A 65-nm Humanoid Robot System-on-Chip Using Time-Domain 3-D Footstep Planning and Mixed-Signal ZMP Gait Scheduler With Inverse Kinematics

Abstract

This work presents a footstep planning chip for humanoid robot. It integrates a time-domain graph search engine for high-level 3-D footstep planning and a mixed-signal zero moment point (ZMP) gait scheduler with neural inverse kinematics, enabling efficient low-level motion control. The key contributions of this work include a time-domain graph search engine for 3-D footstep planning, featuring 3-D search capabilities, $D^{\ast } $ replanning for real-time adjustments, redundant path blocking, and efficient result readout. In addition, it introduces an energy-efficient mixed-signal ZMP gait scheduler for maintaining robot balance, along with a time-domain neural-network-based inverse kinematics module for controlling robot joints. This work is demonstrated in situ on a fully assembled robot using the 65-nm system-on-chip (SoC), achieving $2.7\times $ energy savings for graph search and an $18.4\times $ improvement in energy efficiency for motion control compared with prior works.