Abstract: 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*.

Key words: RRT,UAV,Navigation planning,Convergence speed,Lazy sampling