Optimal Minimum Energy Path Planning of Mobile Robot Based on Improved WOA-GA with Hybrid Strategy for Community-Centric Application

• Published in: 2023 International Conference on Machine Learning and Cybernetics (ICMLC) pp. 489 - 494
• Main Authors: Du, Jiang, Da-Lin, Zhou, Gong-Fa, Li, Bao-Li, Lu, Jun-Tong, Yun, Ying, Liu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.07.2023
• Subjects: Energy consumption; Genetic algorithm; Green manufacturing; Machine learning; Machine learning algorithms; Mobile robot; Path planning; Planning; Whale algorithm; Whale optimization algorithms
• ISSN: 2160-1348
• DOI: 10.1109/ICMLC58545.2023.10327958

The path planning of mobile robots is investigated to minimize energy loss in intelligent manufacturing. To achieve optimal energy consumption planning, this paper proposes a hybrid strategy that enhances the whale optimization algorithm (WOA) and the genetic algorithm (GA). The WOA-GA algorithm combines their respective advantages and effectively mitigates premature convergence exhibited by the standard traditional algorithm. To enhance the global and local search capabilities of the standard WOA, we employ adaptive weighting and a Cauchy mutation strategy. Additionally, we introduce an adaptive crossover mutation probability and an elite strategy to mitigate the detrimental effects of the genetic algorithm's evolutionary operation in the late iterations, thereby improving its performance. This paper compares the performance of the standard GA, the standard WOA-GA algorithm, and the enhanced WOA-GA algorithm. The experimental results demonstrate that the enhanced WOA-GA algorithm can achieve optimal energy-efficient path planning using the hybrid strategy. The average energy consumption of the enhanced WOA-GA algorithm can be reduced by 30%, while the maximum energy consumption can be reduced by 47%. The results confirm the effectiveness and feasibility of the proposed method in enhancing the level of green manufacturing.