Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This work presents the application of particle swarm optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches.

Meeting Name

Congress on Evolutionary Computation, 2004. CEC2004


Electrical and Computer Engineering

Keywords and Phrases

Collective Robotic Search Applications; Mobile Robots; Optimal PSO; Optimisation; Particle Swarm Optimization; Quality Factors; Remotely Operated Vehicles; Search Problems; Target Search; Target Tracing Applications; Unmanned Vehicles

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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