In this course different algorithmic approaches to solve robot motion-planning problems will be studied. the course will start with exact methods for low-dimensional configurations spaces and then continue to high-dimensional spaces. subsequently approaches more suitable for high-dimensional spaces will be studied (sampling-based methods and heuristic-search). finally! exampled of how these algorithms are integrated in different robotic applications such as medical devices will be presented.
learning outcomes: at the end of the course the students will be able to:
1. explain fundamental concepts and challenges in robot motion planning.
2. analyze the complexity of motion-planning algorithms. 3. distinguish between different algorithmic approaches to robot motion planning.
4. rate the suitability of existing motion-planning algorithms for a given motion-planning task.