快速平滑收敛策略下基于QS-RRT的UAV运动规划

QS-RRT based motion planning for unmanned aerial vehicles using quick and smooth convergence strategies

基于快速扩展随机树(rapidly exploring random tree,RRT)的运动规划算法,通过随机采样的方式探索未知任务空间,具有概率完备性和较高的计算效率.该类算法在应用于无人机运动规划时必须对飞行距离、过程安全性和航路平滑度进一步优化.针对这一问题,首先对威胁环境、无人机运动学性能和探测能力建模,然后根据飞行特征设计了随机采样、威胁规避、路径可跟踪性以及全局与局部平滑性等优化策略,并构建快速平滑收敛RRT(quick and smooth convergence RRT,QS-RRT),最后以此为基础分别提出了面向已知和未知任务空间的无人机运动规划算法.仿真结果表明,该算法能够在保证飞行路径收敛性、安全性及其规划效率的基础上,有效缩短飞行距离,改善航路的可跟踪性和平滑度,增强在实际飞行过程中的可操作性.此外,该算法还易于在航路优化效果和规划效率之间权衡,增强了对不同规划任务需求的适应性.

Rapidly exploring random tree （RRT） based motion planning algorithm constructs collision-free paths by biasing the exploration toward the unexplored task space with a random sampling scheme. This algorithm is probabilistically complete and computationally efficient. However, the length, safety and smoothness of the generated path must be improved in motion planning applications for unmanned aerial vehicles （UAVs）. This paper models the threat environment, the UAV＇s maneuverability and sensory ability, and then designs several optimal strategies with respect to sampling, obstacle avoidance, path navigability and path smoothing globally and locally. Consequently, a quick and smooth convergence RRT （QS-RRT） is obtained, and two improved optimal motion planning algorithms are presented for both known and unknown task spaces. Simulation results show that the algorithms can not only guarantee the convergence and path safety, but more importantly shorten the flight distance and remarkably improve the path navigability and smoothness. Furthermore~ the algorithms can trade-off between the optimal degree and computational efficiency, which will optimize the adaptability to different practical mission requirements.