presentation. 4%
• Paper. 7%
• Team work balance. 3%

### Course Delivery Method

Lectures. Mon, Wed: 9:30am-10:45am.

## Schedule (2014)

Week 1. Jan 13 Lec 1. Introduction; what is a robot?; course objectives;

Lec 2. workspaces; configuration spaces; planning algorithms; bug algorithms; Bug0 and Bug1 algorithms;

Choset Chapter 1,2;
Week 2. Jan 20 Lec 3. Completeness of Bug1; upper bounds on Bug1; Bug2 algorithm; performance comparison; range sensors and mathematical description.

Lec 4. Tangent Bug algorithm; implementation issues; wall-following behavior with range sensors;

Textbook Slides - Bug Algos;

Choset CH 2;

Week 3. Jan 27 Lec 5. The idea of a configuration spaces; Config. Spaces of mechanical linkages and mobile robots; Obstacles; A bug algorithm for a 2-link robotic arm;

Lec 6. Mappings between configuration spaces and workspaces; geometric primitives; star algorithms for polygonal robots and obstacles;

Textbook Slides - CSpaces;

Choset Chapter 3;

Week 4. Feb 3 Lec 7. Rigid body configuration spaces; SO(3); SE(3); parameterization of SO(3); kinematics of rigid bodies; Textbook Slides;

Choset Chapter 3;

Week 5. Feb 10 Lec 8. Rigid bodies (contd.); kinematics Vs. dynamics; constraints (holonomic Vs. non-holonomic); Velocity kinematics;

Lec 9. Velocity kinematics (contd.); Games and puzzles as Planning algorithms; Multi-agent configuration spaces; continuity in configuration spaces;

Textbook Slides - CSpaces;

Choset Chapter 3;

Week 6. Feb 17 Lec 10. Discrete configuration spaces; generalized view of state-spaces; state transition functions on discrete configuration spaces; finite state machines and discrete planning;

Lec 11. Graph searches as planning algorihtms;

Slides on Discrete Planning

LaValle Chapter 2; Choset Appendix H;

Week 7. Feb 24 Lec 12. Use of heuristics in cost functions or rewards; H-star algorithm; Dijkstra's algorithm revisited; a formal analysis of discrete planning; value iteration in forward and backward search; Bellmann's principle of optimality;

Lec 13. Artificial potential functions; primitives for attractive and repulsive potentials;

Choset Appendix H, Chapter 4;
Week 8. March 3 Lec 14. Artificial potential functions (contd.); Bush-fire algorithm; local minima; Wave-front planners; Lifts of forces and torques from workspace to configuration spaces;

Lec 15. Artificial potentials in non-Euclidean spaces; navigation functions on sphere-worlds;

Choset Chapter 4;
Week 9. 17 March Lec 16. Navigation functions (contd.); navigation functions on star worlds; convexity and star-property; diffeomorphisms between star worlds and sphere worlds; invariance of navigation function properties under morphisms; topology and obstacles;

Lec 17. Cell decomposition; Trapezoidal cell decompositions; Morse decomposition; Reeb graphs; CSpaces for dynamic obstacles; practical issues; coverage problems in robotics;

Choset Chapter 4, 6;
Week 10. March 24 Midterm. Exam Problems

Lec 18. Introduction to roadmaps in configuration spaces; visibility maps; visibility graph construction; convexity revisited; Voronoi partitions; Generalized voronoi diagrams; GVD roadmaps;

Choset Chapter 5;
Week 11. March 31 Lec 19. Deterministic Vs. Probabilistic methods of roadmap construction; Sampling based planners; Probabilistic roadmaps; PRM algorithm introduction; construction phase; Algorithmic and analytical challenges in roadmap construction using sampling; Distances in non-Euclidean CSpaces; Formal introduction to Metric spaces;

Lec 20. Configuration spaces as metric spaces (contd.); distance metrics on S^1, SO(2), SE(2), T^n; composition of metrics on product spaces; Quaternion algebra; topology of SO(3) as RP^3; distance metrics on SO(3), SE(3) using quaternions; generating random elements of R^n, S^1, SO(2), SE(3); Lebesgue and Haar measures; A reciep to generate random rigid body orientations using quaternion algebra;

Choset Chapter 7;