基于MB-RRT*的无人机航迹规划算法研究

摘要

随着小型无人机的广泛应用,提高无人机的自动巡航能力变得至关重要.无人机航迹规划是指其在已知环境地图信息下展开航迹规划,实现无碰撞的、平滑的、从初始点到达目标点的路径.针对现有算法依然存在收敛速度慢、内存消耗大、航迹规划固定步长和航迹平滑度无法满足实际无人机飞行等问题,提出了MB-RRT* (Modified B-RRT*)算法,通过懒惰采样方法加快算法收敛速度并减少内存占用;设计自适应步长来解决算法在障碍物附近生长树的局限性问题,从而提高了找到初始可行解的速度和质量;然后利用降采样和3次贝塞尔插值算法实现了曲线拟合的功能,使算法最终生成相对平滑的航迹,为无人机实际飞行提供可行的航迹规划方法.最后在多组不同环境复杂度的实验中,通过与其他算法相比较,验证了所提算法的有效性.%With the wide application of unmanned aerial vehicle(UAV),it is important to improve the capacity of automatic navigation.Navigation for UAV is the algorithm that can automatically find out the obstacle-free,smoothing path from start position to target position.Most current navigation algorithms for UAV still have shortcomings including low convergence speed,large memory cost,fixed navigation step setting and smoothing challenges.MB-RRT* algorithm was proposed in this paper which has three outstanding strategies for UAV.Lazy sampling was adopted to improve convergence speed and achieve less memory cost.Self-adaptive step length algorithm was applied to solve navigation limitation near obstacles and improve initial solutions' quality and speed.Down sampling and curve fitting were introduced to improve the convergence rate of the algorithm and the smoothness of the final path.Finally abundant simulations were carried out to testify the high performances of MB-RRT* compared with RRT* and BRRT*.