面向配送应用的双层蚁群优化算法（Bi-Layer Ant Colony Optimization），用于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
浏览论文内容
中文总结 AI 辅助
针对配送应用的多机器人任务分配与路径规划问题，提出双层蚁群优化算法，实验表明其在总行驶距离和完成时间上优于MILP、PSO等基线方法，性能更优。
中文摘要 AI 辅助
本文研究多机器人任务分配（MRTA）问题，该问题对配送与物流应用至关重要。我们的方法首先定义了一种新的代价函数，将MRTA转化为统一的优化问题，同时涵盖任务分配与路径规划。随后提出了双层蚁群优化（ACO）算法，在单个蚁群流程中整合两个相互依赖的决策层以求解该问题。该分层框架可实现多机器人间任务分配与路径规划的同步优化。通过与混合整数线性规划（MILP）和粒子群优化（PSO）的对比实验表明，所提双层ACO在所有任务规模下均能实现最短总行驶距离与最快完成时间。具体而言，与基线方法相比，它最多可减少17.7%的总行驶距离，缩短近20%的完成时间。这些结果证实了所提双层ACO在多机器人配送任务中的高效性、可扩展性与可靠性。
英文摘要
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.
发表机构
- Fulbright University Vietnam（富布赖特越南大学）
- College of Engineering and Computer Science, VinUniversity（vinuniversity工程与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。
