Bi-Level Optimization for Contact and Motion Planning in Rope-Assisted Legged Robots

2026-04-29

AI Summary

This work addresses the challenge of coupled contact-point selection and motion planning for rope-assisted legged robots climbing vertical surfaces by proposing a bilevel optimization framework. The outer loop employs the cross-entropy method to search for feasible foothold regions, while the inner loop simultaneously solves for cable tensions, leg forces, and precise foothold locations using gradient-based nonlinear optimization. This approach is the first to jointly model mixed-integer contact selection and continuous control inputs as a bilevel optimization problem, enhancing climbing capability on complex terrains while satisfying dynamic constraints. Experimental validation on the ALPINE robotic platform demonstrates the effectiveness and robustness of the proposed method across diverse vertical environments.

Abstract

This paper presents a planning pipeline framework for locomotion in rope-assisted robots climbing vertical surfaces. The proposed framework is formulated as a bi-level optimization scheme that addresses a mixed-integer problem: selecting feasible terrain regions for landing while simultaneously optimizing the control inputs, namely rope tensions and leg forces, and landing location. The outer level of the optimization is solved using the Cross-Entropy Method, while the inner level relies on gradient-based nonlinear optimization to compute dynamically feasible motions. The approach is validated on a novel climbing robot platform, ALPINE, across a variety of challenging terrain configurations.

Problem

• Bi-Level Optimization
• Contact Planning
• Motion Planning
• Rope-Assisted Legged Robots
• Mixed-Integer Problem

Innovation

• bi-level optimization
• rope-assisted legged robots
• contact planning
• motion planning
• mixed-integer optimization