Search

Designed to meet the demands of AGV global optimal path planning and dynamic obstacle avoidance, this paper proposes a combination of an improved A* algorithm and dynamic window method fusion algorithm. Firstly, the heuristic function is dynamically weighted to reduce the search scope and improve the planning efficiency; secondly, a path-optimization method is introduced to eliminate redundant nodes and redundant turning points in the path; thirdly, combined with the improved A* algorithm and dynamic window method, the local dynamic obstacle avoidance in the global optimal path is realized. Finally, the effectiveness of the proposed method is verified by simulation experiments. According to the results of simulation analysis, the path-planning time of the improved A* algorithm is 26.3% shorter than the traditional A* algorithm, the search scope is 57.9% less, the path length is 7.2% shorter, the number of path nodes is 85.7% less, and the number of turning points is 71.4% less. The fusion algorithm can evade moving obstacles and unknown static obstacles in different map environments in real time along the global optimal path. [ABSTRACT FROM AUTHOR]

In response to the imperative challenges associated with global path planning and dynamic obstacle avoidance for mobile robots navigating dynamic environments, we introduce a hybrid path planning methodology that synergistically combines an enhanced A* algorithm with an optimized dynamic window method. In the refined A* algorithm, an adaptive weight heuristic search function that comprehensively considers Manhattan distance and Euclidean distance is designed to improve the search efficiency; secondly, a redundant point elimination method is proposed to delete redundant paths node and perform path pruning, and then use Minimum Snap to smooth and optimize the pruned path. To address challenges associated with both random obstacle avoidance and dynamic obstacle avoidance, the fusion algorithm delineated in this study incorporates the global path nodes, derived through enhancements to the A* algorithm, as local target points while also employing the optimized dynamic window method for local path planning. The experimental results indicate that, on average, the improved A* algorithm can decrease the path length by 17.2% and reduce the number of search nodes by 62.3% compared to the conventional A* algorithm. After integrating and optimizing the dynamic window method, it can realize random obstacle avoidance and dynamic Avoidance. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)