University of North Carolina
S. K. Gupta
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. At the same time, advances in manufacturing technologies, sensing and actuator devices have led to the development of powerful robot systems and they need novel capabilities related to dynamic constraints, modeling uncertainty, as well as real-time computation of motion strategies.
In this talk, we will give a brief overview of our recent work on motion planning and coordination for single and multi-robot systems. These include novel methods for motion planning of high-DOF robots that exploit the computational capabilities of many-core GPUs; dealing with point-cloud or noisy sensor data using machine learning and classification methods; local collision avoidance and computation of smooth trajectories for multiple robots based on reciprocal velocity obstacles; and novel methods that combine planning with linear-quadratic feedback control to handle uncertainty. We will highlight the results on the PR2 robot as well as differential-drive robot systems, such as iRobot Create mobile robots.
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 leading conferences on graphics, geometric modeling, visualization, multimedia and high-performance computing. He has published more than 300 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 100,000 users and are widely used in the industry. He has supervised 18 Ph.D. dissertations and is a fellow of ACM and AAAS.