Optimization Based Motion Planning for Multi-Limbed Vertical Climbing Robots

TL;DR

This paper presents an optimization-based motion planning approach for a six-legged wall-climbing robot, decoupling posture and contact force planning to handle constraints like torque, force, and friction.

Contribution

It introduces a novel decoupled optimization framework combining MICP/NLP for posture and convex optimization for contact forces, enabling effective climbing on complex surfaces.

Findings

Successful simulation and experimental validation on various wall configurations.

Enhanced climbing stability and obstacle avoidance capabilities.

Efficient computation of feasible trajectories under physical constraints.

Abstract

Motion planning trajectories for a multi-limbed robot to climb up walls requires a unique combination of constraints on torque, contact force, and posture. This paper focuses on motion planning for one particular setup wherein a six-legged robot braces itself between two vertical walls and climbs vertically with end effectors that only use friction. Instead of motion planning with a single nonlinear programming (NLP) solver, we decoupled the problem into two parts with distinct physical meaning: torso postures and contact forces. The first part can be formulated as either a mixed-integer convex programming (MICP) or NLP problem, while the second part is formulated as a series of standard convex optimization problems. Variants of the two wall climbing problem e.g., obstacle avoidance, uneven surfaces, and angled walls, help verify the proposed method in simulation and experimentation.