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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