We study the combined problem of online task assignment and lifelong path finding, which is crucial for the logistics industries. However, most literature either (1) focuses on lifelong path finding assuming a given task assigner, or (2) studies the offline version of this problem where tasks are known in advance. We argue that, to maximize the system throughput, the online version that integrates these two components should be tackled directly. To this end, we introduce a formal framework of the combined problem and its solution concept. Then, we design a rule-based lifelong planner under a practical robot model that works well even in environments with severe local congestion. Upon that, we automate the search for the task assigner with respect to the underlying path planner. Simulation experiments conducted in warehouse scenarios at Meituan, one of the largest shopping platforms in China, demonstrate that (a)in terms of time efficiency, our system requires only 83.77% of the execution time needed for the currently deployed system at Meituan, outperforming other SOTA algorithms by 8.09%; (b)in terms of economic efficiency, ours can achieve the same throughput with only 60% of the agents currently in use. The code and demos are available at https://github.com/Fernadoo/Online-TAPF.

翻译：本研究探讨了在线任务分配与终身路径规划的联合问题，该问题对物流行业至关重要。然而，现有文献大多（1）在假设任务分配器给定的前提下专注于终身路径规划，或（2）研究该问题的离线版本，即任务预先已知。我们认为，为最大化系统吞吐量，应直接处理集成这两个组件的在线版本。为此，我们提出了该联合问题的形式化框架及其求解概念。随后，我们在一个实用的机器人模型下设计了一种基于规则的终身规划器，该规划器即使在局部拥堵严重的环境中也能良好运行。在此基础上，我们针对底层路径规划器实现了任务分配器的自动化搜索。在中国最大购物平台之一的美团仓库场景中进行的仿真实验表明：（a）在时间效率方面，我们的系统仅需美团当前部署系统执行时间的83.77%，优于其他SOTA算法8.09%；（b）在经济效率方面，我们的系统仅需当前使用智能体数量的60%即可实现相同吞吐量。代码与演示可在 https://github.com/Fernadoo/Online-TAPF 获取。