Swarm intelligence based WSN-mediated distributed multi-robot task allocation

To solve the multi-robot task allocation (MRTA) especially in unknown complex environment, a novel dynamic algorithm was put forward with the advantages of wireless sensor network (WSN). With a powerful abstraction, a formal way of task representation was developed integrating four types of tasks. The architecture and sensor fusion of WSN were discussed. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Adaptive pheromone updating strategy and deadlock elimination are included. A practical implementation with real WSN and real mobile robots were carried out. The successful implementation of tasks validates the efficiency, stability and accuracy of the proposed algorithm. The convergence curve shows that as iterative generation grows, the utility increases and finally reaches a stable and optimal value, and that using sensor information fusion can greatly improve the efficiency. The algorithm is proved better than other tradition algorithms without WSN for MRTA in real time.