IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Computer Vision
Exploring Unknown Structured Environments
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Common subgraph isomorphism detection by backtracking search
Software—Practice & Experience
International Journal of Robotics Research
Particle swarm-based olfactory guided search
Autonomous Robots
Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations
Robotics and Autonomous Systems
Robotics and Autonomous Systems
Multi-robot exploration and fire searching
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Coordinated multi-robot exploration
IEEE Transactions on Robotics
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Robotic clusters: Multi-robot systems as computer clusters
Robotics and Autonomous Systems
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This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots. While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for the exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.