Aiming at the problems of the traditional rapidly exploring random tree(RRT)algorithm in path planning of manipulator

such as high randomness

waste of resources caused by insufficient sampling differences in different environments

low planning efficiency and non-smooth planning path

a dynamic probability sampling RRT(DP-RRT) algorithm is proposed.Firstly

a dynamic probability sampling mechanism is introduced to reflect the changes in local obstacles during the environment by accumulating the number of collisions in node expansion

so as to adjust the sampling bias probability of target points in real time.Secondly

combined with a step-size adaptive strategy

the node step-size which is beneficial to the convergence of the algorithm is increased.Then

the sampling area of the algorithm is limited by extending the angle constraint.Finally

the trajectory is smoothed by a cubic B-spline curve and the simulation experiment of the algorithm is carried out in MATLAB and robot operating system(ROS).Experimental results show that the DP-RRT algorithm can effectively complete the obstacle-avoidance path planning task of manipulator in different environments

and it is superior to the traditional RRT algorithm in terms of planning time

path length and success rate.