Abstract: Continuum robots are bio-inspired devices that seek to find a middle ground between traditional rigid robots and soft robots. With their mix of compliance, payload capability, inherent safety, dexterity, and manipulability, continuum robotic manipulators are perfectly suited to serve as collaborative robots alongside humans in potential areas ranging from manufacturing to healthcare applications. The high degrees of freedom of continuum manipulators, which results in kinematic redundancy, serve as their greatest challenge when it comes to path and motion planning. More specifically, due to their high compliance, performing reliable and stable path planning for such robots poses a major challenge.
In this talk, we will survey some recent progress towards performing path planning for continuum manipulators that addresses some of the above issues, while providing performance guarantees. We also survey some work under progress that investigates the use of machine learning techniques for performing stable motion planning, and the use of anticipatory planning methods for obstacle avoidance in dynamic environments.
Biographical Sketch: Iyad Kanj is a Professor of Computer Science in the School of Computing at DePaul University. He joined DePaul in 2001, after completing his Ph.D. degree at Texas A&M University. His research interests include parameterized algorithms, graph algorithms, and computational geometry. His recent work focuses on applications of the aforementioned areas to problems in AI and robot motion planning. He has published over 100 papers in international journals and conferences in theoretical computer science and artificial intelligence, and regularly serves on program committees of major conferences in these areas. He was awarded DePaul University’s Spirit of Inquiry Award in 2011, and The Excellence in Teaching Award in 2012. His research work has been supported by several internal and external grants.