Bi-Layer Ant Colony Optimization for Multi-Robot Task Allocation and Routing in Delivery Applications

Abstract:This paper addresses the multi-robot task allocation (MRTA) problem, which is essential for delivery and logistics applications. Our approach first defines a new cost function that transforms the MRTA into a unified optimization problem capturing both task assignment and routing. A bi-layer ant colony optimization (ACO) algorithm is then introduced, integrating two interdependent decision layers within a single colony process to solve the problem. This hierarchical framework enables simultaneous optimization of task allocation and route planning across multiple robots. Comparative experiments with mixed-integer linear programming (MILP) and particle swarm optimization (PSO) demonstrate that the proposed bi-layer ACO achieves the shortest total travel distance and fastest completion time across all task sizes. Specifically, it reduces total travel distance by up to 17.7% and completion time by nearly 20% compared with baseline methods. These results confirm the efficiency, scalability, and reliability of the proposed bi-layer ACO for multi-robot delivery tasks.

6 pages. Accepted at 2026 11th International Conference on Intelligent Information Technology (ICIIT 2026)

Robotics (cs.RO)

arXiv:2608.17416 [cs.RO]

(or arXiv:2608.17416v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.17416 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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