Memetic Mission Management
Abstract
Many operations require an area search function, including search-and-rescue, surveillance, hazard detection, structure or site inspection and agricultural spraying. Furthermore, these area search applications often involve varying vehicle and environmental conditions. This article explores using memetic computing to optimize the behavior of a swarm of heterogeneous robotic vehicles executing a search area coverage task. Each vehicle is equipped with a sensing apparatus, and the swarm must collectively explore an occluded environment to achieve a required probability of detection for each location in the search area. The problem is further complicated with the introduction of dynamic vehicle and environmental properties, making adaptability a necessary requirement in order to achieve a high level of mission assurance using unmanned vehicles. Memetic computing is well suited for this type of application, as it can provide rapid, high-quality solutions for complex problems. New memetic methods for search area decomposition, task allocation and path planning are presented, with simulated and real-world results utilizing the Boeing Vehicle Swarm Technology Laboratory.
Recommended Citation
R. J. Meuth et al., "Memetic Mission Management," IEEE Computational Intelligence Magazine, vol. 5, no. 2, pp. 32 - 40, Institute of Electrical and Electronics Engineers (IEEE), Jan 2010.
The definitive version is available at https://doi.org/10.1109/MCI.2010.936310
Department(s)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
1556-603X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2010 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2010
