Footstep Planning for the Honda ASIMO Humanoid

TLDR

Despite advances in stable dynamic walking, humanoid robots still lack navigation autonomy, especially in autonomously selecting foot placements to avoid obstacles. The study introduces a footstep planner for Honda ASIMO that sequences footstep positions to reach a goal while avoiding obstacles. The planner uses state‑dependent actions and an A* search over a finite set of possible foot placements to compute optimal footstep sequences within a time‑limited horizon. Experiments show ASIMO successfully navigating static and dynamic environments with predictable moving obstacles.

Abstract

Despite the recent achievements in stable dynamic walking for many humanoid robots, relatively little navigation autonomy has been achieved. In particular, the ability to autonomously select foot placement positions to avoid obstacles while walking is an important step towards improved navigation autonomy for humanoids. We present a footstep planner for the Honda ASIMO humanoid robot that plans a sequence of footstep positions to navigate toward a goal location while avoiding obstacles. The possible future foot placement positions are dependent on the current state of the robot. Using a finite set of state-dependent actions, we use an A* search to compute optimal sequences of footstep locations up to a time-limited planning horizon. We present experimental results demonstrating the robot navigating through both static and dynamic known environments that include obstacles moving on predictable trajectories.