This paper presents a non-iterative approach for finding the assignment of heterogeneous robots to efficiently execute online Pickup and Just-In-Time Delivery (PJITD) tasks with optimal resource utilization. The PJITD assignments problem is formulated as a spatio-temporal multi-task assignment (STMTA) problem. The physical constraints on the map and vehicle dynamics are incorporated in the cost formulation. The linear sum assignment problem is formulated for the heterogeneous STMTA problem. The recently proposed Dynamic Resource Allocation with Multi-task assignments (DREAM) approach has been modified to solve the heterogeneous PJITD problem. At the start, it computes the minimum number of robots required (with their types) to execute given heterogeneous PJITD tasks. These required robots are added to the team to guarantee the feasibility of all PJITD tasks. Then robots in an updated team are assigned to execute the PJITD tasks while minimizing the total cost for the team to execute all PJITD tasks. The performance of the proposed non-iterative approach has been validated using high-fidelity software-in-loop simulations and hardware experiments. The simulations and experimental results clearly indicate that the proposed approach is scalable and provides optimal resource utilization.

翻译：本文提出了一种非迭代方法，用于分配异构机器人以高效执行在线取货与即时配送（PJITD）任务，并实现资源利用最优。PJITD分配问题被形式化为时空多任务分配（STMTA）问题。成本公式中纳入了地图和车辆动力学相关的物理约束。针对异构STMTA问题，构建了线性求和分配问题。最近提出的基于多任务分配的动态资源分配（DREAM）方法经过改进，用于解决异构PJITD问题。该方法首先计算执行给定异构PJITD任务所需的最少机器人数量（含类型），将这些必需机器人加入团队以确保所有PJITD任务的可行性。随后，更新后的团队中的机器人被分配执行PJITD任务，同时最小化整个团队执行所有PJITD任务的总成本。通过高保真软件在环仿真与硬件实验验证了所提非迭代方法的性能。仿真与实验结果表明，该方法具有良好的可扩展性，并能实现资源利用最优。