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Ahmadzadeh, SR; Leonetti, M; Carrera, A; Carreras, M; Kormushev, P; Caldwell, DG (2014)
Publisher: IEEE
Languages: English
Types: Other
We investigate methods to improve fault-tolerance of Autonomous Underwater Vehicles (AUVs) to increase their reliability and persistent autonomy. We propose a learning-based approach that is able to discover new control policies to overcome thruster failures as they happen. The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the AUV. The model is adapted to a new condition when a fault is detected and isolated. Since the approach generates an optimal trajectory, the learned fault-tolerant policy is able to navigate the AUV towards a specified target with minimum cost. Finally, the learned policy is executed on the real robot in a closed-loop using the state feedback of the AUV. Unlike most existing methods which rely on the redundancy of thrusters, our approach is also applicable when the AUV becomes under-actuated in the presence of a fault. To validate the feasibility and efficiency of the presented approach, we evaluate it with three learning algorithms and three policy representations with increasing complexity. The proposed method is tested on a real AUV, Girona500.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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