CS689 Planning Motions of Robots and Molecules
Place and Time: Innovation Hall 206, F 4:30-7:10 pm
Office Hours: F 2:30-4:30 pm
This course covers topics from artificial intelligence, algorithms, and databases. It presents algorithms that model and simulate physical and biological systems. The course will focus on motion-planning algorithms for robotic systems in the presence of obstacles. Simple deterministic and sampling-based approaches to motion planning will be covered. Advanced planning methods including planning with kinematics and dynamic constraints will also be presented. Selected topics will include 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.
Material will be disseminated in the form of lectures. Students will be tested on the comprehension of the basic material through homework programming projects and a midterm exam. In addition to the basic material, special topics will be covered. Extra credit in the homeworks will allow students that are interested in advanced topics and research to demonstrate their abilities. Extra credit will not account for more than 10% of the total grade of a homework. No programming is involved in the exam, only pseudocode. No late homeworks or project deliverables will be accepted. A final research project will replace the final exam.
An exciting component of the class is that students will be able to do high-level programming and quickly reap the benefits of decades of research in algorithmic motion planning. The OOPSMP programming platform will be employed, which allows students to implement state-of-the-art planning algorithms through essentially plug-and-play. The platform will make it not only trivial to implement current algorithms but also fun to experiment with new ones. Students will be free to explore their own research ideas in this class.
Homeworks (3): 45%
Paper Presentation: 5%
Final Project: 25%