Path planning of mobile robot based on Improved RRT Algorithm

In the path planning of mobile robot, aiming at the problem that the rapidly-exploring random tree (RRT) algorithm, which adopts the global uniform sampling strategy, leads to strong randomness and non-progressive optimization in the process of path growth, an improved RRT algorithm is proposed. Firstly, in the sampling stage, we establish the region sampling to select target points, and add gravitational component to solve the problem of RRT algorithm which is too random. Secondly, the obstacle expansion method is adopted to solve the limitation of long sampling time when the growing tree is close to the obstacle area. In view of the difficulty of the narrow-channel robot to pass, the ”Bridge Test” is introduced to guide the robot to walk. The improved algorithm effectively reduces the occupied space memory, running time and number of nodes, shortens the path length at the same time. By comparing the improved RRT algorithm with the basic RRT algorithm and the asymptotically optimal bi-directional rapidly-exploring random tree (B-RRT*) algorithm, the simulation results show that the improved RRT algorithm has shorter path and better time, and is more efficiently.