A receding-horizon multi-contact motion planner for legged robots in challenging environments

2026-02-11

AI Summary

This work addresses the challenge of efficiently generating multi-contact motions for legged robots in complex environments such as narrow passages, large gaps, and chimneys. The authors propose a receding-horizon multi-contact motion planner that jointly optimizes contact locations and whole-body trajectories, thereby circumventing conventional multi-stage pipelines. By integrating a quadratic-programming-based posture generator with a reactive replanning mechanism, the approach enhances robustness against local minima and improves computational efficiency. Experimental results demonstrate that, under short planning horizons, the method achieves a 45%–98% speedup over existing approaches. Although computational time increases with a four-step horizon, motion quality improves significantly, reducing the number of support changes by up to 47%.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We present a novel receding-horizon multi-contact motion planner for legged robots in challenging scenarios, able to plan motions such as chimney climbing, navigating very narrow passages or crossing large gaps. Our approach adds new capabilities to the state of the art, including the ability to reactively re-plan in response to new information, and planning contact locations and whole-body trajectories simultaneously, simplifying the implementation and removing the need for post-processing or complex multi-stage approaches. Our method is more resistant to local minima problems than other potential field based approaches, and our quadratic-program-based posture generator returns nodes more quickly than those of existing algorithms. Rigorous statistical analysis shows that, with short planning horizons (e.g., one step ahead), our planner is faster than the state-of-the-art across all scenarios tested (between 45% and 98% faster on average, depending on the scenario), while planning less efficient motions (requiring 5% fewer to 700% more stance changes on average). In all but one scenario (Chimney Walking), longer planning horizons (e.g., four steps ahead) extended the average planning times (between 73% faster and 400% slower than the state-of-the-art) but resulted in higher quality motion plans (between 8% more and 47% fewer stance changes than the state-of-the-art).

Problem

• multi-contact motion planning
• legged robots
• challenging environments
• receding-horizon planning
• whole-body trajectory

Innovation

• receding-horizon planning
• multi-contact motion planning
• whole-body trajectory optimization
• quadratic programming
• legged locomotion