Planning of Obstacle-Aided Navigation for Multi-Legged Robots Using a Sampling-Based Method Over Directed Graphs

Abstract

Existing work in legged robot navigation in cluttered environments often seeks collision-free paths that avoid obstacle interactions. Here we present a new approach for multi-legged robots to utilize leg-obstacle collisions to generate desired dynamics. To predict the change of robot state under repeated leg-obstacle collisions, we construct a discretized directed graph model: each node of the graph represents a different robot state, whereas the directed edges pointing from one node to another represent the transitions from one robot state to the next within one stride. These obstacle-modulated state transitions can depend on the robot gaits used. To capture this dependence, an empirical interaction model is used to compute the change of robot state based on initial contact positions between robot legs and obstacles. To validate the prediction of robot state transitions, we experimentally measure the state of a quadrupedal robot as it traverses a periodic obstacle field with three different gaits: bound, trot, and pace. We observed that the robot could passively converge to different steady state orientations, and these steady states corresponded well with the Strongly-Connected-Path-Components (SCPCs) within the directed graph. Searching over the graph for connected paths of SCPCs allows development of a gait planner that can generate gait switching strategies for a robot to achieve desired states by simply engaging with a sequence of leg-obstacle collisions. We demonstrate in experiments that using this method an open-loop quadrupedal robot was able to achieve desired orientations within a periodic obstacle field without any sensory input or active steering.