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

2026-08-18

AI Summary

本文针对多机器人任务分配问题，通过定义新的成本函数和使用双层蚁群优化算法，实现了任务分配与路径规划的同时优化，提高了效率。

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.

Problem

• multi-robot task allocation
• delivery applications
• task assignment
• routing

Innovation

• bi-layer ant colony optimization
• multi-robot task allocation
• routing
• cost function
• hierarchical framework