Optimal task scheduler design using the underwater risk assessment

In this paper, we propose a multi-task scheduler by considering the underwater risk assessment for autonomous underwater vehicle. We divide the task scheduler into two parts: a path coordinator and a condition interpreter. The path coordinator finds the minimum cost sequence from several combinations of paths by using a genetic algorithm. The condition interpreter limits high risk paths such as obstacle environment and strong tidal current. By considering both the path coordinator and the condition interpreter, we get an optimal path for a given condition, and this algorithm is verified through simulations.