# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 2 Ontology in SAR Scenario
### The Ontology in SAR
and (orderingConstraints value RescueActions14) and (orderingConstraints value RescueActions23) and (orderingConstraints value RescueActions24) and (orderingConstraints value RescueActions34)  
Relying on the individual class in Protege, the one of constraints among subactions is defined as follows. It defines the time sequence of action _PickUpVictim_ and action _FreeVictim_. Assuming that a specific task has \(n\) subactions, it is easy to know that the total number to define the specific task is \(C_{n}^{2}\), provided that we completely define the time sequences of all subactions. **Individuals**: RescueActions12  
**Types:**  
PartialOrdering-Strict  
**Annotations:**  
occursAfterInOrdering PickUpVictim  
occursBeforeInOrdering FreeVictim  
As shown in Figure 4, the entity ontology contains three parts--robots, hardware, and software--which demonstrate the capability and characteristic of robots. Robots include various types of robot, like ground robots, underwater robots, and air robots. The concepts of robots can be instantiation as individuals in Protege. Hardware is composed of the components and devices the various types of robots may have. It can be further divided into perception devices, navigation devices, and base devices. Navigation devices refer to the hardware devices that robots need to navigate and locate, such as IMU (Inertial Measurement Unit). Perception devices refer to the hardware devices that robots need to perceive and understand the environment, such as LIDAR (Light Detection and Ranging) and a camera. Base devices refer to the hardware devices related to the low-level control of the robot, such as the motor, battery, and so on. Software consists of the functional ROS node, which can publish its specific topic and subscribe to the other topics. It can also achieve the communication among control nodes. A variety of relationships can be defined by developers to describe the relationship among the entity ontology and with other ontologies. As shown in Figure 5, the environment ontology is mainly used to query and reason how to make the high-level decision for the intelligence of robots. It is important for the achievement of intelligent robots. The environment ontology describes all the things existing in SAR. It includes the environment map, which is already known for the robots to navigate. It also includes the objects that may be unknown for the robots and need to be recognized to update the environment ontology. The updated knowledge can be shared with other robots to reduce the repetitive work and improve the efficiency of task execution. Apart from the contents above, corresponding relationships also exist among the three parts which connect this knowledge with one another. These relationships can be defined according to the developers' own needs. Taking the disaster rescue task as an example (Figure 6), the rescue task is achieved by the mobile wheeled robot TurtleBot3 and in the environmental map of Room2. The latter is built by the Lidar of TurtleBot1. Accordingly, these three ontology modules can be linked and added to constraints so that they make up the whole ontology jointly. It takes about 2.34 s to generate 55,000 individuals by the evaluation test of our ontology. The maximum generation rate is about 23,500 individuals per second. As a comparison, KnowRob [15] has a maximum generation rate of 22,000 individuals per second, and ORO [17] has a maximum generation rate of 7245 individuals per second. Figure 4: The entity ontology is shown in Protége. Figure 5: The environment ontology shown in Protége.