systems to interact in a synergistic and trustworthy fashion with human operators, and handling uncertainty in the location, motion, and intent of other objects. The research groups led by MIT Aeronautics and Astronautics Professors Emilio Frazzoli, Jonathan P. How, and Brian Williams have developed several new approaches to address the computational complexity challenges inherent in both trajectory design and activity planning.
TRAJECTORY PLANNING Trajectory planning in an environment with constrained dynamics and obstacles is a complex problem, even for guiding a single vehicle. Numerous approximate solutions have been developed over the years using a variety of different strategies. One approach, called sampling-based planning, is particularly attractive because it scales well with the problem size and provides a systematic method of exploring the environment for a feasible path, while, at the same time, refining any path to the goal that may have been found. Key amongst these algorithms is the Rapidly-Exploring Random Tree (RRT) that has proven to be a successful approach for designing safe trajectories for autonomous systems. MIT DARPA Urban Challenge team members How and Frazzoli built on this success to develop the Closed-loop Rapidly-Exploring Random Tree (CL-RRT) algorithm to address the challenges of planning paths for an autonomous car with unstable and nonlinear dynamics. The algorithm was successfully demonstrated at the 2007 DARPA Urban Challenge, which was held from October 26 though November 3rd in Victorville, Calif. MIT developed a unique autonomous vehicle: Talos, a Land Rover LR3 equipped with a diverse range of lidar, vision, radar, and navigation sensors connected to a powerful blade cluster computer system. The MIT vehicle employed novel algorithmic approaches to perception, planning, and control for the challenging task of autonomous driving in uncertain, dynamic environments. The vehicle was one of 35 that participated in the DARPA Urban Challenge National Qualifying Event, and, based on our performance there, was one of 11 teams to qualify for the Urban Challenge Event based on our performance. The vehicle was one of only six teams to complete the race, finishing in fourth place.
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AEROASTRO 2010-2011