Work

Energy-Efficient Motion Planning for Repetitive Industrial Tasks: An Adaptive Obstacle Modeling Approach

Year: 2026

Type: article

Abstract: Efficient operation of robotic manipulators in repetitive industrial tasks, such as welding and logistics sorting, requires careful coordination of obstacle representation and motion planning. Traditional methods, such as axis-aligned bounding boxes, generate overly conservative trajectories, while highly detailed models impose excessive computational burden, both increasing cumulative energy consumption in long-duration operations. This paper presents an adaptive sphere-based obstacle modeling framework integrated with energy-aware motion planning for repetitive manipulation tasks. The proposed method employs an improved Whale Optimization Algorithm with nonlinear parameter adjustment and elite guidance mechanisms to generate compact sphere representations through adaptive voxelization. Experimental validation using a 6-DOF UR5 manipulator demonstrates substantial performance improvements over conventional AABB models, achieving 31–66% energy reduction and 12.5–37% shorter configuration-space paths, with competitive modeling efficiency (2.63–3.34 s) compared to 11 metaheuristic algorithms. The framework provides a systematic methodology for integrating obstacle modeling with motion planning, particularly suitable for applications where cumulative energy savings are critical in repetitive operations.

Source: Applied Sciences

DOI: 10.3390/app16062842

Authors: Zhitao Yang, Likun Hu

Institution: Guangxi University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896