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Proceedings Paper

Optimizing a search strategy for multiple mobile agents
Author(s): Pedro DeLima; Daniel Pack; John C. Sciortino
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Paper Abstract

In this paper, we propose a rule-based search method for multiple mobile distributed agents to cooperatively search an area for mobile target detection. The collective goals of the agents are (1) to maximize the coverage of a search area without explicit coordination among the members of the group, (2) to achieve suffcient minimum coverage of a search area in as little time as possible, and (3) to decrease the predictability of the search pattern of each agent. We assume that the search space contains multiple mobile targets and each agent is equipped with a non-gimbaled visual sensor and a range-limited radio frequency sensor. We envision the proposed search method to be applicable to cooperative mobile robots, Unmanned Aerial Vehicles (UAVs), and Unmanned Underwater Vehicles (UUVs). The search rules used by each agent characterize a decentralized search algorithm where the mobility decision of an agent at each time increment is independently made as a function of the direction of the previous motion of the agent, the known locations of other agents, the distance of the agent from the boundaries of the search area, and the agent's knowledge of the area already covered by the group. Weights and parameters of the proposed decentralized search algorithm are tuned to particular scenarios and goals using a genetic algorithm. We demonstrate the effectiveness of the proposed search method in multiple scenarios with varying numbers of agents. Furthermore, we use the results of the tuning processes for different scenarios to draw conclusions on the role each weight and parameter plays during the execution of a mission.

Paper Details

Date Published: 2 May 2007
PDF: 8 pages
Proc. SPIE 6563, Evolutionary and Bio-inspired Computation: Theory and Applications, 65630B (2 May 2007); doi: 10.1117/12.724967
Show Author Affiliations
Pedro DeLima, Air Force Academy (United States)
Daniel Pack, Air Force Academy (United States)
John C. Sciortino, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 6563:
Evolutionary and Bio-inspired Computation: Theory and Applications
Misty Blowers; Alex F. Sisti, Editor(s)

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