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A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm
| Content Provider | MDPI |
|---|---|
| Author | Wang, Hao Li, Guoqing Hou, Jie Chen, Lianyun Hu, Nailian |
| Copyright Year | 2022 |
| Description | Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency. |
| Starting Page | 294 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics11030294 |
| Journal | Electronics |
| Issue Number | 3 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-18 |
| Access Restriction | Open |
| Subject Keyword | Electronics Industrial Engineering Transportation Science and Technology Underground Intelligent Vehicles Path Planning Rrt* Algorithm Articulated Vehicles Unmanned Driving |
| Content Type | Text |
| Resource Type | Article |
