Hu Jing, Niu Junchao, Zhang Bangcheng, Gao Xiang, Zhang Xinming, Huang Sa
School of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Changchun Institute of Technology, Changchun 130103, China.
Biomimetics (Basel). 2025 Feb 26;10(3):142. doi: 10.3390/biomimetics10030142.
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.
自动导引车（AGV）在医疗环境中运送患者的过程中会面临动态和静态障碍物，并且需要实时避开这些障碍物。本文提出了一种基于羚羊自适应行为的仿生避障策略，旨在解决这一问题。首先，利用羚羊迁徙的仿生特性对传统人工势场和动态窗口算法进行改进。其次，通过添加新的势场力点和增大窗口尺寸来提高AGV导航的成功率和预测范围。在数值模拟平台上进行了仿真实验，验证结果表明本文提出的仿生避障策略能够同时避开动态和静态障碍物。在该实例中，路径规划的成功率提高了34%，运行时间减少了33%，平均路径长度减少了1%。所提方法有助于在运送患者过程中实现“动态与静态”避障的一体化，通过使用AGV运送患者有效节省时间。它为在医疗环境中实现避障和快速装载AGV提供了理论依据。
