Robotic Arm Control System Based on AI Wearable Acceleration Sensor

The position of mechanical arm in people’s life is getting higher and higher. It replaces the function of human arm, moving and moving in space. Generally, the structure is composed of mechanical body, controller, servo mechanism, and sensor, and some specified actions are set to complete according to the actual production requirements. The manipulator has flexible operation, good stability, and high safety, so it is widely used in industrial automation production line. With the development of science and technology, many practical production requirements for the function of the manipulator are more and more refined, especially in the high-end research field. For example, medical devices, automobile manufacturing, deep-sea submarines, and space station maintenance put forward higher requirements for it. In terms of miniaturization and precision, it can meet the needs of scientific research and actual production. But these are inseparable from the motion control system technology. This paper mainly introduces the research of manipulator control system based on AI wearable acceleration sensor, aiming to provide some ideas and directions for the research of wearable manipulator. This paper presents the research method of manipulator control system based on AI wearable acceleration sensor, including the establishment of manipulator kinematics model, common filtering algorithm, and PI algorithm of speed control system. It is used for the research and experiment of manipulator control system based on AI wearable acceleration sensor. The experimental results show that the average matching rate of the manipulator control system based on AI wearable acceleration sensor is as high as 88.89%, and the stability of the feature descriptor is high.

[1]  Sungho Lee,et al.  All-Day Mobile Healthcare Monitoring System Based on Heterogeneous Stretchable Sensors for Medical Emergency , 2020, IEEE Transactions on Industrial Electronics.

[2]  R. Vasudevan,et al.  Erratum: “A Continuum Model for Fiber-Reinforced Soft Robot Actuators” [ASME J. Mech. Rob., 2018, 10(2), p. 024501; DOI: 10.1115/1.4039101] , 2020 .

[3]  A. S. Jamaludin,et al.  Design of spline surface vacuum gripper for pick and place robotic arms , 2020 .

[4]  Chin-Hsing Kuo,et al.  Gravity Compensation Design of Planar Articulated Robotic Arms Using the Gear-Spring Modules , 2020 .

[5]  K. Polat,et al.  A Novel Wearable Real-Time Sleep Apnea Detection System Based on the Acceleration Sensor , 2020 .

[6]  Guangquan Zhou,et al.  [A Wearable System for Cervical Spondylosis Prevention Based on Artificial Intelligence]. , 2020, Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation.

[7]  Matthew Stone,et al.  That and There: Judging the Intent of Pointing Actions with Robotic Arms , 2020, AAAI.

[8]  Seong Young Ko,et al.  An Automated Extracorporeal Knot-tying System Using Two Concentric Tube Robotic Arms for Deployment through a 3-mm Port , 2020, International Journal of Control, Automation and Systems.

[9]  Chloe Gui,et al.  Brain controlled robotic arms - advancements in prosthetic technology , 2019 .

[10]  Nasser Kehtarnavaz,et al.  Data Augmentation in Deep Learning-Based Fusion of Depth and Inertial Sensing for Action Recognition , 2019, IEEE Sensors Letters.

[11]  M. Falconi IEEE, Control Systems Society, and Women in Engineering in Ecuador [Member Activities] , 2018, IEEE Control Systems.

[12]  Mohammad Hossein Jarrahi,et al.  Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making , 2018, Business Horizons.

[13]  Fabrice Jotterand,et al.  Artificial intelligence, physiological genomics, and precision medicine. , 2018, Physiological genomics.

[14]  Eric J Topol,et al.  Information and Artificial Intelligence. , 2018, Journal of the American College of Radiology : JACR.

[15]  Davide Morelli,et al.  Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device , 2017, Healthcare technology letters.

[16]  Huimin Lu,et al.  Brain Intelligence: Go beyond Artificial Intelligence , 2017, Mobile Networks and Applications.

[17]  Fei-Yue Wang Artificial Intelligence and Intelligent Transportation: Driving into the 3rd Axial Age with ITS , 2017, IEEE Intelligent Transportation Systems Magazine.

[18]  D. Hassabis,et al.  Neuroscience-Inspired Artificial Intelligence , 2017, Neuron.

[19]  Allaoui Tayeb,et al.  Artificial Intelligence-Based Fault Tolerant Control Strategy in Wind Turbine Systems , 2017 .

[20]  Sin-Jin Lin,et al.  Integrated Business Prestige and Artificial Intelligence for Corporate Decision Making in Dynamic Environments , 2017, Cybern. Syst..

[21]  A. Zaikin,et al.  Supervised learning in synthetic biology : student cells and teacher cells , 2016 .

[22]  Yang Gu,et al.  CRISPR-based genome editing and expression control systems in Clostridium acetobutylicum and Clostridium beijerinckii. , 2016, Biotechnology journal.

[23]  Dong-jin Choi,et al.  Software Architecture of a Wearable Device to Measure User's Vital Signal Depending on the Behavior Recognition , 2016 .

[24]  Abid Sarwar,et al.  Novel benchmark database of digitized and calibrated cervical cells for artificial intelligence based screening of cervical cancer , 2016, Journal of Ambient Intelligence and Humanized Computing.

[25]  Yunhui Liu,et al.  Automatic 3-D Manipulation of Soft Objects by Robotic Arms With an Adaptive Deformation Model , 2016, IEEE Transactions on Robotics.

[26]  David B. Camarillo,et al.  In Vivo Evaluation of Wearable Head Impact Sensors , 2015, Annals of Biomedical Engineering.

[27]  Peng Dai,et al.  Human Intelligence Needs Artificial Intelligence , 2011, Human Computation.

[28]  Lars Mönch,et al.  A Simulation Framework for the Performance Assessment of Shop-Floor Control Systems , 2003, Simul..

[29]  Bertram C. Bruce,et al.  Theoretical issues in reading comprehension : perspectives from cognitive psychology, linguistics, artificial intelligence, and education , 1980 .