# HelpResponder--System for the Security of First Responder Interventions
## 4 Implementation of the Safe and Flame-Aware Intervention Architecture
### Mobile Ground Autonomous Vehicle (MGAV) for Flame/Fire Focus Detection  
The data acquisition system also includes real-time video capture, environmental monitoring, and processing equipment coupled to the mobile ground autonomous vehicle (MGAV). The MGAV is based on [33] and uses three ultrasonic sensors to recognize the environment, with sonar operation controlled by an ATmega328P microcontroller. We include more sensors to improve navigation and acquisition. Figure 6(a),b shows the components and deployment, and the connections between them are shown in Figure 6(c). The MGAV can navigate within the confined enclosure without user interaction. It can monitor the environmental parameters to detect the focus of the emergency based on network deployment.  
Figure 6: Definitions of TP, FP, FN, and TN.  
Figure 7: _Cont._  
The description of the modules is as follows:  
Vision module: as we described in a previous section, the UDOO contains the intelligence to detect fire by using artificial vision.  
Database module: it allows access to monitored data related to images, environmental data, and data of interest at an intervention. We implemented an external database server for remote data management based on a MySQL Server 5.7.29 installed on an Ubuntu server 18.04 LTS (Long Time Service) machine. The access to the database content uses HTTPS and secure authentication. Therefore, only authorized users can obtain access. All stored data is time stamped, and it is associated with a specific experiment, allowing a comparison of the result between the different tests.  
Control module: the MGAV can navigate among avoiding obstacles using ultrasonic sensors included at this module. The MGAV has been equipped with HC-SR04 ultrasonic sensors; an ATMEL processor; the TT Micro DC Geared motor--capable of rotating at 160 rpm without unload at 6V voltage; a motor driver based on the L298N integrated circuit, a dual full-bridge driver; and finally, protection diodes and an LM7805 regulator which supplies a 5V supply voltage to the L298N IC and also to external sensors.  
The location of the beacons will be developed for each scenario to locate firefighters. This means that the robot has distance sensors (ultrasound) to overcome inaccuracies in the absolute location. An RF52840 PDK was used [33]. Each node can be integrate a different environmental sensor. For this research, we have validated the onboard unit using a KY-026 (flame sensor), the SoC nRF52840 PDK temperature, the LM35 (environmental temperature), and the MQ-7 (toxicity in the air, CO concentration sensor) [39]. We have defined the next equations to calculate the values of measured risk. The AnalogReadT [see Equation (6)] is the value registered from the SoC nRF52840 PDK temperature. The AnalogReadE (see Equation (7)) is the value registered from the LM35. The minimum conditions establish an exposure time of fewer than 30 min for the following parameters: air temperature of 60 \({}^{\circ}\)C, carbon monoxide contents of 350 ppm. A CO value over 3000 ppm and an HCN (hydrogen  
Figure 7: Autonomous indoor navigation device. (**a**) Frontal, (**b**) lateral, (**c**) connection diagram.  
cyanide) value over 300 ppm are fatal. These values will be useful to evaluate the critical factors in the air during intervention according to the normative described in [1; 2; 3]:  
\[\text{Temperature}\ [^{\circ}\text{C}]=\text{AnalogReadT}.\frac{5000\,\text{mV}}{4096} \cdot\frac{1\,^{\circ}\text{C}}{10\,\text{mV}} \tag{11}\]  
\[Rs[\Omega]=RL\cdot\frac{5-V_{in}}{V_{in}}=1000\Omega\cdot\frac{5V-\left(\frac{ AnalogReadE5}{4096}\right)}{\frac{AnalogRead\cdot 5}{4096}} \tag{12}\]  
\[CO\ concentration\ [\text{ppm}]=233.9\cdot\left(\frac{Rs}{5463}\right)^{-1.4} \tag{13}\]