Massachusetts Institute of Technology
Talk Title: Navigation and Mapping for Robot Teams in Uncertain Environments
Many robotic tasks require robot teams to autonomously operate in challenging, partially observable, dynamic environments with limited field-of-view sensors. In such scenarios, individual robots need to be able to plan/execute safe paths on short timescales to avoid imminent collisions. Robots can leverage high-level semantic descriptions of the environment to plan beyond their immediate sensing horizon. For mapping on longer timescales, the agents must also be able to align and fuse imperfect and partial observations to construct a consistent and unified representation of the environment. Furthermore, these tasks must be done autonomously onboard, which typically adds significant complexity to the system. This talk will highlight three recently developed solutions to these challenges that have been implemented to (1) robustly plan paths and demonstrate high-speed agile flight of a quadrotor in unknown, cluttered environments; and (2) plan beyond the line-of-sight by utilizing the learned context within the local vicinity, with applications in last-mile delivery. We further present a multi-way data association algorithm to correctly synchronize partial and noisy representations and fuse maps acquired by (single or multiple) robots, showcased on a simultaneous localization and mapping (SLAM) application.
Jonathan P. How is the Richard C. Maclaurin Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He received a B.A.Sc. from the University of Toronto in 1987, and his S.M. and Ph.D. in Aeronautics and Astronautics from MIT in 1990 and 1993, respectively. Prior to joining MIT in 2000, he was an assistant professor in the Department of Aeronautics and Astronautics at Stanford University. He was the editor-in-chief of the IEEE Control Systems Magazine (2015-19) and was elected to the Board of Governors of the IEEE Control System Society (CSS) in 2019. His research focuses on robust planning and learning under uncertainty with an emphasis on multiagent systems. His work has been recognized with multiple awards, including the 2020 AIAA Intelligent Systems Award, the 2002 Institute of Navigation Burka Award, the 2011 IFAC Automatica award for best applications paper, the 2015 AeroLion Technologies Outstanding Paper Award for Unmanned Systems, the 2015 IEEE Control Systems Society Video Clip Contest, the IROS Best Paper Award on Cognitive Robotics (2017 and 2019) and three AIAA Best Paper in Conference Awards (2011-2013). He was awarded the Air Force Commander's Public Service Award (2017). He is a Fellow of IEEE and AIAA.