Improved RRT-Connect Based Path Planning Algorithm for Mobile Robots

Path planning plays a key role in the application of mobile robots and it is an important way to achieve intelligent mobile robots. Traditional path planning algorithms need to model environmental obstacles in a deterministic space, which is complex and easily trapped in local minimal. The sampling-based path planning algorithm performs collision detection on the environment and it is able to quickly obtain a feasible path. In order to solve the problem of inefficient search of the sampling-based Rapidly Expanding Random Tree (RRT-Connect) path planning algorithm, an improved RRT-Connect mobile robot path planning algorithm (IRRT-Connect) is proposed in this paper. In order to continue to speed up the search of the algorithm, a simple and efficient third node is generated in the configuration space, allowing the algorithm to be greedily extended with a quadruple tree in the proposed algorithm. Further, the method of adding guidance is proposed to make the algorithm have the characteristics of biasing towards the target point when expanding, which improves the exploration efficiency of the algorithm. In order to verify the effectiveness of the proposed algorithm, this paper compares the execution performance of the four algorithms in six environments of different complexity. The results of the simulation experiments show that the proposed improved algorithm outperforms the RRT, RRT-Connect and RRT* algorithms in terms of the number of algorithm iterations, planning time and final path length in different environments. In addition, the improved algorithm was ported to the ROS mobile robot for experiments with real-world scenarios.