Real-Time Motion Planning for Physical Robots
Algorithmic motion planning has been actively studied in robotics and related areas for more than three decades. There is a rich collection of techniques that have been successfully used for CAD/CAM, bioinformatics, computer gaming and other applications. However, current techniques have two major limitations in terms of applications to physical robots. They are mostly designed for static environments or assume an exact geometric representation of all the obstacles in the scene.
In this talk, we give a brief overview of our recent work on proximity queries and motion planning for high-DOF robots. We present novel techniques to perform collision and proximity queries with point-cloud sensor data, collected using depth or other sensors. We also present new planning algorithms based on optimization-formulation and sample-based planning. The optimization formulation takes into account collision-free and dynamics constraints, and handles dynamic obstacles using a replanning framework. We demonstrate its application to human-like robots with dynamic constraints and Cartesian planning of industrial manipulators. In terms of sample-based planning, we present a novel RRT-based algorithm based on Poisson sampling. All these planners can be parallelized on commodity, many-core GPUs and offer considerable speed over prior techniques. Finally, we present algorithms for multi-robot planning using reciprocal velocity obstacles. We highlight their performance on human-like models with 26-DOF, PR2 robot with active sensing and a KUKA manipulator. Furthermore, we demonstrate their applications to warehouse automation and human-robot interaction.
Dinesh Manocha is currently the Phi Delta Theta/Mason Distinguished Professor of Computer Science at the University of North Carolina at Chapel Hill. He received his Ph.D. in Computer Science at the University of California at Berkeley 1992. He has received Junior Faculty Award, Alfred P. Sloan Fellowship, NSF Career Award,
Office of Naval Research Young Investigator Award, Honda Research Initiation Award, Hettleman Prize for Scholarly Achievement. Along with his students, Manocha has also received 12 best paper awards at the leading conferences on graphics, geometric modeling, visualization, multimedia and high-performance computing. He has published more than 450 papers and some of the software systems related to collision detection, GPU-based algorithms and geometric computing developed by his group have been downloaded by more than 150,000 users and are widely used in the industry.
He has supervised 26 Ph.D. dissertations and is a fellow of ACM, AAAS, and IEEE. He received Distinguished Alumni Award from Indian Institute of Technology, Delhi. He was a co-founder of Impulsonic, developer of physically-based sound simulation tools, and that was acquired by Valve.