Multi-Sensor Data Fusion-based Parallel Manipulator with IoT Monitoring Employing Machine Learning

• Published in: SN computer science Vol. 4; no. 2; p. 165
• Main Author: Shreyanth, S.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.03.2023; Springer Nature B.V
• Subjects: Access control; Accuracy; Actuator position; Advances in Computational Intelligence for Artificial Intelligence; Algorithms; Artificial intelligence; Computer engineering; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Control data (computers); Cost control; Data integration; Data integrity; Data Structures and Information Theory; Design; Information Systems and Communication Service; Internet of Things and Data Analytics; Machine Learning; Manipulators; Multisensor fusion; Neural networks; Original Research; Pattern Recognition and Graphics; Python; Robot arms; Robotics; Robots; Sensors; Software; Software Engineering/Programming and Operating Systems; Three dimensional models; Vision; Internet of things; Image processing; Multi-sensor data fusion; Machine learning; Sensor fusion; Signal processing; Convolution neural networks; Artificial intelligence; Intelligent systems; Robotics
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01600-4

A robotic parallel manipulator is implemented by employing embedded systems integrated with a set of sensors. More than one type of sensor are implemented together with the control input data from a human limb. Initially, a data set is collected on the map to certain equivalent actuations at the manipulator, and then using an appropriate machine learning algorithm, the control data value for the continuous position of the actuator is generated. A substantial amount of work is done on mapping the position of the limb to the actuator position by creating a three-dimensional model conventional 3D conversion, which is used on the boundary values of the input and output matched with a certain level of intermediate values, a proper training dataset for a machine learning algorithm can be created. The position of the manipulator is monitored by an IoT system and a set of sensors installed at the end. In addition, applied sciences transmits the possession date of the equator, and this information can be viewed remotely from any device connected to the internet.