Integration of improved APF and RRT algorithms for enhanced path planning in mobile robotics

Abstract

In the process of path planning for mobile robots, situations occur such as local optimal solutions and failure to reach the target point when the traditional Artificial Potential Field (APF) method is used independently for path search; When the traditional Rapidly-exploring Random Tree method (RRT) is used independently for path search, the generated random path tree has multiple branching branches, thereby resulting in high time and distance costs. In response to the above issues, it’s attempted that the two methods were integrated and compensated for each other’s shortcomings. Firstly, the mathematical model of the traditional APF algorithm was improved to solve the problems of unbalanced force and unreachable targets. Secondly, the improved APF was established in the RRT algorithm space by the guide of geometric probability model, and APF contributed to enhancing the target directionality of RRT. Simulation experiments had shown that the fusion algorithm had significant advantages in path length and runtime.