Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope Migration
Automated Guided Vehicles (AGVs) face dynamic and static obstacles in the process of transporting patients in medical environments, and they need to avoid these obstacles in real time. This paper proposes a bionic obstacle avoidance strategy based on the adaptive behavior of antelopes, aiming to add...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/3/142 |
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| author | Jing Hu Junchao Niu Bangcheng Zhang Xiang Gao Xinming Zhang Sa Huang |
| author_facet | Jing Hu Junchao Niu Bangcheng Zhang Xiang Gao Xinming Zhang Sa Huang |
| author_sort | Jing Hu |
| collection | DOAJ |
| description | Automated Guided Vehicles (AGVs) face dynamic and static obstacles in the process of transporting patients in medical environments, and they need to avoid these obstacles in real time. This paper proposes a bionic obstacle avoidance strategy based on the adaptive behavior of antelopes, aiming to address this problem. Firstly, the traditional artificial potential field and dynamic window algorithm are improved by using the bionic characteristics of antelope migration. Secondly, the success rate and prediction range of AGV navigation are improved by adding new potential field force points and increasing the window size. Simulation experiments were carried out on a numerical simulation platform, and the verification results showed that the bionic obstacle avoidance strategy proposed in this paper can avoid dynamic and static obstacles at the same time. In the example, the success rate of path planning is increased by 34%, the running time is reduced by 33%, and the average path length is reduced by 1%. The proposed method can help realize the integration of “dynamic and static” avoidance in the process of transporting patients and effectively save time by using AGVs to transport patients. It provides a theoretical basis for realizing obstacle avoidance and rapidly loading AGVs in medical environments. |
| format | Article |
| id | doaj-art-b4e6e85d1abb4db6a1d25650d119e16f |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-b4e6e85d1abb4db6a1d25650d119e16f2025-08-20T03:43:34ZengMDPI AGBiomimetics2313-76732025-02-0110314210.3390/biomimetics10030142Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope MigrationJing Hu0Junchao Niu1Bangcheng Zhang2Xiang Gao3Xinming Zhang4Sa Huang5School of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaChangchun Institute of Technology, Changchun 130103, ChinaSchool of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaBethune Second Clinical School of Medicine, Jilin University, Changchun 130015, ChinaAutomated Guided Vehicles (AGVs) face dynamic and static obstacles in the process of transporting patients in medical environments, and they need to avoid these obstacles in real time. This paper proposes a bionic obstacle avoidance strategy based on the adaptive behavior of antelopes, aiming to address this problem. Firstly, the traditional artificial potential field and dynamic window algorithm are improved by using the bionic characteristics of antelope migration. Secondly, the success rate and prediction range of AGV navigation are improved by adding new potential field force points and increasing the window size. Simulation experiments were carried out on a numerical simulation platform, and the verification results showed that the bionic obstacle avoidance strategy proposed in this paper can avoid dynamic and static obstacles at the same time. In the example, the success rate of path planning is increased by 34%, the running time is reduced by 33%, and the average path length is reduced by 1%. The proposed method can help realize the integration of “dynamic and static” avoidance in the process of transporting patients and effectively save time by using AGVs to transport patients. It provides a theoretical basis for realizing obstacle avoidance and rapidly loading AGVs in medical environments.https://www.mdpi.com/2313-7673/10/3/142medical AGVbionic strategyartificial potential field algorithmdynamic window algorithmpath planning |
| spellingShingle | Jing Hu Junchao Niu Bangcheng Zhang Xiang Gao Xinming Zhang Sa Huang Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope Migration Biomimetics medical AGV bionic strategy artificial potential field algorithm dynamic window algorithm path planning |
| title | Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope Migration |
| title_full | Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope Migration |
| title_fullStr | Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope Migration |
| title_full_unstemmed | Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope Migration |
| title_short | Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope Migration |
| title_sort | obstacle avoidance strategy and path planning of medical automated guided vehicles based on the bionic characteristics of antelope migration |
| topic | medical AGV bionic strategy artificial potential field algorithm dynamic window algorithm path planning |
| url | https://www.mdpi.com/2313-7673/10/3/142 |
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