Abstract
Coordinating the dynamics of large groups, or swarms, of autonomous underwater vehicles in order to search a given target area can be difficult due to the plurality of the system, environmental com- plications, and the prolonged and indefinite duration of the patrol. This paper examines the use of swarm inversion to optimize the behavioral dynamics of a swarm of autonomous agents in a patrol search with underwater morphological and environmental constraints. In partic- ular, the range of the forward-looking sensor range of agents varies spatially, requiring more search time in dark areas to maintain a high level of surveillance. This results in a tradeoff between the uniform coverage and surveillance frequency. The patrol fitness is determined via simulation feedback, and particle swarm optimization is used to invert and refine the behavior of the swarm. The tradeoffs between high search frequency and search uniformity are examined, as well as the evolved swarm's adaptability to varying environmental conditions and robustness of agent numbers. Results demonstrate that swarm inver- sion can yield effective agent behaviors for maintaining a presence in a given target zone despite stochastic navigation and an anisotropic environment.
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