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

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.

Submitted to ICCAS 2020

References in corpus (2)

• Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo
• Learnings Options End-to-End for Continuous Action Tasks

Cited by in corpus (5)

• Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas
• Machine-Learning-Based Classification of GPS Signal Reception Conditions Using a Dual-Polarized Antenna in Urban Areas
• Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning
• Performance Evaluation and Hybrid Application of the Greedy and Predictive UAV Trajectory Optimization Methods for Localizing a Target Mobile Device
• Indoor Path Planning for Multiple Unmanned Aerial Vehicles via Curriculum Learning