## 2 Materials and Methods
### Quadruped Robot Dog and Networked External Sensor Circuit for Air Quality Monitoring  
The PuppyPi Pro by Hiwonder displayed in Figure 3 is a QRD with LiDAR that was used in a series of experiments as a means of data acquisition for locations that are remote, confined, and generally inaccessible to humans. This particular QRD is powered by a Raspberry Pi 4B running the Robot Operating System (ROS). Air quality and environmental data, including the spatial positioning of the QRD, are examined through readings collected from several sensors mounted on an 82 mm \(\times\) 53 mm (L \(\times\) W) half-sized breadboard attached to the front of the QRD. The MQ-9 CO and Combustible Gas sensor paired with the MQ-135 Gas sensor, calibrated to measure excess CO\({}_{2}\) concentrations, monitor gas levels. A DHT-11 sensor tracks humidity and temperature, and a Keyes KY-018 Photoresistor Light Detector Module detects the presence and intensity of light levels in the surrounding environment. These data are aggregated using an Arduino Nano 33 IoT, a microcontroller board equipped with an LSM6 DS3 3-axis digital accelerometer and gyroscope Inertial Measurement Unit (IMU), providing movement and orientation details. The Arduino Nano 33 IoT includes the Nina W102 uBlox module for wireless communication via Bluetooth and single-band 2.4 GHz Wi-Fi. In this case, updated sensor readings are transmitted using the User Datagram Protocol (UDP) to a locally hosted Python server in JSON string format at a frequency of 2 Hz. An HTC 5G Hub is used as a Wireless Local Area Network (WLAN). On reception of sensor data packets, they are parsed, formatted, and inserted into a locally hosted InfluxDB time-series database as individual, timestamped points. The InfluxDB server connects to a locally hosted Grafana Dashboard, which sup  
Figure 3: Quadruped robot dog for building safety monitoring. (**a**) Quadruped robot dog (PuppyPi Pro). (**b**) Sensor nodes mounted on quadruped robot dog. ports real-time and interactive data visualization. The data are simultaneously written to a CSV file in a separate directory as a backup to the database, see Figure 4.  
Both MQ sensors have two ways to convey the measured gas parameters: an analog output voltage that can be remapped to PPM values and an active-low digital output pin which triggers if the reference voltage exceeds a level set by a potentiometer included in the breakout board. This project focuses on the digital output from the MQ-135 to detect CO\({}_{2}\) values exceeding 1000 ppm--a documented threshold where declines in cognitive faculties are noticeable in humans after 2.5 h of exposure [34]. As the Arduino Nano 33 IoT operates at a 3.3 V operating voltage, two additional potentiometers act as voltage dividers to limit the MQ-9 and the MQ-135 sensor output from 5 V to 3.3 V. The Arduino Nano 33 IoT is programmed to transmit a warning to the Python server on the falling edge of the digital output of the MQ-135, indicating that the sensor exceeds approximately 1000 PPM. Quadruped Robot Dog Floor Mapping with Infrastructural Acoustic Analysis and Low-Light Visual Monitoring  
In order to monitor the terrain within a building, the original floor plan is used as a navigational aid for the robot, guiding it through a room filled with complex obstacles. For this procedure, the Hector SLAM algorithm [35] completes the mapping. To use the maps for navigation, RViz collects the data and displays them on the PC. RViz is a 3D visualization tool in ROS Noetic [36] that runs as part of an Ubuntu image on the VMware Workstation Pro on a PC. The robot navigates around the building with its PS2 wireless controller and sends the mapping data back with ROS Noetic. The PC serves as the Master in the ROS communication system. The developed map is overlaid and compared with the floor plan to survey the building to verify its safety before the human enters the suspected area.