Research on Robot Path Planning Based on Reinforcement Learning
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
This project has conducted research on robot path planning based on Visual SLAM. The main work of this project is as follows: (1) Construction of Visual SLAM system. Research has been conducted on the basic architecture of Visual SLAM. A Visual SLAM system is developed based on ORB-SLAM3 system, which can conduct dense point cloud mapping. (2) The map suitable for two-dimensional path planning is obtained through map conversion. This part converts the dense point cloud map obtained by Visual SLAM system into an octomap and then performs projection transformation to the grid map. The map conversion converts the dense point cloud map containing a large amount of redundant map information into an extremely lightweight grid map suitable for path planning. (3) Research on path planning algorithm based on reinforcement learning. This project has conducted experimental comparisons between the Q-learning algorithm, the DQN algorithm, and the SARSA algorithm, and found that DQN is the algorithm with the fastest convergence and best performance in high-dimensional complex environments. This project has conducted experimental verification of the Visual SLAM system in a simulation environment. The experimental results obtained based on open-source dataset and self-made dataset prove the feasibility and effectiveness of the designed Visual SLAM system. At the same time, this project has also conducted comparative experiments on the three reinforcement learning algorithms under the same experimental condition to obtain the optimal algorithm under the experimental condition.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 41 references that are in the map. First posted on arXiv 22 Apr 2024.
Builds on
4 of 41 references.
References · 4
- 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
- 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 878
- 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 806
- 2017A Deeper Look at Experience Replaycited by 105
Built on by
Citing papers per year: 2024: 1.
1 paper.
- 2024A Survey on Reinforcement Learning Applications in SLAMcited by 3
- 2024A Survey on Reinforcement Learning Applications in SLAM26 Aug 2024
Similar, not cited
Close in wording, no citation either way
- 2024Research on Autonomous Robots Navigation based on Reinforcement Learning
- 2025MA-SLAM: Active SLAM in Large-Scale Unknown Environment using Map Aware Deep Reinforcement Learning
- 2024Multi-Object Navigation in real environments using hybrid policies
- 2024SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation
- 2024Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
- 2025The Emergence of Deep Reinforcement Learning for Path Planning
- 2025RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation
- 2025SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning