# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 1 Introduction  
Today, many theories and methods have emerged in the field of artificial intelligence (AI), which have also been deeply applied in many domains. As a typical method of AI, deep learning (DL) has made a great breakthrough [1]. It is also widely used in robotics [2; 3]. However, DL is difficult to explain, which limits its application in some fields requiring knowledge reasoning. Since the 1970s, AI researchers have gradually realized that symbolic knowledge methods play a key role in more powerful AI systems. They think that knowledge and knowledge reasoning are the core of AI. Since then, ontology has been strongly developed as a form of knowledge base. It can represent and understand the complex real world. Similar to the human thinking mode, robots can use knowledge and reasoning to realize smart decision-making. Now, it has been widely used in AI [4; 5], semantic web [6; 7], informatics [8], and other fields. There are many kinds of complex emergency tasks in disaster search and rescue scenario. It is a huge challenge for a robot to perform these kinds of complex tasks. After a disaster, the robot must explore the whole unknown environment, locate the victims, and send back the information of the victims in a short time [9; 10; 11]. An important goal of the article is to make robots capable of performing search and rescue (SAR) tasks autonomously. However, at the present stage, all rescue robots are remotely controlled by operators in the actual rescue tasks [12]. In this way, it is easy to lose direction, bump into obstacles by mistake, or fall into negative obstacles [13]. In order to reduce the operation errors of rescue workers, it is necessary to improve the autonomy and intelligence of robots. High autonomy can greatly improve efficiency, and high intelligence can improve the performance of robots. As shown in Figure 1, the control of robots can be divided into three levels. Low-level control focuses on the control of motors and sensors. Middle-level control focuses on environment perception, Simultaneous Localization and Mapping (SLAM), and autonomous navigation. High-level control mainly involves smart decision-making of robots. In previous studies, most researchers focused on SLAM and navigation, corresponding to the middle-level control in Figure 1. In this level, search and rescue are not targeted at the beginning, and the robot generally needs to explore the environment globally, with low efficiency and lack of prior knowledge of SAR. Our research focuses on high-level control and smart decision-making of robots based on ontology in an SAR scenario. There are some typical study works and applications on robotic ontology. KnowRob [14; 15; 16] is an integrated knowledge management system in autonomous robots, which aims to build a knowledge base for indoor service robots. It consists of Web Ontology Language (OWL) language ontology and an extensible reasoning engine. However, it is suitable in the indoor service environment and difficult to extend to the application of SAR. In addition, OpenRobots Common-Sense ontology (ORO) [17; 18] integrates data from a variety of sources, such as sensors and human interaction. After being built in OWL, ORO is stored in the OpenJena ontology management library. However, ORO focuses on human-computer interaction and lacks support for SAR. Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) Ontology [19; 20; 21] is designed to describe and understand the complex environments of underwater unmanned robots and promote multi-robot cooperation. However, it is only built for underwater unmanned robots. The paper by [PERSON] and [PERSON] [22] applied the ontology methods to industrial production. The action schema (corresponding to the task ontology presented in our article) is built in the form of action recipes. When decomposing the high-level task, the program directly queries the action recipes, which lack extendibility and intelligence.