## 2 Materials and Methods
### Fire Prevention and Fire Detection  
When it comes to fire prevention and fire detection, there are many existing solutions. Some of them use different types of sensors, which include temperature sensors [11], smoke and gas sensors [12], or flame detectors [13], which are becoming obsolete due to their limitations. In [14], the authors presented an advanced method for fire detection using chemical sensors. The advantage of that kind of approach lies in the fact that chemical symptoms of fire appear even before the smoke or flame, so it presents an excellent method for fire prevention. Using CNNs in conjunction with raw RGB picture-processing and computer-vision algorithms has been a popular fire-detection technology during the past decade. The two pre-trained networks utilized in [15] were VGG16 and ResNet-50, which the authors improved by adding more fully connected layers. They evaluated these models using a dataset that was unbalanced and contained fewer fire images, simulating the environment of the actual world. The results indicated higher accuracy in comparison to the base models, but the additional layers also resulted in longer training times. Two steps make up the unique fire-detection approach that the authors suggested in [16]. The first step is to use a faster R-CNN network to find and locate potential fire zones. In the second phase, validation of the fire zones that have been spotted is carried out by analyzing spatial attributes using linear dynamic systems. Finally, VLAD encoding is used, which greatly enhances efficiency and decreases detection errors, to differentiate between actual fire and fire-colored objects. The proposed solution maintained a high true-positive rate while drastically reducing the false-positive rate since they employed many fire-colored images for training. Based on CNN's cutting-edge object-identification models, the authors in [17] suggested four revolutionary fire-detection techniques: Faster R-CNN, R-FCN, SSD, and YOLOv3. Faster R-CNN and R-FCN are examples of two-stage object-detection networks since they comprise both a classification network and a region-proposal network. In the initial step, CNN uses input images to produce region suggestions. The second stage determines whether the fire is present in the suggested regions using region-based object-detection CNN. One \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component Type** & **Component** & **Approximate Price** \\ \hline \multirow{4}{*}{Drivetrain} & Differential drive, 2 rubber wheels and \\  & motors with encoders (200 W), 2 caster & USD 600 \\  & wheels for stability, suspension \\  & Industrial PC (i7-10610U, 32 GB DDR4, \\  & 512 GB SSD) & USD 1000 \\ CPU (image processing) & Nvidia Jetson Xavier AGX 64 GB & USD 1700 \\ Battery & LiFePO4, 24 V, 100 Ah, 10 h autonomy & USD 600 \\ Sensors & Gyroscope, accelerometer, temperature & USD 300 \\  & sensor, 3 \(\times\) ultrasonic sensors in front & \\ Sensor-data management & Custom electronics with CAN Bus interface & Indoor built \\ LiDAR & RPLidar A3 (2D, 25 m range) & USD 600 \\ RGB-D camera & Orbbec Astra (640 \(\times\) 480 @ 30 fps, RGB \\  & and depth) & USD 150 \\ Visual camera & 1080p 30 fps RGB camera with NIR area & Indoor built \\  & capabilities & Indoor built \\ Infrared thermal camera & 384 \(\times\) 288 30 fps with full radiometric data & Indoor built \\  & Various gas sensors, microphone, LED & USD 200 \\ Other & reflector, laser range finder & \\  & 3 \(\times\) 3 kg (powder, foam, CO\({}_{2}\)), mechanical & \\ Fire extinguishing & hand, electronic nozzle & Indoor built \\  & Weight & 30 kg \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hardware specifications of the final version of FireBot. stage networks (SSD and YOLOv3) were proposed since two-stage networks have slower detection speeds. They use a single forward CNN to forecast the object class. All suggested solutions outperformed other non-CNN-based approaches in tests using two separate datasets.