Bi-Level Optimization for Contact and Motion Planning in Rope-Assisted Legged Robots
Ruben Malacarne, Ioannis Tsikelis, Enrico Mingo Hoffman, Michele Focchi
TLDR
This paper introduces a bi-level optimization framework for rope-assisted legged robots to plan contact and motion for climbing vertical surfaces.
Key contributions
- Presents a bi-level optimization framework for rope-assisted legged robots climbing vertical surfaces.
- Addresses mixed-integer problems: selecting landing regions and optimizing control inputs (tensions, forces).
- Uses Cross-Entropy Method for outer level and gradient-based optimization for inner level.
- Validated on a novel climbing robot, ALPINE, across various challenging terrain configurations.
Why it matters
This paper advances robotic locomotion by enabling rope-assisted legged robots to autonomously plan complex climbs. The bi-level optimization approach allows for robust navigation on challenging vertical terrains, paving the way for applications in inspection and exploration.
Original 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.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.