针对深海采矿车的遍历路径规划方法研究

Research on Traversal Path Planning Method for Deep-Sea Mining Vehicle

针对深海采矿车在海底复杂条件下周围环境信息缺少,实时处理数据能力低,环境建模困难复杂,难以自主和高效完成深海采矿作业等问题,提出一种深海采矿车遍历采矿作业的全覆盖路径规划方法。首先利用AUV(自主水下机器人)采集数据得到的水下DEM(数字高程模型)数据模型构建出海底的三维静态地图模型,根据得到的底质类型数据划分障碍物构建出二维静态栅格环境模型;然后考虑栅格状态、距离因素和转向因子建立启发函数,实现遍历路径采矿工作。结合实际采矿过程中出现的采矿死区问题,引入改进A*算法规划逃离出死区路径,保证了重复率尽可能小;最后通过MATLAB仿真验证。结果表明,该算法确保采矿覆盖率为100%的同时降低了重复率和转弯次数,提高了深海采矿车的工作效率和经济效益,为深海采矿车海底路径规划提供了理论基础。

There are lots of problems for deep-sea mining vehicles under complex seabed conditions, such as the lack of environmental information, the low real-time processing ability of data, the difficulty of environmental modeling, and the difficulty of autonomous and efficient completion of deep-sea mining operations. To solve these problems, a full-coverage path planning method for deep-sea mining vehicles traversing mining operations was proposed. Firstly, the underwater DEM(Digital Elevation Model) data model obtained from AUV(Autonomous Underwater Vehicle) data was used to construct the three-dimensional static map model of the seabed, and the two-dimensional static grid environment model was constructed by dividing obstacles according to the obtained sediment type data. Then, considering the grid state, distance factor and steering factor, the heuristic function was established to realize the traversing mining operations according to the path planning. Combined with the mining dead zone problem in the actual mining process, the improved A* algorithm was introduced to plan the escape path from the dead zone, and ensured that the repetition rate was as small as possible. Finally, it was verified by MATLAB simulation. The results show that the algorithm ensures the mining coverage rate of 100% while reducing the repetition rate and the number of turns, improves the work efficiency and economic benefits of the deep-sea mining vehicle, and provides a theoretical basis for the path planning of deep-sea mining vehicle.