Abstract
Planning a reasonable driving path for trucks in mining areas is a key point to improve mining efficiency. In this paper, a path planning method based on Rapidly-exploring Random Tree Star (RRT*) is proposed, and several optimizations are carried out in the algorithm. Firstly, the selection process of growth target points is optimized. Secondly, the process of selecting the parent node is optimized and a Dubins curve is used to constraint it. Then, the expansion process from tree node to random point is optimized by the gravitational repulsion field method and dynamic step method. In the obstacle detection process, Dubins curve constraint is used, and the bidirectional RRT* algorithm is adopted to speed up the iteration of the algorithm. After that, the obtained paths are smoothed by using the greedy algorithm and cubic B-spline interpolation. In addition, to verify the superiority and correctness of the algorithm, an unmanned mining vehicle kinematic model in the form of front-wheel steering is developed based on the Ackermann steering principle and simulated for CoppeliaSim. In the simulation, the Stanley algorithm is used for path tracking and Reeds-Shepp curve to adjust the final parking attitude of the truck. Finally, the experimental comparison shows that the improved bidirectional RRT* algorithm performs well in the simulation experiment, and outperforms the common RRT* algorithm in various aspects.
Highlights
Mineral resources, as the most important raw materials for industrial production, have become a top priority for research in various countries on how to mine them efficiently and intelligently
4 Conclusion In this paper, an improved bidirectional Rapidlyexploring Random Tree (RRT)∗ algorithm is proposed, which is applied to the path planning of mining trucks
Compared with the basic RRT∗ algorithm, the algorithm used in this paper mainly uses bidirectional RRT∗ and optimizes the path solving process of RRT∗
Summary
As the most important raw materials for industrial production, have become a top priority for research in various countries on how to mine them efficiently and intelligently. The research on establishing and improving the kinematics model supported by the corresponding path planning algorithm mainly belongs to the field of robotics, and in field of vehicle simulation, most studies only uses simple differential rotation models or only simple geometry instead, and the structure of such models is low in difficulty, the gap with the models of real vehicles is large. To this extent, the study lacks practical guidance. The greedy algorithm and cubic B-spline interpolation are used to smooth the path to reduce the length of the path respectively
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