From Dynamic Programming to RRTs: Algorithmic Design of Feasible Trajectories

This paper summarizes our recent development of algorithms that construct feasible trajectories for problems that involve both differential constraints (typically in the form of an underactuated nonlinear system), and global constraints (typically arising from robot collisions). Dynamic programming approaches are described that produce approximately-optimal solutions for low-dimensional problems. Rapidly-exploring Random Tree (RRT) approaches are described that can find feasible, non-optimal solutions for higher-dimensional problems. Several key issues for future research are discussed.

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