ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Multi-source Signals Guiding Swarm Robots Search
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Theoretical analysis of three bio-inspired plume tracking algorithms
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Multi-robot exploration and fire searching
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multi-robot based chemical plume tracing with virtual odor-source-probability sensor
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Robot algorithms for localization of multiple emission sources
ACM Computing Surveys (CSUR)
A learning particle swarm optimization algorithm for odor source localization
International Journal of Automation and Computing
Multi-robot olfactory search in structured environments
Robotics and Autonomous Systems
A fuzzified systematic adjustment of the robotic Darwinian PSO
Robotics and Autonomous Systems
International Journal of Swarm Intelligence Research
Autonomous Agents and Multi-Agent Systems
Robotics and Autonomous Systems
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This article presents a new algorithm for searching odour sources across large search spaces with groups of mobile robots. The proposed algorithm is inspired in the particle swarm optimization (PSO) method. In this method, the search space is sampled by dynamic particles that use their knowledge about the previous sampled space and share this knowledge with other neighbour searching particles allowing the emergence of efficient local searching behaviours. In this case, chemical searching cues about the potential existence of upwind odour sources are exchanged. By default, the agents tend to avoid each other, leading to the emergence of exploration behaviours when no chemical cue exists in the neighbourhood. This behaviour improves the global searching performance.The article explains the relevance of searching odour sources with autonomous agents and identifies the main difficulties for solving this problem. A major difficulty is related with the chaotic nature of the odour transport in the atmosphere due to turbulent phenomena. The characteristics of this problem are described in detail and a simulation framework for testing and analysing different odour searching algorithms was constructed. The proposed PSO-based searching algorithm and modified versions of gradient-based searching and biased random walk-based searching strategies were tested in different environmental conditions and the results, showing the effectiveness of the proposed strategy, were analysed and discussed.