Optimal Viewpoints Selection for Monitoring Teleoperators Based on Event Artificial Potential Method

The visual monitoring performance of teleoperated robots in complex environments has always been challenging, and existing viewpoint selection methods such as free and first-person viewpoints are inadequate. The free viewpoint provides a high degree of control freedom but increases the cognitive load of the operator, while the first-person viewpoint has a limited field of view, making it difficult to obtain a global perception. In this paper, we propose an automatic viewpoint selection method based on artificial potential field (APF). By setting gravity and repulsion fields around important points and obstacles, the camera robot can adaptively adjust its viewpoint to ensure that the operator always obtains the best view for task execution. Experiments were designed for a mobile robot navigating a sanitization task, and the method was compared with existing methods. The results show that the proposed method excels in objective indicators such as task completion time, while significantly reducing the operator's emotional fluctuation and cognitive load, providing technical support for realizing efficient, comfortable, and low-risk teleoperation.