Multi-robot systems have emerged as a key technology for addressing the efficiency and cost challenges in labor-intensive industries. In the representative scenario of smart farming, planning efficient harvesting schedules for a fleet of electric robots presents a highly challenging frontier problem. The complexity arises not only from the need to find Pareto-optimal solutions for the conflicting objectives of makespan and transportation cost, but also from the necessity to simultaneously manage payload constraints and finite battery capacity. When robot loads are dynamically updated during planned multi-trip operations, a mandatory recharge triggered by energy constraints introduces an unscheduled load reset. This interaction creates a complex cascading effect that disrupts the entire schedule and renders traditional optimization methods ineffective. To address this challenge, this paper proposes the segment anchoring-based balancing algorithm (SABA). The core of SABA lies in the organic combination of two synergistic mechanisms: the sequential anchoring and balancing mechanism, which leverages charging decisions as `anchors' to systematically reconstruct disrupted routes, while the proportional splitting-based rebalancing mechanism is responsible for the fine-grained balancing and tuning of the final solutions' makespans. Extensive comparative experiments, conducted on a real-world case study and a suite of benchmark instances, demonstrate that SABA comprehensively outperforms 6 state-of-the-art algorithms in terms of both solution convergence and diversity. This research provides a novel theoretical perspective and an effective solution for the multi-robot task allocation problem under energy constraints.

翻译：多机器人系统已成为应对劳动密集型产业效率与成本挑战的关键技术。在智慧农业这一代表性场景中，为电动机器人车队规划高效采收调度是一个极具挑战性的前沿问题。其复杂性不仅源于需要为完工时间与运输成本这两个冲突目标寻找帕累托最优解，还在于必须同时管理有效载荷约束与有限电池容量。当机器人在规划的多行程作业期间动态更新负载时，由能量约束触发的强制充电会引入计划外的负载重置。这种相互作用会产生复杂的级联效应，破坏整个调度计划并使传统优化方法失效。为应对这一挑战，本文提出基于分段锚定的平衡算法（SABA）。SABA的核心在于两个协同机制有机结合：顺序锚定与平衡机制利用充电决策作为“锚点”系统性地重构中断路径，而基于比例分割的再平衡机制负责对最终解的完工时间进行细粒度平衡与调优。通过在真实案例研究和一组基准实例上进行的大量对比实验表明，SABA在解收敛性和多样性方面全面优于6种先进算法。本研究为能量约束下的多机器人任务分配问题提供了新的理论视角和有效解决方案。