Abstract
Abstract
This study investigates the use of four common path planning algorithms - RRT (Rapidly-Exploring Random Tree), Dijkstra, A*, and ACO (Ant Colony Optimization) - for autonomous endpoint search in pre-defined grid mazes. Path planning is a crucial problem in robot navigation, involving the ability to find optimal paths with the minimum running time in complex environments. In this research, it firstly introduces the basic principles and mechanisms of each algorithm. RRT is a random sampling-based algorithm that explores the search space by generating a tree-like structure randomly. Based on graph search, Dijkstra’s method determines the best solution by figuring out the shortest routes between the starting point and other places. A* algorithm combines heuristic functions and the concept of Dijkstra’s algorithm to reduce the search space and improve search efficiency. ACO algorithm simulates the behavior of ants searching for food by updating pheromone information and employing search strategies to find the optimal path. Next, a series of experiments to evaluate the performance of these four algorithms in pre-defined mazes. Search time, path lengths, and accuracy of the optimal path for each algorithm are recorded. Eventually, the result analysis of the four algorithms and the comparison are given in the last two sections of this article. In addition, A* algorithm has the best performance under the conditions set out in this article.