A comprehensive study of path planning algorithms for autonomous robots.

Path planning plays a key role in autonomous robot research. This paper studies in detail three of the various path planning algorithms for autonomous robots, including the A-Star path planning algorithm, unit decomposition, and rapid exploration of random trees (RRT). The A-Star algorithm determines the shortest route between two points in a graph. The principle is to calculate the distance between the starting point and the end point, select the shortest point, and then recalculate the distance and select the shortest route until reaching the target point. RRT can be used for path planning in multi-dimensional, complex spaces. The main principle is to randomly generate points in the map and connect them to the nearest nodes to form Cell decomposition is to divide the plane space into units of different sizes, turn the complex space into a simple space, and use the A-Star path planning algorithm to calculate the shortest path from the starting point to the target point. This article chooses experiments with different formers to analyze the application of various algorithms. Finally, this article concludes that cell decomposition divides simple geometric maps and uses the A-Star algorithm to plan paths, which can make the route on the geometric map more accurate. Different extension algorithms of RRT can be used in scenarios that require high accuracy, high quality, and reasonable computational costs. A-Star is also a suitable choice for quick route planning. This study conducts a comprehensive study of autonomous robot path planning algorithms, which will help future research select the most appropriate algorithm based on specific application requirements. [ABSTRACT FROM AUTHOR]