Date & Time:
Monday, July 16, 2012; 2:00 PM
Operator error is a significant factor in a majority of manned and unmanned vehicle accidents. In this talk, a framework for semi-autonomous vehicle accident avoidance will be presented that has been shown to effectively mitigate collisions caused by operator error. The framework analyzes sensor data (from vision and/or LIDAR data) to identify "no go" regions in the environment, and automatically synthesize constraints on vehicle position. An optimal trajectory and associated control inputs are then found via linear or nonlinear model predictive control. The "threat" to the vehicle is quantified from various metrics computed over the optimal trajectory. A number of approaches for arbitrating between operator and control system authority, based on the predicted threat, will be discussed. Extensive simulation and experimental testing will be described for both manned and unmanned scenarios. Future directions in threat assessment and semi-autonomous control, based on the integration of vision-based sensing and active steering control, will also be discussed.
Dr. Karl Iagnemma
Director, MIT Robotic Mobility Group
Dr. Karl Iagnemma is a Principal Research Scientist at MIT, where he directs the Robotic Mobility Group. He holds a B.S. from the University of Michigan, and an M.S. and Ph.D. from MIT, where he was a National Science Foundation Graduate Fellow. Dr. Iagnemma's primary research interests are in the areas of design, modeling, motion planning, and control of mobile robots and vehicle systems. His research in these areas has been supported by DARPA, Ford Motor Company, TARDEC, ARO, and other organizations. He is author of the monograph, "obile Robots in Rough Terrain: Estimation, Planning and Control with Application to Planetary Rovers" (Springer, 2004). Dr. Iagnemma serves as an advisor to NASA in the area of MER rover mobility analysis, and is a science team collaborator for the upcoming MSL rover mission.