Knowledge- and structure- driven motion planning

Gao Tang, Yajia Zhang, Kris Hauser

Summary

Although configuration space paradigms lead to effective general-purpose motion planning algorithms for problems of moderate scale (dozens of dimensions), they work poorly on very large-scale problems that involve hundreds or thousands of dimensions, mixed continuous and discrete reasoning, or require near-optimal solutions. New planning paradigms are needed to handle these large problems. This project is investigating two complementary approaches: 1) knowledge-driven, in which good solutions exhibit of common patterns that can be learned automatically (e.g., a humanoid using similar stepping motions to navigate uneven terrain), and 2) structure-driven, in which the constraints and performance criteria possess inherent structure that can be exploited to compute better plans more quickly (e.g., the motions of distant objects are essentially decoupled). We have applied these techniques to a wide variety of systems, including drones, humanoid robots, manipulation under clutter, and the motion of biological molecules.

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Illustration of the Minimum Constraint Displacement algorithm on a problem with 100 circular obstacles. A roadmap is grown using sampling-based techniques, with increasing exploration limits, until a path is found to the goal. Local optimization improves path quality and displacement magnitudes. The process repeats until convergence.