Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles

• URL: http://arxiv.org/abs/2009.11799v1
• Date: Thu, 24 Sep 2020 16:42:56 GMT
• Title: Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles
• Authors: Sanghyun Kim, Jongmin Park, Jae-Kwan Yun, and Jiwon Seo
• Abstract summary: We applied reinforcement learning to perform motion planning for an unmanned aerial vehicle (UAV) in an open space with static obstacles. As the reinforcement learning progressed, the mean reward and goal rate of the model were increased.
• Score: 3.5356468463540214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we applied reinforcement learning based on the proximal policy optimization algorithm to perform motion planning for an unmanned aerial vehicle (UAV) in an open space with static obstacles. The application of reinforcement learning through a real UAV has several limitations such as time and cost; thus, we used the Gazebo simulator to train a virtual quadrotor UAV in a virtual environment. As the reinforcement learning progressed, the mean reward and goal rate of the model were increased. Furthermore, the test of the trained model shows that the UAV reaches the goal with an 81% goal rate using the simple reward function suggested in this work.

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