Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs

TL;DR

This paper presents a novel planning method called the Randomized Possibility Graph that efficiently explores feasible bipedal robot motions in semi-unstructured environments with arbitrary obstacles by leveraging high-level approximations.

Contribution

The paper introduces the Randomized Possibility Graph, a new planning approach that combines high-level approximations with lower-level motion planning for complex environments.

Findings

Successfully demonstrated in simulations with semi-unstructured terrains

Efficiently explores the continuum of whole body motions

Handles arbitrary 3D obstacles effectively

Abstract

Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions; they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the "Randomized Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, "semi-unstructured" means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions.