用双层蚁群算法同时优化多机器人任务分配与路径规划。
Bi-Layer Ant Colony Optimization for Multi-Robot Task Allocation and Routing in Delivery Applications
- 设计双层蚁群算法，分层协同优化任务分配与路径。
- 相比基线方法，总行程减少17.7%，完成时间缩短近20%。
- 适合大规模配送场景，对多机器人系统有高实用性。
本文针对多机器人任务分配（MRTA）问题，该问题在配送与物流中至关重要。提出一种新成本函数，将任务分配与路径规划统一为一个优化问题。引入双层蚁群优化（bi-layer ACO）算法，在单一蚁群过程中集成两个相互依赖的决策层，实现多机器人任务分配与路径规划的协同优化。与混合整数线性规划（MILP）和粒子群优化（PSO）的对比实验表明，所提方法在所有任务规模下均实现最短总行程和最快完成时间。具体而言，总行程最多降低17.7%，完成时间减少近20%。结果验证了该方法在多机器人配送任务中的高效性、可扩展性与可靠性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。