## 1 Introduction
In the case of persistent non-compliance by a group of persons wandering from one place to another, the robot pursued that group and kept displaying the message. One of the limitations of this system is that while pursuing that group, there is a high possibility that the robot would not be able to attend to other non-compliant groups. Moreover, this study is missing the mechanism to track groups that remained unattended by the robot. Furthermore, there was no long-term tracking of non-compliant groups to further monitor their behavior after receiving the alert message from the robot. Consequently, it was not possible to track repetitive violations using this \begin{table}
\begin{tabular}{c c c} \hline \hline
**Disease Name** & **Time Period** & **Death Toll** \\ \hline Spanish Flu & 1918–1919 & 40 Million–50 Million \\ Hong Kong Flu & 1968–1970 & 1 Million \\ HIV/AIDS & 1981–Present & 25 Million–35 Million \\ SARS & 2002–2003 & 770 \\ Swine Flu & 2009–2010 & 200,000 \\ Ebola & 2014–2016 & 11,000 \\ COVID-19 & 2019–Present & 2.04 Million \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The history of pandemics with respect to the death toll. system. Due to these issues, this system may not be able to effectively enforce physical distancing constraints in large areas such as shopping malls, airports, etc. In this regard, we present an autonomous and interactive monitoring system with large-scale area coverage for effective and efficient monitoring to combat COVID-19 and future pandemics. In the present study, we present a cooperative MARS for monitoring and enforcing physical distancing constraints in large areas through human-robot interaction (HRI) to combat COVID-19 and future pandemics. In the present study, a group of persons who violate physical distancing constraints is referred to as a non-compliant group. The aims of the proposed system are as follows: (1) persistent monitoring of large indoor areas using multiple CCTV cameras to detect the violation of physical distancing constraints; (2) interactive encouragement of non-compliant groups to adhere to physical distancing constraints by giving them an alert message through speech-based HRI; (3) long-term tracking and re-identification of non-compliant groups through a multi-camera system to alert them on highest priority and report to the control room in the case of repetitive violations of physical distancing constraints. As shown in Figure 1, the design of the proposed system is based on two types of agents: (1) fixed agents, i.e., calibrated CCTV cameras, and (2) mobile agents, i.e., self-docking autonomous robots with collision-free navigation. Both agents work cooperatively by mutually sharing useful information between each other. In the present system, the persistent monitoring of physical distancing constraints is performed based on the visual information received from the distributed network of multiple CCTV cameras mounted within the building. The team of robots stay at their docking stations until a violation of physical distancing constraints is detected by the cameras. In the case of a detected violation, the system shares the location of the non-compliant group with the robot that is located closest to the area of the building where the violation was detected. After receiving the location, the robot navigates to the given location to convey an alert message to the target non-compliant group. The system architecture with a detailed description of each functional module is presented in Section 3. The main contributions of the present study are summarized as follows:  
1. We propose an intelligent and cooperative MARS for the efficient monitoring of physical distancing constraints and interactively enforcing them through HRI to combat COVID-19 and future pandemics. To the best of our knowledge, we are the first to propose such a monitoring system, which is based on a distributed network of multiple cameras and a multi-robot system (MRS) to combat ongoing and future pandemics. 2.