Bi-Level Optimization for Contact and Motion Planning in Rope-Assisted Legged Robots
It addresses the mixed-integer problem of selecting terrain regions and optimizing control inputs for rope-assisted climbing robots, a niche domain-specific challenge.
This paper presents a bi-level optimization framework for planning contact and motion in rope-assisted legged robots climbing vertical surfaces, validated on the ALPINE robot across challenging terrains.
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.