Dr. Pedro G. DeLima Profile

Dr. Pedro G. DeLima

Researcher at US Air Force Academy

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Proceedings Article | 2 May 2007 Paper

Optimizing a search strategy for multiple mobile agents

Pedro DeLima, Daniel Pack, John Sciortino

Proceedings Volume 6563, 65630B (2007) https://doi.org/10.1117/12.724967

KEYWORDS: Unmanned aerial vehicles, Sensors, Target detection, Detection and tracking algorithms, Genetic algorithms, Intelligence systems, Cameras, Algorithm development, Optimization (mathematics), Computer simulations

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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.

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