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.

本研究基于视觉 SLAM 进行了机器人路径规划的研究，主要工作包括构建基于 ORB-SLAM3 系统的密集点云地图、将点云地图转换成适用于二维路径规划的格网地图、以及基于强化学习的路径规划算法研究。实验验证了设计的视觉 SLAM 系统的可行性和有效性，并在相同实验条件下对三种强化学习算法进行了比较验证，得到了在实验条件下最优的算法。

基于强化学习的机器人路径规划研究

Research on Robot Path Planning Based on Reinforcement Learning

The scientific community is able to present a new set of solutions to
practical problems that substantially improve the performance of modern
technology in terms of efficiency and speed of computation due to the
advancement in neural networks architectures. We present the latest works
considering the utilization of neural networks in robot path planning. Our
survey shows the contrast between different formulations of the problems that
consider different inputs, outputs, and environments and how different neural
networks architectures are able to provide solutions to all of the presented
problems.

利用神经网络在机器人路径规划中的应用有不同的输入、输出和环境，不同的网络架构能够提供解决方案。