In a May 2016 ceremony, Lockheed Martin CTO Keoki Jackson (center, left) AeroAstro ‘89, SM ‘92, ScD ‘97, and MIT AeroAstro department head Jaime Peraire congratulate each other after signing a research collaboration agreement between the two organizations. Initial research will focus on robotics and autonomous systems, and be conducted professors AeroAstro professors Jonathan How, Nick Roy, Sertac Karaman, Julie Shah, and Russ Tedrake, and Department of Mechanical Engineering Professor Sangbae Kim. (William Litant/MIT photograph)
ment, beliefs, uncertainties, intentions of the vehicles, predicted behaviors (e.g., trajectories), and confidence intervals of the learning algorithms. Recent research includes the following: Robust Planning in Uncertain Environments: ACL developed consensus-based bundle algorithm (CBBA) as a distributed taskplanning algorithm that provides provably good, conflict-free,
approximate solutions for heterogeneous multi-agent missions. Aside from extensions to task time-windows, coupled agent constraints, asynchronous communications, and limited network, CBBA has been validated in real-time flight test experiments. ACL has also extended its development of chance-constrained rapidlyexploring random trees (CC-RRT), a robust planning algorithm to identify probabilistically feasible trajectories, to new aerospace
Published on Nov 18, 2016
Annual magazine review of MIT Aeronautics and Astronautics Department research and educational initiatives.