Abstract
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.