Evaluation of Operational Energy Efficiency for Bridge Cranes Based on an Improved Multi-Strategy Fusion RRT Algorithm

• Published in: Machines (Basel) Vol. 13; no. 10; p. 924
• Main Authors: Wang, Quanwei, Wang, Xiaoyang, Ji, Ziya, Liu, Weili, Fang, Yingying, Hou, Jiayi, Liu, Xuying, Wen, Hao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 07.10.2025
• Subjects: Adaptability; Algorithms; Automation; Bias; Cranes; Cranes & hoists; Curves; Deep learning; Energy consumption; Energy efficiency; Flexibility; Heuristic; Materials handling; Nodes; Optimization; Path planning; Planning; Sampling; Smoothness; Strategy
• ISSN: 2075-1702; 2075-1702
• DOI: 10.3390/machines13100924

Aiming at the problems of low efficiency, high energy consumption, and poor path quality during the multi-mechanism operation of bridge cranes in spatial tasks, an improved Rapidly exploring Random Tree (RRT) algorithm based on multi-strategy fusion is proposed for energy-efficient path planning. First, the improved algorithm introduces heuristic path information to guide the sampling process, enhancing the quality of sampled nodes. By defining a heuristic boundary, the search space is constrained to goal-relevant regions, thereby improving path planning efficiency. Secondly, focused sampling and reconnection strategies are adopted to significantly enhance path quality while ensuring the global convergence of the algorithm. Combined with line segment sampling and probability control strategies, the algorithm balances global exploration and local refinement, further optimizing path selection. Finally, Bezier curves are applied to smooth the generated path, markedly improving path smoothness and feasibility. Comparative experiments conducted on a constructed three-dimensional simulation platform demonstrate that, compared to other algorithms, the proposed algorithm achieves significant optimization in planning time, path cost, number of path nodes, and number of random tree nodes, while generating smoother paths. Notably, under different operational modes, this study provides a quantitative evaluation of operational efficiency and energy consumption based on energy efficiency trade-offs, offering an effective technical solution for the intelligent operation of bridge cranes.