Deployment of nursing robot for seasonal flu: fast social distancing detection and gap-seeking algorithm based on obstacles-weighted control

Seasonal flu is currently a major public health issue the world is facing. Although the World Health Organization (WHO) suggests social distancing is one of the best ways to stop the spread of the flu disease, the lack of controllability in keeping a social distance is widespread. Spurred by this concern, this paper developed a fast social distancing monitoring solution, which combines a lightweight PyTorch-based monocular vision detection model with inverse perspective mapping (IPM) technology, enabling the nursing robot to recover 3D indoor information from a monocular image and detect the distance between pedestrians, then conducts a live and dynamic infection risk assessment by statistically analyzing the distance between the people within a scene and ranking public places into different risk levels, called Fast DeepSOCIAL (FDS). First, the FDS model generates the probability of an object’s category and location directly using a lightweight PyTorch-based one-stage detector, which enables a nursing robot to obtain significant real-time performance gains while reducing memory consumption. Additionally, the FDS model utilizes an improved spatial pyramid pooling strategy, which introduces more branches and parallel pooling with different kernel sizes, which will be beneficial in capturing the contextual information at multiple scales and thus improving detection accuracy. Finally, the nursing robot introduces a gap-seeking strategy based on obstacles-weighted control (GSOWC) to adapt to dangerous indoor disinfection tasks while quickly avoiding obstacles in an unknown and cluttered environment. The performance of the FDS on the nursing robot platform is verified through extensive evaluation, demonstrating its superior performance compared to seven state-of-the-art methods and revealing that the FDS model can better detect social distance. Overall, a nursing robot employing the Fast DeepSOCIAL model (FDS) will be an innovative approach that effectively contributes to dealing with this seasonal flu disaster due to its fast, contactless, and inexpensive features.