Autonomous and remotely operated underwater vehicles allow us to reach places which have previously been inaccessible and perform complex repair, exploration and analysis tasks. As their navigation is not infallible, they may cause severe damage to themselves and their often fragile surroundings, such as flooded caves, coral reefs, or even accompanying divers in case of a collision. In this study, we used a shallow neural network, consisting of interlinking PID controllers, and trained by a genetic algorithm, to control a biologically inspired AUV with a soft and compliant exoskeleton. Such a compliant structure is a versatile and passive solution which reduces the accelerations induced by collisions to 56% of the original mean value acting upon the system, thus, notably reducing the stress on its components and resulting reaction forces on its surroundings. The segmented structure of this spherical exoskeleton protects the encased system without limiting the use of cameras, sensors or manipulators.
Keywords: cave diving; compliant structure; exoskeleton; genetic algorithm; shock absorbability.
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