Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations
Robotics and Autonomous Systems
Physical parameter optimization in swarms of ultra-low complexity agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
What a cognitive radio network could learn from a school of fish
WICON '07 Proceedings of the 3rd international conference on Wireless internet
Robot algorithms for localization of multiple emission sources
ACM Computing Surveys (CSUR)
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
PSO and ACO in optimization problems
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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In this paper, we provide a biasing expansion swarm approach (BESA) for using multiple simple mobile agents, with limited sensing and communication capabilities, to collaboratively search and locate an indeterminate number of emission sources in an unknown large-scale area. The key concept in this approach is swarm behavior. By applying the three properties of the swarm behavior: separation, cohesion and alignment, our approach can ensure the agent group attains dynamically stable ad-hoc connectivity and fast target convergence. Using a grid map to represent the unknown environment, an ad-hoc network for wireless communication and our biasing expansion algorithm for path planning, each agent simultaneously considers all concentration values collected by other swarm members and determines the positive gradient direction of the whole coverage area of the swarm. This will make the swarm immune to the random sensor errors, local aerosol accumulations and other local interference effects during their search. We present a simulated environment that has multiple emission sources and complex aerosol accumulation and distribution. Based on the simulation, our approach can achieve better performance than the gradient descent approach, which currently appears to be the most popular algorithm for emission source localization.