Abstract
The search and rescue (SAR) scenario is complex and uncertain where a robot needs to understand the scenario to make smart decisions. Aiming at the knowledge representation (KR) in the field of SAR, this paper builds an ontology model that enables a robot to understand how to make smart decisions. The ontology is divided into three parts, namely entity ontology, environment ontology, and task ontology. Web Ontology Language (OWL) is adopted to represent these three types of ontology. Through ontology and Semantic Web Rule Language (SWRL) rules, the robot infers the tasks to be performed according to the environment state and at the same time obtains the semantic information of the victims. Then, the paper proposes an ontology-based algorithm for task planning to get a sequence of atomic actions so as to complete the high-level inferred task. In addition, an indoor experiment was designed and built for the SAR scenario using a real robot platform—TurtleBot3. The correctness and usability of the ontology and the proposed methods are verified by experiments.
Highlights
Today, many theories and methods have emerged in the field of artificial intelligence (AI), which have been deeply applied in many domains
Our research focuses control and smart decision-making of robots basedofon ontology anontology on high-level control and smart decision-making robots basedinon in an search and rescue (SAR) scenario
Task ontology describes the task knowledge related to smart decision-making of robots, such as the ontology describes the task knowledge related to smart decision-making of robots, such as the task task decomposition and allocation task allocation via hierarchical structure
Summary
Many theories and methods have emerged in the field of artificial intelligence (AI), which have been deeply applied in many domains. As a typical method of AI, deep learning (DL). Has made a great breakthrough [1]. It is widely used in robotics [2,3]. DL is difficult to explain, which limits its application in some fields requiring knowledge reasoning. 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
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