Intelligent Control of Multilegged Robot Smooth Motion: A Review

Abstract

Motion control is crucial for multilegged robot locomotion and task completion. This study aims to address the fundamental challenges of inadequate foot tracking and weak leg compliance control in multilegged robot motions. The first section summarizes and discusses the feasibility and operability of smooth motion for multilegged robots based on the necessary conditions and application value of walking over complicated and unstructured terrain. The second to fourth sections present a qualitative research method of literature review to study the effects of several factors on the smooth motion of multilegged robots, including foot force perception and motion planning, active compliance control, gait stability, and Deep Reinforcement Learning (DRL) control. We conducted a case analysis of multilegged robots using traditional Nonlinear Model Predictive Control (NMPC), biomimetic Central Pattern Generator (CPG), and DRL control in the latest international open-source projects to help readers further follow the latest open-source algorithms for legged robot motion control. In addition, we analyze the challenges these influencing factors face in optimization and provide suggestions for effectively promoting the realization of review objectives. In the fifth section, we discuss the challenges of springy gait and intelligent switching control in solving the problems of poor foot tracking and weak leg compliance. We explore and forecast the potential application of the Probability Density Function (PDF) control of the foot force in the smooth motion of multilegged robots. In the sixth section, we systematically discuss, analyze, and summarize the existing problems in this research field. We also highlight the shortcomings of existing research methods, look forward to future research areas, and highlight the obstacles that need to be overcome to promote future research in this field.

Keywords

• Multilegged robot
• foot planning
• gait stability
• compliant control
• probability density function control
• deep reinforcement learning control