一种针对月球飞行探测器的运动基元规划方法

Motion Planning for Lunar Flying Vehicle Using Motion Primitives

考虑先验地图缺失、障碍物分布未知的工作环境,提出一种融合运动基元、启发式和终端打靶的月球飞行探测器(LFV)最优动力学运动规划方法。首先对比分析了基于常量推力和常量加速度的基元生成模式。为降低约束核查耗时,提出应在加速度控制空间中设计基元。随后针对由时变非凸加速度控制空间导致的基元族难以快速生成的问题,设计了基于离散度的确定性均匀采样策略和有效采样点的筛选方法。其中采样策略可在提高基元对可达域覆盖率的同时最小化采样点数量;筛选方法通过建立基元的位置和速度极值表达式,将处理对象由整个基元转换为单个状态,从而减少约束核查数量。最后设计适用于LFV的启发式算法以降低算法的运行时间,设计层次化终端打靶策略以增加目标状态被连接到轨迹树的成功率。仿真结果表明,所提方法有效平衡了最优性和快速性之间的矛盾,保证了LFV的飞行安全。

A fast optimal kinodynamic motion planning method for LFV is proposed,taking into account the absence of prior map and unknown obstacle distribution.The method is based on motion primitives,heuristics and terminal shooting.Firstly,a comparison is made between primitive generation models based on constant thrust and constant acceleration.To reduce the time of constraint checking,it is suggested that primitives should be designed in the acceleration control space.In order to address the challenge of generating primitive families fastly due to the time-varying non-convex acceleration control space,a deterministic uniform sampling strategy based on dispersion and a screening method of effective sampling points are designed.This sampling strategy minimizes the number of sampling points while improving coverage of primitives within the reachable domain.By establishing extremum expressions for position and velocity of primitives,a filtering method converts the entire primitive into a single state,thereby reducing the number of constraint checks required.Finally,a heuristic method suitable for LFV is designed to reduce algorithm running time,and a hierarchical terminal shooting strategy is developed to increase success rate in connecting target state with trajectory tree.Simulation results demonstrate that this proposed method effectively balances optimality with fast computation while ensuring flight safety for LFV.