[Retracted] Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network

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Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.

Section: Methodsmentioning

confidence: 99%

Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

Yang

Sun

et al. 2021

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“…With the continuous enrichment of computer control theory and deep learning and other related theories, the requirements for the movement accuracy of robots have gradually increased Material and Methods ( Huang et al, 2019 ; Miao et al, 2021 ). Robots with high motion accuracy can improve efficiency and avoid wasting resources when moving with high accuracy requirements, especially when completing transportation tasks in high-risk production areas ( Ma et al, 2020 ; Sun et al, 2020a ; Liu et al, 2021a ). HUR Sung Wook et al propose a new approach to efficient trajectory optimization that exploits the fact that the dynamics of a deterministic system is uniquely determined by the initial state and control over the time horizon of interest ( Hur et al, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

“…The concept of digital twin was proposed by Prof. Michael Grieves, who showed in his paper that virtual models and related subsystems are constructed to represent physical entities in a virtual information space and establish two-way dynamic connections through data from physical devices in real space, but due to technical limitations at that time, the concept of digital twin did not gain much attention ( Tan et al, 2020 ; Sun et al, 2021 ). In 2011, NASA applied the digital twin concept to the Apollo project to construct virtual bodies of space vehicles in virtual information space, and through the observation and analysis of the virtual bodies, the prediction and maintenance of the flight status of space vehicles were realized, and then the digital twin technology began to attract attention ( Tao et al, 2018 ; Liu Y et al, 2021a ). Many internationally renowned companies have already started to explore the application of digital twin technology in product design, manufacturing and service ( Sun et al, 2020c ; Tao et al, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Liu

Tao

et al. 2022

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102

Mobile robots have an important role in material handling in manufacturing and can be used for a variety of automated tasks. The accuracy of the robot’s moving trajectory has become a key issue affecting its work efficiency. This paper presents a method for optimizing the trajectory of the mobile robot based on the digital twin of the robot. The digital twin of the mobile robot is created by Unity, and the trajectory of the mobile robot is trained in the virtual environment and applied to the physical space. The simulation training in the virtual environment provides schemes for the actual movement of the robot. Based on the actual movement data returned by the physical robot, the preset trajectory of the virtual robot is dynamically adjusted, which in turn enables the correction of the movement trajectory of the physical robot. The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data. It provides a method to map learning in the virtual domain to the physical robot.

“…However, the industrial manipulator with four degrees-offreedom, the joints and links are not completely independent of each other. There are interactions, so the controllers for each part cannot be designed separately (Sun et al, 2020d;Liu et al, 2021e).…”

Section: Pd Control Of Gravity Compensationmentioning

confidence: 99%

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Liu

Yun

et al. 2022

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With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations, and random noise. Therefore, it is essential to improve the robust and accuracy of the controller. In this article, a self-tuning particle swarm optimization (PSO) fuzzy PID positioning controller is designed based on fuzzy PID control. The quantization and scaling factors in the fuzzy PID algorithm are optimized by PSO in order to achieve high robustness and high accuracy of the manipulator. First of all, a mathematical model of the manipulator is developed, and the manipulator positioning controller is designed. A PD control strategy with compensation for gravity is used for the positioning control system. Then, the PID controller parameters dynamically are minute-tuned by the fuzzy controller 1. Through a closed-loop control loop to adjust the magnitude of the quantization factors–proportionality factors online. Correction values are outputted by the modified fuzzy controller 2. A quantization factor–proportion factor online self-tuning strategy is achieved to find the optimal parameters for the controller. Finally, the control performance of the improved controller is verified by the simulation environment. The results show that the transient response speed, tracking accuracy, and follower characteristics of the system are significantly improved.