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

arXiv: https://arxiv.org/abs/2009.11799 · HTML: https://mlsanity.com/paper/2009.11799/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds](https://mlsanity.com/paper/2502.18745/) (2025)
- [Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas](https://mlsanity.com/paper/2204.14001/) (2022)
- [UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment](https://mlsanity.com/paper/2304.05959/) (2023)
- [Zonal RL-RRT: Integrated RL-RRT Path Planning with Collision Probability and Zone Connectivity](https://mlsanity.com/paper/2410.24205/) (2024)

## Abstract (arXiv)

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.