A Soar-Based Space Exploration Algorithm for Mobile Robots

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 3; p. 426
• Main Authors: Luo, Fei, Zhou, Qin, Fuentes, Joel, Ding, Weichao, Gu, Chunhua
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19-03-2022; MDPI
• Subjects: Algorithms; Cognition & reasoning; cognitive computing; Decision making; Deep learning; Environment models; Heuristic; heuristic algorithms; Heuristic methods; Knowledge; Machine learning; Memory; Optimization; reinforcement learning; Robotics; Robots; Soar; Space exploration; Unmanned aerial vehicles; Workloads; cognitive computing; Soar; heuristic algorithms; space exploration; reinforcement learning
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24030426

Space exploration is a hot topic in the application field of mobile robots. Proposed solutions have included the frontier exploration algorithm, heuristic algorithms, and deep reinforcement learning. However, these methods cannot solve space exploration in time in a dynamic environment. This paper models the space exploration problem of mobile robots based on the decision-making process of the cognitive architecture of Soar, and three space exploration heuristic algorithms (HAs) are further proposed based on the model to improve the exploration speed of the robot. Experiments are carried out based on the Easter environment, and the results show that HAs have improved the exploration speed of the Easter robot at least 2.04 times of the original algorithm in Easter, verifying the effectiveness of the proposed robot space exploration strategy and the corresponding HAs.