Sensing-Based Self-Reconfigurable Decision-Making Mechanism for Autonomous Modular Robotic System

• Published in: IEEE sensors journal Vol. 20; no. 13; pp. 7097 - 7106
• Main Authors: Majed, Aliah, Harb, Hassan, Nasser, Abbass, Clement, Benoit, Reynet, Olivier
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Automatic; Computer simulation; Data collection; Data simulation; Data storage; data storage reduction; Decision analysis; Decision making; Detectors; Engineering Sciences; Environmental monitoring; Fuzzy logic; Memory; Modular robotic system; Modular systems; Morphology; multi-detectors; Reconfiguration; Robot sensing systems; Robotics; self-reconfiguration; Service robots; Signal and Image processing
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.2979280

Nowadays, robotic technology finds its way quickly across industries affecting business and people lives. In addition, the rapid growth in communication technologies leads to a new generation of robotics called as Modular Robotic System (MRS). Basically, MRS is autonomous kinematic machine with variable morphology, structure, and functionality. The detectors represent the eyes of the MRS that collect data about different environments and states, while the controller forms the brain of the MRS that must analyze the collected data and take decision about the suitable reconfiguration morphology. However, the huge number of data sensed by the detectors provides two main challenges for MRS; first, it overloads the limited storage of the MRS and, second, it complicates the self-reconfiguration decision required to change its shape according to the monitored environment. In this paper, we propose a sensing-based processing mechanism for data storage and decision making in modular robotic systems. Our mechanism consists of two phases: data storage reduction and self-reconfiguration. The first phase uses aggregation process in order to eliminate redundant data collected thus, reduce the amount of data need to be stored in MRS. The second phase uses the fuzzy logic model and allows MRS to be self-reconfigurable by taking the right decision about the desired shape. The efficiency of our mechanism is demonstrated based on real data simulation while important results have been obtained in terms of data storage and self-reconfiguration decision.