A Mobile Robot Path Planning Algorithm Based on Improved A Algorithm and Dynamic Window Approach

• Published in: IEEE Access Vol. 10; pp. 57736 - 57747
• Main Authors: Li, Yonggang, Jin, Rencai, Xu, Xiangrong, Qian, Yuandi, Wang, Haiyan, Xu, Shanshan, Wang, Zhixiong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; Institute of Electrical and Electronics Engineers (IEEE); The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; Barriers; Curves; Heuristic algorithms; hybrid algorithms; improved A algorithm; improved DWA; Inflection points; Interference; Local optimization; Mobile robots; Path planning; Robots; Search problems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3179397

The traditional A* algorithm has several problems in practical applications, such as many path turning points, redundant nodes, and long running time. it is sometimes impossible to plan the theoretical optimal route. To solve the above problem, this paper presents an optimized A* algorithm, the adaptive adjustment step algorithm and the three-time Bezier curve are used to solve the problems of many turning points, large turning angles, and long running time in the search path. Moreover, aiming at the path planning problem of mobile robots facing dynamic obstacle interference in complex environments, an algorithm that integrates the improved A* algorithm with the dynamic window method is proposed, which not only solves the shortcomings of the A* algorithm in which the dynamic obstacles cannot be avoided, but also prevents the mobile robot from falling into local optimization. The results show that the fusion algorithm of the improved A* algorithm and the dynamic window method with the traditional A* algorithm reduces the number of turns by 50% and the path length by 3.62% compared with the original algorithm. In the same environment, compared with the traditional algorithm, the hybrid algorithm in this paper reduces the average time consumption by 10.27%, the number of path inflection points by 57.14%, and the accuracy is higher than 33.33%, which is more effective in complex dynamic environments.