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Motion Planning in Complex Environments using Closed-loop Prediction

This paper describes the motion planning and control subsystems of Team MIT’s entry in the 2007 DARPA Grand Challenge. The novelty is in the use of closed-loop prediction in the framework of Rapidly-exploring Random Tree (RRT). Unlike the standard RRT, an input to the controller is sampled, followed by the forward simulation using the vehicle model and the controller to compute the predicted trajectory. This enables the planner to generate smooth trajectories much more efficiently, while the randomization allows the planner to explore cluttered environment. The controller consists of a Proportional-Integral speed controller and a nonlinear pure-pursuit steering controller, which are used both in execution and in the simulation-based prediction. The main advantages of the forward simulation are that it can easily incorporate any nonlinear control law and nonlinear vehicle dynamics, and the resulting trajectory is dynamically feasible. By using a stabilizing controller, it can handle vehicles with unstable dynamics. Several results obtained using MIT’s race vehicle demonstrate these features of the approach.