Stability Constrained Mobile Manipulation Planning on Rough Terrain

TL;DR

This paper introduces a real-time trajectory planning framework for mobile manipulators on rough terrain, ensuring stability and traction optimization through a novel sampling-based algorithm and nonlinear control, demonstrated in timber harvesting simulations.

Contribution

It develops a stability-constrained planning method combining sampling-based and nonlinear control techniques for mobile manipulators on uneven terrain.

Findings

Feasible online trajectory planning on rough terrain.

Effective stability and traction optimization demonstrated.

Enhanced computational efficiency in dynamic environments.

Abstract

This paper presents a framework that allows online dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are presented as theoretical background. Then, a sampling-based quasi-static planning algorithm modified for stability guarantee and traction optimization in continuous dynamic motion is presented along with a mathematical proof. The robot's quasi-static path is then used as an initial guess to warm-start a nonlinear optimal control solver which may otherwise have difficulties finding a solution to the stability-constrained formulation efficiently. The performance and computational efficiency of the framework are demonstrated through an application to a simulated timber harvesting mobile manipulator machine working on varying terrain. The results demonstrate feasibility of online trajectory planning on varying terrain while satisfying the dynamic stability constraint.