UAV Path Planning and Obstacle Avoidance Based on Reinforcement Learning in 3D Environments

Abstract

This study proposes using unmanned aerial vehicles (UAVs) to carry out tasks involving path planning and obstacle avoidance, and to explore how to improve work efficiency and ensure the flight safety of drones. One of the applications under consideration is aquaculture cage detection; the net-cages used in sea-farming are usually numerous and are scattered widely over the sea. It is necessary to save energy consumption so that the drones can complete all cage detections and return to their base on land. In recent years, the application of reinforcement learning has become more and more extensive. In this study, the proposed method is mainly based on the Q-learning algorithm to enable improvements to path planning, and we compare it with a well-known reinforcement learning state–action–reward–state–action (SARSA) algorithm. For the obstacle avoidance control procedure, the same reinforcement learning method is used for training in the AirSim virtual environment; the parameters are changed, and the training results are compared.