Abstract This paper addresses multi-robot task scheduling for two robot types arising from heterogeneous robotic order fulfillment systems. The heterogeneous multi-robot system comprises two types of robots with specialized and complementary capabilities to achieve long-cycle and multi-station order fulfillment tasks on a logistic network. This problem is extremely challenging because of innate complex-schedule constraints of tasks and coupled temporal–spatial relations between all robots. After set-theoretic and mixed integer linear programming problem formulations, we use coupled approach, instead of decoupled approaches to explore the synergy between heterogeneous robots, which is different from most existing similar works. To model the structural (complex-schedule) and quantitative (temporal–spatial) coupledness of robots’ time-extended task schedules, an edge-weighted and vertex-weighted block sequence graph is introduced. Based on this model, time-extended task scheduling is achieved using rank-minimal heuristic and genetic algorithm metaheuristic. Theoretically, this model is complete and non-redundant. Empirically, compared with decoupled approach, optimality and efficiency of the proposed methods are evaluated on designed instances. The results demonstrate that coupled methods can achieve near-optimal solutions with higher performance ratio than decoupled methods in moderate time. At the same time, coupled methods can leverage spatial and temporal properties of miscellaneous tasks, and balance instantaneous and time-extended decisions to achieve tight collective synergy in the long run.

中文翻译：

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执行复杂调度订单履行任务的两种机器人类型的异构多机器人系统的耦合任务调度

摘要 本文讨论了由异构机器人订单履行系统产生的两种机器人类型的多机器人任务调度。异构多机器人系统包括两种具有专门和互补能力的机器人，以在物流网络上实现长周期和多工位的订单履行任务。由于任务固有的复杂调度约束以及所有机器人之间的耦合时空关系，这个问题极具挑战性。在集合论和混合整数线性规划问题公式化之后，我们使用耦合方法而不是解耦方法来探索异构机器人之间的协同作用，这与大多数现有的类似工作不同。为了模拟机器人时间扩展任务计划的结构（复杂计划）和定量（时间-空间）耦合性，引入了边加权和顶点加权块序列图。基于该模型，采用秩最小启发式和遗传算法元启发式实现时延任务调度。从理论上讲，该模型是完整且非冗余的。从经验上看，与解耦方法相比，在设计实例上评估了所提出方法的最优性和效率。结果表明，耦合方法可以在适度的时间内以比解耦方法更高的性能比获得接近最优的解决方案。同时，耦合方法可以利用杂项任务的空间和时间特性，