Title: Multi-objective task allocation for electric harvesting robots: a hierarchical route reconstruction approach Show Chinese title

Title: 多目标任务分配用于电动收获机器人：一种分层路径重构方法

Abstract: The increasing labor costs in agriculture have accelerated the adoption of multi-robot systems for orchard harvesting. However, efficiently coordinating these systems is challenging due to the complex interplay between makespan and energy consumption, particularly under practical constraints like load-dependent speed variations and battery limitations. This paper defines the multi-objective agricultural multi-electrical-robot task allocation (AMERTA) problem, which systematically incorporates these often-overlooked real-world constraints. To address this problem, we propose a hybrid hierarchical route reconstruction algorithm (HRRA) that integrates several innovative mechanisms, including a hierarchical encoding structure, a dual-phase initialization method, task sequence optimizers, and specialized route reconstruction operators. Extensive experiments on 45 test instances demonstrate HRRA's superior performance against seven state-of-the-art algorithms. Statistical analysis, including the Wilcoxon signed-rank and Friedman tests, empirically validates HRRA's competitiveness and its unique ability to explore previously inaccessible regions of the solution space. In general, this research contributes to the theoretical understanding of multi-robot coordination by offering a novel problem formulation and an effective algorithm, thereby also providing practical insights for agricultural automation. Show Chinese abstract

Abstract: 随着农业劳动力成本的增加，多机器人系统在果园采摘中的采用速度加快了。 然而，由于完成时间和能耗之间的复杂相互作用，尤其是在负载相关速度变化和电池限制等实际约束下，高效协调这些系统具有挑战性。 本文定义了多目标农业多电气机器人任务分配（AMERTA）问题，该问题系统地结合了这些常被忽视的实际约束。 为了解决这个问题，我们提出了一种混合分层路径重构算法（HRRA），该算法集成了几种创新机制，包括分层编码结构、双阶段初始化方法、任务序列优化器和专门的路径重构算子。 在45个测试实例上的大量实验表明，HRRA在七种最先进的算法中表现出色。 统计分析，包括Wilcoxon符号秩检验和Friedman检验，实证验证了HRRA的竞争性及其探索之前无法到达的解空间区域的独特能力。 总体而言，这项研究通过提供一种新的问题表述和一种有效的算法，促进了多机器人协调的理论理解，同时也为农业自动化提供了实用的见解。