Objectives and Outcomes


  • Instructor: Amarda Shehu amarda\AT\cs.gmu.edu
    Place and Time: Innovation Hall 131, W 4:30-7:10 pm
    Office Hours: ENGR #4422, W 2:30-4:30 pm


  • At the end of this course, students will have been familiarized with methods for a computational treatment of complex physical systems in the presence of geometric and dynamic constraints. Students will be able to implement deterministic and sampling-based approaches for planning motions of robot systems in the presence of obstacles. In particular, students will be able to employ the OOPSMP motion-planning programming platform to implement advanced planning methods that can handle kinodynamic constraints. Selected topics will expand students' understanding of sensor-based motion planning, manipulation planning, assembly planning, planning under uncertainty, and robotics-inspired methods for the modeling and characterization of biological molecules as special articulated chains.