A Dynamic Heterogeneous Team-based Non-iterative Approach for Online Pick-up and Just-In-Time Delivery Problems

TL;DR

This paper introduces a non-iterative, optimal resource allocation method for heterogeneous robots to efficiently handle online pickup and delivery tasks, validated through simulations and hardware tests.

Contribution

It adapts the DREAM approach to solve the heterogeneous PJITD problem with a non-iterative method that guarantees feasibility and optimal resource utilization.

Findings

Scalable approach demonstrated in simulations and hardware experiments.

Achieves optimal resource utilization for heterogeneous robot teams.

Validates effectiveness in real-time online PJITD tasks.

Abstract

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.