Unmanned underwater vehicles (UUVs) have become an integral part in helping humans do underwater explorations more efficiently and safely since these vehicles can stay underwater much longer than any human can possibly do and they require little or almost no human interaction. These vehicles are subject to dynamic and unpredictable nature of the underwater environment resulting to complexities in their navigation. This paper proposes a fuzzy logic-based controller to allow the vehicle to navigate autonomously while avoiding obstacles. The said controller is implemented in an actual lowcost underwater vehicle equipped with magnetometer and ultrasonic sensors. The intelligence of the UUV includes a two fuzzy logic block, namely Motion Control block and Heading Correction block. The fuzzy logic controller takes in target positions in X, Y and Z axes. Also, the heading error and rate of heading error are included as inputs in order to correct the bearing or direction of the vehicle. A heuristic and integration stage is also included after these fuzzy logic blocks for vehicle's collision avoidance. The controller output parameters are the adjusted thrusters' speeds which dictate the six thrusters speed and direction. With the proper output commands from this controller, the vehicle is able to navigate in its predefined destination.
Unmanned underwater vehicles (UUVs) are mostly used for safe underwater explorations and researches. UUVs are subject to different parameters that changes over time. Such parameters are not considered in kinematic modelling of vehicles. As such, a dynamic modelling of underwater vehicles is necessary. This study proposes a dynamic model that is utilizing Artificial Neural Network (ANN), for a 5-thruster underwater vehicle design. The training data for the ANN model is gathered by empirical methods. The dynamic model is represented by UUV variables: thrusters input voltages and resulting velocity vector. The results of the neural network showed accuracy and reliability due to the low Mean Square Error (MSE) and satisfactory regression plots.
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