スマート果樹園における複数回収穫ロボットのタスクスケジューリングのための適応経験ベース離散遺伝的アルゴリズム
An adaptive experience-based discrete genetic algorithm for multi-trip picking robot task scheduling in smart orchards
果樹園での複数ロボットによる収穫タスクのスケジューリング問題に対し、負荷分散初期化・クラスタリング局所探索・経験適応選択を組み込んだ遺伝的アルゴリズムを提案し、効率的な解探索を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Peng Chen, Jing Liangb, Kang-Jia Qiao, Hui Song, Cai-Tong Yue, Kun-Jie Yu, Ponnuthurai Nagaratnam Suganthan, Witold Pedrycz
分類: cs.RO
原文アブストラクト
The continuous innovation of smart robotic technologies is driving the development of smart orchards, significantly enhancing the potential for automated harvesting systems. While multi-robot systems offer promising solutions to address labor shortages and rising costs, the efficient scheduling of these systems presents complex optimization challenges. This research investigates the multi-trip picking robot task scheduling (MTPRTS) problem. The problem is characterized by its provision for robot redeployment while maintaining strict adherence to makespan constraints, and encompasses the interdependencies among robot weight, robot load, and energy consumption, thus introducing substantial computational challenges that demand sophisticated optimization algorithms.To effectively tackle this complexity, metaheuristic approaches, which often utilize local search mechanisms, are widely employed. Despite the critical role of local search in vehicle routing problems, most existing algorithms are hampered by redundant local operations, leading to slower search processes and higher risks of local optima, particularly in large-scale scenarios. To overcome these limitations, we propose an adaptive experience-based discrete genetic algorithm (AEDGA) that introduces three key innovations: (1) integrated load-distance balancing initialization method, (2) a clustering-based local search mechanism, and (3) an experience-based adaptive selection strategy. To ensure solution feasibility under makespan constraints, we develop a solution repair strategy implemented through three distinct frameworks. Comprehensive experiments on 18 proposed test instances and 24 existing test problems demonstrate that AEDGA significantly outperforms eight state-of-the-art algorithms.
