Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Coordination for Multi-Robot Exploration and Mapping
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Coordinated multi-robot exploration
IEEE Transactions on Robotics
Adaptive behavioral control of collaborative robots in hazardous environments
HSI'09 Proceedings of the 2nd conference on Human System Interactions
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The key to multi-robot exploration is how to select appropriate targets for robots to avoid collision and overlap. However, the distribution of targets for multiple robots is an NP hard problem. This paper presents a multi-robot cooperative exploration strategy based on the immune genetic algorithm. With its random global searching and parallel processing, genetic algorithm is applied for multi-robots multiple targets combinatorial distribution. With its antibody diversity maintaining mechanism, the immune algorithm is used to get over the premature convergence of genetic algorithm. The selection probability is computed based on the similarity vector distance to guarantee the antibody's diversity. The crossover and mutation probability are adjusted based on the fitness of antibody to decrease the possibility of local optimal. The extensive simulations demonstrate that the immunity-based adaptive genetic algorithm can effectively distribute the targets to multiple robots in various environments. The multiple robots can explore the unknown environment quickly.