United we stand, divided we fall: Integrating Continuous Robot Motion Planning and Discrete Action Planning

12:00 noon, November 10, Tuesday, 2009, ENGR 4201

Speaker

Erion Plaku
Postdoctoral Fellow
Laboratory for Computational Sensing and Robotics
Johns Hopkins University

Abstract

Research in robotics has focused since its inception towards increasing the ability of robots to plan and act on their own in order to complete assigned high-level tasks.

Toward this goal, this talk presents a multi-layered approach that automatically and efficiently plans the sequence of motions the robot needs to execute so that the resulting trajectory is dynamically feasible, avoids collisions with obstacles, and satisfies a given high-level specification. In distinction from traditional approaches in motion planning, the proposed approach can take into account sophisticated high-level specifications given by Finite State Machines, Linear Temporal Logic, STRIPS, Hidden Markov Models, and other planning-domain definition languages. Such expressive models make it possible to specify complex tasks that frequently arise in navigation, manipulation, robotic-assisted surgery, search-and-rescue missions. Initial validation in physics-based simulations with high-dimensional robotic models demonstrate significant computational speedups over related work and show the ability of the proposed approach to efficiently plan valid trajectories that satisfy complex high-level specifications.

Short Bio

Erion Plaku is a Postdoctoral Fellow at the Laboratory for Computational Sensing and Robotics at Johns Hopkins University. He received the Ph.D. degree in Computer Science from Rice University in 2008. His research focuses on motion planning and control of cyber-physical systems for human-machine cooperative or fully automatic task performance in complex domains. Some applications include robot navigation, manipulation, haptic exploration, and robotic-assisted surgery. His research interests encompass robotics, hybrid systems, AI, logic, data mining, and large-scale distributed computing.