A Flexible Semantic Ontological Model Framework and Its Application to Robotic Navigation in Large Dynamic Environments
Sung-Hyeon Joo, Sang-Hyeon Bae, Jun-Hyeon Choi, Hyunjin Park, Sangwook Lee, Sujeong You, Taeyoung Uhm, Jiyoun Moon, Tae‐Yong Kuc
Abstract
Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understanding the working environments beyond metric information and need countermeasures against various situations. In this paper, we propose a semantic navigation framework based on a Triplet Ontological Semantic Model (TOSM) to manage various conditions affecting the execution of tasks. The framework allows robots with different kinematics to perform tasks in indoor and outdoor environments. We define the TOSM-based semantic knowledge and generate a semantic map for the domains. The robots execute tasks according to their characteristics by converting inferred knowledge to Planning Domain Definition Language (PDDL). Additionally, to make the framework sustainable, we determine a policy of maintaining the map and re-planning when in unexpected situations. The various experiments on four different kinds of robots and four scenarios validate the scalability and reliability of the proposed framework.