针对滩涂履带车在受潮汐影响的滩涂环境中进行长时间勘测作业的需求,提出柯西贝塞尔快速搜索随机树星(Cauchy Bessel Rapidly-exploring Random Tree Star,CB-RRT*)算法进行路径规划.为规划出安全路径,基于全局地图和潮汐数据,并通过滩涂履带车到分界区的距离构建出滩涂预测模型;为提高滩涂履带车移动到目标点需进行多次路径规划的速度,对初始路径的关键树节点使用柯西概率密度函数进行采样缩小采样范围来提高节点的利用率,进而提高算法的收敛性;在重选父节点过程中考虑最大转角约束设定相应系数,并使用连续二次贝塞尔曲线进行拼接的方式来生成路径,达到提高路径平滑度的目的和解决平滑后路径与原路径偏差过大造成的安全性问题.仿真实验结果表明,CB-RRT*算法在静态滩涂环境和动态滩涂环境中,能大大提高算法的收敛性和路径的平滑性,且保证路径长度最优,研究内容可以保证滩涂履带车在各种滩涂环境中进行长时间安全作业.
Abstract
Cauchy Bessel rapidly-exploring random tree star ( CB-RRT*) algorithm is proposed for the path planning of tracked vehicle for long-term surveying operations in tidal flat environments. In order to plan a safe path,a tidal flat prediction model is constructed based on the global map. The tidal data and the distance from the tidal flat tracked vehicle to the demarcation area. Using the Cauchy probability density function to sample the key tree nodes of the initial path can reduce the sampling range and improve the utilization of nodes,which increases the speed of multiple path planning of tidal flat tracked vehicle moving to a target point. Considering the maximum corner constraint to set the corresponding coefficient in the process of reselecting the parent node and using the continuous quadratic Bezier curve to splicing to generate a path,the smoothness of the path can be improved and the safety problem caused by the excessive deviation between the smoothed path and the original path can be also solved. The simulated results show that CB-RRT* algorithm can greatly improve the convergence of the algorithm and the smoothness of the path in the static and dynamic tidal flat environments,and ensures the optimal path length. The proposed method can ensure the long-term safe operation of tidal flat tracked vehicle in various tidal flat environments.
关键词
滩涂履带车/路径规划/柯西概率密度函数/最大转角约束/贝塞尔曲线
Key words
tidal flat tracked vehicle/path planning/Cauchy probability density function/maximum corner constraint/Bezier curve
万方数据