Multi-Robot Task Allocation Based on Swarm Intelligence

Abstract

Multi-Robot Systems, Trends and Development 394 (OAP). ST-MR-IA often appears in real world applications; that is, some tasks require the combined effort of multiple robots. These two types of tasks are also called loosely-coupled tasks and tightly-coupled tasks, respectively. Although some approaches for solving either loosely-coupled task or tightly-coupled task allocation have been proposed, few approaches for solving both loosely-coupled and tightly-coupled task allocation have been developed. In this chapter, we present a task allocation mechanism based on swarm intelligence for the large-scale multi-robot system, with both loosely-coupled and tightly-coupled task allocation. The mechanism adopts a hierarchical architecture. At the high level, we employ an Ant Colony Algorithm to find optimal allocations. Namely, each ant performs a task allocation so as to choose an undertaker for every task. At the low level, each ant forms a task-oriented robot coalition to perform a tightly-coupled task. Ant colony optimization (ACO), the particle swarm and ant colony optimization (PSACO) and the quantum-inspired ant colony optimization (QACO) are adopted to form the coalition. Finally, the algorithm is implemented in the TeamBots simulation platform. Simulation results show that the proposed mechanism can effectively solve loosely-coupled and tightly-coupled task allocation in the large-scale multi-robot system.