Traversing Environments Using Possibility Graphs with Multiple Action Types
This addresses the challenge of multi-modal locomotion for legged robots in adverse environments, representing an incremental improvement over existing multi-modal planners.
The paper tackles the problem of enabling legged robots to traverse complex environments by sequencing multiple locomotion types like walking, crawling, and jumping, presenting a motion planning method that uses a Possibility Graph to quickly find routes through challenging scenarios.
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 continuous actions. To this end, this paper formulates and exploits the Possibility Graph---which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions---to utilize lower-level single-action motion planners more effectively. We show that the Possibility Graph can quickly find routes through several different challenging environments which require various combinations of actions in order to traverse.