An Optimized Path Selection Algorithm for the Minimum Number of Turns in Path Planning Using a Modified A-Star Algorithm

TL;DR: An optimized path selection algorithm for the minimum number of turns in path planning using a modified A-star algorithm finds the optimal path for a robot in challenging maps, minimizing the number of turns and maximizing distance covered in a single spin.

Abstract: Robots are becoming more prevalent in everyday life. The most difficult aspect of using a robot is defining its path. Path planning describes the movement of the robot from the starting point to the goal point. This research provides an improved A-star method for a mobile robot’s path-planning finding capabilities in challenging maps. The improved A-star algorithm makes use of the A-star algorithm’s properties. First, the grid surface model is built, and the modified A-star algorithm’s evaluation function is calculated. Second, the acquired multiple least-distance paths were further analyzed using a global minimum number of turns approach, which can speed up convergence and smoothen the global path. In the combinatorial selection of the optimum path from the set of the shortest paths, the path with the minimum diversion of the robot from the initial to the final position is used. It enables the robot to travel the greatest distance possible in a single spin. The straight path enables the robot to jump and cover the most amount of ground in a single leap while keeping a consistent pace. MATLAB was used to run the experiments on a specific grid area.