Autonomous Underwater Vehicles (AUVs) are currently being used by the Navy for mine countermeasures. AUVs include both submarine and tracked crawlers. Recent search strategies have been implemented using both submarines and crawlers; submarines to sweep large areas to detect possible mines, and crawler to re-acquire the possible mines and perform classification. The primary scope of this paper is the control strategies for the crawlers to best cover an area. Both a motion controller and a mine reacquisition scheduling system were developed. Simulations were performed using Autonomous Littoral Warfare Systems Evaluator - Monte Carlo (ALWSEMC) to complete studies on optimal crawler control strategies. These simulations included 1 submarine and 3 crawlers. Two reacquisition scheduling systems were compared, one using a closest target strategy, and one using fuzzy logic that used additional information available to the crawler to best utilize time and resources. It was found that a fuzzy logic scheduling system outperformed the baseline system by reducing the amount of time to reacquire all targets.

Current underwater crawling vehicles could benefit by using rotating head sonar data to avoid collisions with obstacles. We have developed and optimized a fuzzy logic controller using software for simulation of an underwater environment. The optimization results show near an order of magnitude increase in performance over both straight line and lawnmower search patterns with relatively small changes in the system parameters. The fuzzy logic controller has the capability of navigating a crawler safely and quickly between mission specific points.

A non-linear, fuzzy logic controller was developed for an autonomous underwater crawler. Due to fuzzy rules based on linguistic variables, the controller is applicable to many autonomous applications.The controller is hierarchical in design with an obstacle avoidance, a path finding, and a supervisor model. An optimization procedure was developed using an algorithm based on the simplex method and simulations done in an autonomous vehicle simulator. Vehicle performance was quantified using a performance function designed to penalize a vehicle for colliding with obstacles and deviating from a straight line path. Optimization was performed using two different methods to determine the optimal numeric values to the linguistic variables. Both methods resulted in enhanced vehicle performance.

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