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Research of hybrid path planning with improved A* and TEB in static and dynamic environments

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Abstract

In this study, we introduce a new approach to path planning suitable for both static and dynamic environments. Our method combines the Obstacle Avoidance Improved A* (OA-IA*) algorithm with the Time Elastic Band (TEB) technique. The OA-IA* incorporates four key elements: utilization of robot direction information, adaptive adjustment of bandwidth, enhancement of evaluation function, and path smoothing operations. We conducted experiments to validate our approach, including simulations and real-world verifications in various environments. In the simulation experiments, we compared our method with two previous approaches: Improved Local Particle Swarm Optimization (ILPSO) and Obstacle Avoidance RRT (OA-RRT) method. Across seven different simulated maps, the OA-IA* algorithm showed an average improvement of 0.19 in Path Optimal Degree (POD) compared to the ILPSO algorithm, along with an average time savings of 11 s. Furthermore, compared to the OA-RRT algorithm, the OA-IA* algorithm achieved an average POD increase of 0.36, resulting in an average time savings of 60.37 s. Moreover, we compared our method with APF-RRT*, APF-RRT, RRT, and RRT* approaches across 50 simulation maps. On average, our method achieved higher POD values by 0.54, 0.31, 0.85, and 0.26 compared to APF-RRT*, APF-RRT, RRT, and RRT* methods, respectively. Additionally, the average running time of our method was significantly reduced by 90 s, 64.7 s, 38.13 s, and 19.4 s compared to APF-RRT*, APF-RRT, RRT, and RRT* methods, respectively. In the experimental verification section, we tested our method in a real office, laboratory, and workshop environments. In two real-world environments spanning 9.4 m\(^2\) and 9.2 m\(^2\), our enhanced A* method integrated with TEB exhibited an average POD value that at least 0.125 higher compared to that of ILPSO combined with TEB. These results demonstrate the effectiveness of our hybrid path planning method in complex dynamic environments, achieving optimal outcomes in terms of path length, smoothness, and speed. In addition, it also ensures the smoothness of the path and speed, and the smoothness is less than 0.05 rad/s\(^2\).

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No datasets were generated or analyzed during the current study.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (Grant No. 62276207) and the Key Research and Development Project of China Energy Engineering Group Co., Ltd. (No. CEEC2022-ZDYF-01).

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Author notes

  1. Ning An and Zongfang Ma have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Technology, Xi’an University of Architecture and Technology, No.13 Yanta Road Middle Section, Beilin District, Xi’an, 710055, China

    Lin Zhang

  2. Department of Mechanical Engineering, Xi’an Jiaotong University, No.28 Xianning West Road, Beilin District, Xi’an, 710049, China

    Ning An

  3. Department of Artificial Intelligence and Automation System, Xi’an University of Architecture and Technology, No.13 Yanta Road Middle Section, Beilin District, Xi’an, 710055, China

    Zongfang Ma

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  1. Lin Zhang
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Contributions

Lin Zhang contributed to the conception of the study, performed the experiment, and wrote the main and revised manuscript. Ning An contributed to data analysis of Figs. 4, 5, 6, 7, 8, 9, 10, and 11 and revised manuscript preparation. Zongfang Ma helped perform the analysis with constructive discussions. All authors reviewed the manuscript.

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Correspondence to Lin Zhang.

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Zhang, L., An, N. & Ma, Z. Research of hybrid path planning with improved A* and TEB in static and dynamic environments. J Supercomput 80, 18009–18047 (2024). https://doi.org/10.1007/s11227-024-06155-0

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