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Journal Article

Probabilistic Completeness of Randomized Possibility Graphs for Bipedal Walking in Semi-Unstructured Environments


We present a theoretical analysis of a recent whole body motion planning method, the Randomized Possibility Graph, which uses a high-level decomposition of the feasibility constraint manifold in order to rapidly find routes that may lead to a solution. These routes are then examined by lower- level planners to determine feasibility. In this paper, we show that this approach is probabilistically complete for bipedal robots performing quasi-static walking in “semi-unstructured” environments. Furthermore, we show that the decomposition into higher and lower level planners allows for a considerably higher rate of convergence in the probability of finding a solution when one exists. We illustrate this improved convergence with a series of simulated scenarios.


Michael X. Grey
Aaron D. Ames
C. Karen Liu
Journal Name
Robotics Science and Systems (RSS), 2017
Publication Date
June, 2017