Traversing Environments Using Possibility Graphs for Humanoid Robots
This addresses the challenge of multi-modal locomotion planning for humanoid robots in adverse settings, representing an incremental improvement over existing methods.
The paper tackles the problem of enabling humanoid robots to traverse complex environments requiring multiple locomotion modes by introducing a Possibility Graph that uses high-level approximations to efficiently explore action possibilities, showing it can quickly find paths through challenging environments.
Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments. Footstep planners are typically only designed for one mode of locomotion, but traversing unfavorable environments may require several forms of locomotion to be sequenced together, such as walking, crawling, and jumping. Multi-modal motion planners can be used to address some of these problems, but existing implementations tend to be time-consuming and are limited to quasi-static actions. This paper presents a motion planning method to traverse complex environments using multiple categories of actions. We introduce the concept of the "Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level single-action motion planners to be utilized more efficiently. We show that the Possibility Graph can quickly find paths through several different challenging environments which require various combinations of actions in order to traverse.