ASL-DWA: An Improved A-Star Algorithm for Indoor Cleaning Robots

Abstract

The traditional A-star algorithm has many search nodes, and the obtained path has polylines and cannot avoid local unknown obstacles. In response to these problems, this paper proposes a new improved A-star algorithm suitable for indoor cleaning robots, called ASL-DWA (A Star Leading Dynamic Window Approach). First, to solve the problem of many search nodes in the A-star algorithm, a new hybrid heuristic function that combines Euclidean distance and point-to-line distance is proposed, thereby reducing the number of search nodes. Then, to solve the problem that the A-star algorithm has a polyline path and cannot avoid local unknown obstacles, this paper designs the global path yaw angle according to the relationship between the real-time position of the robot and the global path, which is added as a score item to the traditional score function. A decay coefficient with prediction function is also added to the score function to reduce the risk of the algorithm falling into local optima. Finally, a mechanism to adaptively adjust the coefficient according to the distance between the robot and the target point is designed, thereby realizing ASL-DWA. The ASL-DWA algorithm is tested in three indoor environments and compared with traditional algorithms. The experimental results show that ASL-DWA can meet the path planning requirements of mobile robots in indoor environments, and has obvious advantages over traditional algorithms.