Reinforcement Learning Based Obstacle Avoidance for Autonomous Underwater Vehicle

  • Prashant BhopaleEmail author
  • Faruk Kazi
  • Navdeep Singh
Research Article


Obstacle avoidance becomes a very challenging task for an autonomous underwater vehicle (AUV) in an unknown underwater environment during exploration process. Successful control in such case may be achieved using the model-based classical control techniques like PID and MPC but it required an accurate mathematical model of AUV and may fail due to parametric uncertainties, disturbance, or plant model mismatch. On the other hand, model-free reinforcement learning (RL) algorithm can be designed using actual behavior of AUV plant in an unknown environment and the learned control may not get affected by model uncertainties like a classical control approach. Unlike model-based control model-free RL based controller does not require to manually tune controller with the changing environment. A standard RL based one-step Q-learning based control can be utilized for obstacle avoidance but it has tendency to explore all possible actions at given state which may increase number of collision. Hence a modified Q-learning based control approach is proposed to deal with these problems in unknown environment. Furthermore, function approximation is utilized using neural network (NN) to overcome the continuous states and large state-space problems which arise in RL-based controller design. The proposed modified Q-learning algorithm is validated using MATLAB simulations by comparing it with standard Q-learning algorithm for single obstacle avoidance. Also, the same algorithm is utilized to deal with multiple obstacle avoidance problems.


Obstacle avoidance Autonomous underwater vehicle Reinforcement learning Q-learning Function approximation 



The authors would like to acknowledge the support of Centre of Excellence (CoE) in Complex and Nonlinear dynamical system (CNDS), through TEQIP-II, VJTI, Mumbai, India.


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Copyright information

© Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentVeermata Jijabai Technological InstituteMumbaiIndia

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