Multi-objective posture planning for unmanned walking excavators based on nested optimization

Abstract

Walking excavators are all-terrain multifunctional excavators that are often utilized in operations on complex unstructured terrain. Due to structural complexity and the diversity of the terrain on which these excavators operate, ride comfort is a crucial and challenging factor. To address these issues, this paper proposes a nested posture planning strategy for unmanned walking excavators (UWEs) based on multi-objective optimization. First, a 7-degree-of-freedom kinematic model of the chassis is established based on closed-loop vector equations, and a machinery–terrain coupling dynamical model is established based on the Lagrange method. Subsequently, the radial basis function (RBF) is employed to characterize the motion trajectories of the supporting hydraulic cylinders. Furthermore, a nonlinear trajectory planning model that integrates multiple objectives, multiple constraints, and terrain information is constructed to minimize attitude deviations and energy consumption associated with active adjustments during operation. To accelerate model solving, a two-stage nested optimization strategy is proposed. Finally, a high-fidelity mathematical-physics environment and real-world experiments are constructed to investigate the performance of the proposed method. The results demonstrate that the UWE can successfully traverse challenging terrain with excellent chassis posture in several scenarios.

Graphical abstract

Keywords

unmanned walking excavator / radial basis function / nested optimization / nonlinear trajectory planning

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