CMPE633Fall2011
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The course will be taught from a combination of the following textbooks.  The course will be taught from a combination of the following textbooks.  
Revision as of 13:12, 9 August 2011
CMPE633: Topics in Robotics and Control
Fall 2011: Robot Dynamics and Control 

Instructor
Dr Abubakr Muhammad, Assistant Professor of Electrical Engineering
Email: abubakr [at] lums.edu.pk
Office: Room 9309A, 3rd Floor, SSE Bldg
Office Hours:
Teaching assistant.
Course Details
Year: 201112
Semester: Fall
Category: Grad
Credits: 3
Elective course for electrical engineering, computer engineering and computer science majors
Course Website: http://cyphynets.lums.edu.pk/index.php/CMPE633Fall2011
Course Description
Generic
A researchmethods based course to study advanced topics in robotics and control system design with emphasis on field robotics, unmanned aerial and ground vehicles, planning algorithms, autonomous systems, telerobotics, Human Robot Interaction (HRI) and other related areas. The course prepares students to do independent work at the frontiers of robotics and control research.
This year
This course introduces essential concepts and analytical tools to understand the dynamics and associated control systems for common robot tasks such as manipulation, grasping and locomotion. Topics include forward and inverse kinematics; path planning, collision avoidance and trajectory generation; robot body and actuator dynamics; sensory feedback and trajectory tracking; stability, disturbance rejection and force control.
Objectives
 Introduce mathematical foundation of robot motion and control in a general setting.
 Introduce ideas of configuration space, path planning and trajectory generation.
 Teach use of geometric and dynamical models in robotics.
 Highlight control theoretic issues in trajectory planning and sensory feedback.
 Introduce practical applications of robotics in mobile manipulation tasks, teleoperation, automated assembly, manufacturing, home and service industry.
Prerequisites
Grads. CMPE435 (Robotics ) OR CMPE432 (Feedback design) OR CMPE633 (Fall 2011) OR permission of instructor Undergrads. EE361 (Feedback control) AND permission of instructor
Text book
The course will be taught from a combination of the following textbooks.
Primary Texts
 Robot Dynamics and Control by Spong et al. (Available as a free legal download)
 Principles of Robot Motion by Choset et al. [available as low priced edition]
Secondary Texts
 Planning Algorithms by Steve Lavalle. [available as a free legal download]
 Probabilistic Robotics by Thrun et al.
 Research papers.
Grading Scheme
 Assignments: 20%
 Midterm: 20%
 Final Exam: 30%
 Group Project: 30%
 Proposal. 10%
 Report. 20%
 Presentation. 20%
 Demo/working. 50%
Course Delivery Method
Lectures. Labs
Schedule
WEEK  TOPICS  READINGS/REFERENCES 

