Catalog Description: This course will start with an introduction to the configuration spaces and kinematics of different robotic systems, including holonomic & non-holonomic mobile robots, spatial robots, and robotic manipulators. Following that basic motion planning algorithms, including potential & navigation function-based motion planning and graph search based motion planning, will be introduced. Sensor-based motion planning and motion planning under uncertainties using probabilistic representations will be introduced. Students will learn about estimation and filtering (Kalman filter, Markov filter, particle filters) and probabilistic robot action models (Markov chains, Markov decision processes, POMDP). Students will get hands-on experience in implementing the algorithms on MATLAB/C++. Application to multi-robot coordination problems, multi-robot coverage problems, pursuit-evasion problems, task allocation problems and exploration problems will be discussed. If time permits, students will be briefly introduced to topological motion planning, motion planning on manifolds and motion planning on flow fields. The evaluation will be based on two term projects and a final presentation.

Course Site:

• https://coursesite.lehigh.edu/course/view.php?id=168530

Course Documents:

• Course Information and Policies

teaching robotics