# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 1 Introduction
Perception and Manipulation Knowledge (PMK) [23] puts forward a knowledge processing framework for navigation tasks in indoor scenario following IEEE-1872 standards [24]. In references [22; 25], they use other logical languages, such as BC, to represent knowledge in the field of robotics [26; 27]. However, this approach is not conducive to the sharing of knowledge and the updating of new knowledge. However, there has not been an ontology built for SAR. Our ontology is designed especially for SAR and is applied to the high-level decision-making of robots. Task planning is easy with the knowledge representation in SAR. The challenge of our research work is how to efficiently and reasonably represent and update the complex task knowledge for smart decision-making in SAR. On the basis of the ontology we have  
Figure 1: The three levels of robotic control defined in article. built, another challenge is how to infer the tasks the robot should to perform in its current state. Then, the robot should know how to decompose the tasks into a sequence of atomic actions. We consider spatio-temporal, continuous, and discrete information to represent the knowledge in SAR with the form of ontology. The Semantic Web Rule Language (SWRL) was used to compensate for OWL's inability to express complex rules. The robot can reason through the ontology with the SWRL rules to obtain the tasks the robot should perform in the current circumstances. Besides, in the process of performing tasks, the robot can obtain the position information of victims by Bayesian Reasoning and get other semantic information by scanning the QR (Quick Response) code. All the information will then be updated in the ontology. In the ontology, the representation of high-level tasks and atomic actions are independent. This article proposed an algorithm for task planning in SAR by matching the execution preconditions of atomic actions and their effects on the environment from the initial state to the goal. The task planning module can also be executed to adapt the different input tasks. Finally, the indoor experiment environment is built, and the usability of the built ontology is verified experimentally. In the real robot experiment, we used Robot Operating System (ROS) [28] as the software system and TurtleBot3 as the hardware platform. The experimental results show that the robot can perform the high-level smart decision-making well based on the ontology. The ontology we have built provides a domain knowledge base for the robot to better understand a complex environment and plan tasks in an SAR scenario. The rest of the paper is arranged as follows. Section 2 describes the ontology we have built in detail. Section 3 presents the methods we adopted about the knowledge reasoning and knowledge updating. Section 4 introduces the methods of carrying out the experiment about smart decision-making by the real robot. Section 5 displays the results obtained from the experiments. Section 5 also analyzes and discusses the results. Finally, Section 6 summarizes all the work and highlights future research directions.