Week 1. August 23  Lec 1. Introduction; robotics and autonomous systems;
Lec 2. workspaces; configuration spaces; planning algorithms; bug algorithms; Bug0 and Bug1 algorithms;  Choset CH 1,2; 
Week 2. August 30  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; wallfollowing behavior with range sensors;  Choset CH 2; LaValle SEC 12.3.3; 
Week 3. September 6  Lec 5. Introduction to discrete planning; state space models and examples; discrete feasible planning; graph search algorithms; general forward search;
Lec 6. Breadth first search; depth first search; Dijkstra's algorithm; A*; systematic searches; configuration spaces revisited; mechanical linkages; forward and inverse kinematics; toroidal configuration spaces;  LaValle CH2; 
Week 4. September 13  Lec 7. Mapping workspace obstacles into configuration space obstacles; visualizing high dimensional configuration spaces; a first look at obstacles in SE(2); polygonal robots and obstacles;  Choset CH3, Appendix F 
Week 5. September 20  Lec 8. representing polygonal objects via linear inequalities; computing configuration space obstacle polygons from workspace descriptions; star algorithm; Minkowski sums and differences of sets;
Lec 9. Holonomic constraints; degrees of freedom in a configuration space; rigid body CSpace in 2D; rigid bodies in 3D; SO(3) matrix group; Euler parameterization;
 Lavalle SEC4.3.2, SEC3.2;
Choset CH3, Appendex F, E. 
Week 6. September 27  Lec 10. SO(2) revisited via complex numbers; generalization to SO(3); quaternion algebra; topology of SO(3);
Lec 11. Gimbal lock problem; parameterization problems and topology of configuration spaces; topology of S^1 x S^1 vs S^2; S^3; SO(3); RP^3; quaternions as a double cover of SO(3);  Lavalle SEC3.2 ,SEC4.2; Choset CH3, Appendix E;
Gimbal lock problem in Euler Angles. Video. 
Week 7. October 4  Lec 12. 2D and 3D rigid bodies revisited; rotations and translations of points and bodies; types of joints; kinematic chains; DHparameters;
Lec 13. Introduction to roadmaps; deterministic/combinatorial methods for roadmap construction; general properties; visibility graphs; linesweep algorithm;  LaValle SEC3.3; Choset CH5. 
Week 8. October 11  Lec 14. Voronoi diagrams; generalized Voronoi diagrams (GVD); bushfire algorithm; wavefront algorithm;
Lec 15. Potential field methods; vector fields and gradients; critical points and Hessian matrix; attractive and repulsive potential fields; gradient descent algorithm; local minima and other practical implementation issues; another look at bushfire algorithm;  Choset CH4; 
Week 9. October 18  Lec 16. Introduction to navigation functions; navigation functions for the sphereshaped world; compositions of potentials; analytical switches; empirical tuning of navigation functions;
Lec 17. Starshaped worlds; diffeomorphisms; mapping stars to spheres; analytical switches revisited; navigation functions for star worlds;  Choset CH4; 
Week 10. October 25  Lec 17. Inverse and forward kinematics; lifting velocities and forces between coordinate changes; modified potential field method for direct workspace control;
Lec 18. Introduction to sampling based planning; problem history and motivation; computational complexity of planning algorithms; introduction to probabilistic roadmaps algorithm (PRM); query phase of PRM; Tutorial. Using the MATLAB robotics toolbox. (Oct 29)  Choset SEC3.8, SEC4.7, CH 7; 
Week 12. November 1  Project Proposal Presentations. (Session 1)
Project Proposal Presentations. (Session 2)  
Week 13. November 8  Lec 20. PRMs continued. Refinements in PRM construction; sampling difficulties and narrow passages; distance functions for nonEuclidean spaces; metric spaces and Lpnorms for complex configuration spaces; Cartesian products; metrics for S^1, SO(3) using complex numbers and quaternions; generating a random configuration in S^1, SO(3);
Lec 21. Introduction to probabilistic robotics; modeling sensor noise; basic probability and statistics; linear transformations of Gaussian random variables; interpreting and visualizing the covariance matrix; state and measurement; Lec 22. Linear systems; discretetime linear models from equations of motion; examples; process noise and sensor noise; Objectives of Kalman filtering;  Choset CH7, CH8; 
Week 14. November 15  
Week 15. November 22 
Lec 23. Basic laws of probability and Bayes rule; belief about state; derivation of the Bayesian filter; prediction and update; dealing with beliefs and distributions for nonlinear nonGaussian systems; Kalman Filtering as a special case; Lec 24. Derivation of the Kalman filter; combining estimates from two scalar Gaussians; innovation and Kalman gain; estimation in the absence of process and sensor noise; state and covariance prediction; combining measurement and prediction in the presence of process noise;  Thrun CH 2; Choset CH8

Week 16. Novemeber 29 
Lec 25. Completion of proof of KF; basic SLAM using Kalman filtering; nonlinear models of sensing and robot motion; Extended Kalman filtering; Lec 26. Nonlinear transformations of random variables; generation of nonGaussian noise via nonlinear transforms; modeling sensor noise; ultrasound, laser models and other nonGaussian sources; data association problem; Lec 27. Probabilistic localization using Bayesian filtering; data association problem revisited; histogram Bayes filter; introduction to particle filtering; computational issues in sampling arbitrary distributions; practical demonstration of Kalman filtering.  Thrun CH 2,3,4; Choset CH8 
Week 17. December 6 
Lec 27. Wrapup. Future directions. Take home Exam. Due December 7th.
 
Week 18. December 13
Dec 27. Final grades submission; Dec 20Jan 21 Semester break.  Project Presentations/Viva/Demo 1. Dec 14th
Project Presentations/Viva/Demo 2. Dec 15th
