# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 3 Methods
### The Update of Ontology  
The objects in the disaster rescue scenario are represented as individual classes. Taking Figure 10 as an example, [PERSON] is the instance of victim and has some attributes defined as we need. In the disaster rescue scenario, we pay more attention on the vital signs of victim such as heart rate (HR) and blood pressure (BP). In addition, the basic information of the victim is also vital for smart decision-making, such as position and gender. In the initial state, the position is default. The position knowledge will be updated with the corresponding victim being recognized and located, which is also the building process of the semantic map. The traditional method to build the semantic map is to recognize and locate objects. Then the recognized information will update the environment ontology in the language of OWL. That connects the map by SLAM with the semantic information. In our research work, we make some assumptions and simplify to focus on the smart decision-making work. In the process of recognizing the objects in the disaster rescue environment, we adopt the method to recognize based on the Quick Response (QR) code, which avoids the complex semantic information obtaining process. QR code is a 2D matrix code. It is widely used in daily life for its quick recognition ability, large and stable information reserve. In the process of the robot searching for the target, the QR code attached  
Figure 10: The knowledge representation in environment ontology. (**a**) The structure of environment ontology; (**b**) the knowledge update in environment ontology. to the simulated target can be scanned by the onboard camera. After the processing of the QR code recognition program, the functional semantic information can be obtained, providing the original data for the construction of the semantic map, shown in Figure 10b. According to the requirements of SAR tasks, the functional semantic information can be designed as the following form:  
\[\theta\ =\ (GEN,HR,BP) \tag{3}\]  
The target object is the victim ready to search to rescue. Thus, the QR code is designed to represent the victim. \"GEN\" is defined as the gender of victim, \"HR\" is defined as the heart rate of the victim, and \"BP\" represents blood pressure. The robot recognizes the target through QR code and obtains semantic information. However, the position information of the target object, including room frame and concrete position information, is still missing. As shown in Figure 11, on the basis of previous research works, the position information of the target object is calculated by Bayesian reasoning. \(P_{G,t}(L)\) represents the probability of the existence of target object in grid \(G\) at time \(t\), \(L\) represents the existence of target object, and then Bayesian reasoning can be used to continuously update the probability of detected target object. \(P\) means that the target object does exist in the case that the algorithm detects the target, while \(O\) means that the target object is detected. In addition, the existence of a target object at a certain position is also related to the geometric measurement information corresponding to this location, which can be obtained through the 2D occupied grid map constructed by the middle layer. As shown in Figure 12, \(M(G)\) is used to represent the geometric categories of grid \(G\), \(\text{M}(G)\in\{M1,M2,M3,M4\}\). \(M1\) represents that the detected object is in the obstacle area of the grid map, \(M2\) represents that the detected object is in the unknown area of the grid map, \(M3\) means that the detection point is in the free area of the grid map but there are obstacles nearby, and \(M4\) means that the detection point is in the free area of the grid map and no obstacles nearby. Besides, the definition of the size of the nearby area to set the threshold according to the specific environment, such as 20 cm. Figure 11: The process of semantic information recognition and updating.