Nonlinear Oscillations: Exact Solutions and their Approximations

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Modelling and Control of Robot Manipulators

Abstract Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10 18 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community. 

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2022 roadmap on neuromorphic computing and engineering

This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication whenever normal verbal communication is not possible or not desirable. In this review, we focus on the first case and present latest SSI research aimed at providing new alternative and augmentative communication methods for persons with severe speech disorders. SSIs can employ a variety of biosignals to enable silent communication, such as electrophysiological recordings of neural activity, electromyographic (EMG) recordings of vocal tract movements or the direct tracking of articulator movements using imaging techniques. Depending on the disorder, some sensing techniques may be better suited than others to capture speech-related information. For instance, EMG and imaging techniques are well suited for laryngectomised patients, whose vocal tract remains almost intact but are unable to speak after the removal of the vocal folds, but fail for severely paralysed individuals. From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. Despite considerable advances in recent years, most present-day SSIs have only been validated in laboratory settings for healthy users. Thus, as discussed in this paper, a number of challenges remain to be addressed in future research before SSIs can be promoted to real-world applications. If these issues can be addressed successfully, future SSIs will improve the lives of persons with severe speech impairments by restoring their communication capabilities.

Silent Speech Interfaces for Speech Restoration: A Review

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.

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

The myoelectric interfaces are being used in rehabilitation technology, assistance and as an input device. This review focuses on an insightful analysis of the data acquisition system of EMG signals from these interfaces. According to applications reported in research articles of the last five years, the properties of the sensors, the number of channels, the pre-processing of the EMG signal, as well as the software and hardware used were identified. This analysis was performed for the following applications: monitoring of muscular activation for rehabilitation, muscle activation plans, and identification of possible pathologies, exoskeletons, electric of wheelchairs, prosthetics control, myoelectric bracelets, handwriting recognition and silent speech recognition. The results presented in this review become a guide of recommendations for the myoelectric signal processing according to the application of the interface. The main developments, degrees of research and open challenges are also presented in this direction.

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Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review

This paper describes the kinematics and dynamics modelling of a mechanical system consisting of a spherical inverted pendulum whose base is mounted on a parallel planar mechanism, better known as a five-bar mechanism. The whole mechanism has four degrees of freedom, but it has only two actuators and so it is an under-actuated system. The nonlinear dynamics model of the complete system is first obtained using a non-minimal set of generalized coordinates, and then a reduced equivalent model is computed. To validate this approach, the reduced model is linearized and simulations are carried out, showing the stabilization of the system with a simple LQR controller. Experimental results on an academic prototype are also presented.

https://journals.sagepub.com/doi/pdf/10.5772/60027

Modelling and Control of a Spherical Inverted Pendulum on a Five-bar Mechanism

The so-called Hexapod robot is a parallel manipulator produced by Quanser Inc. This mechanism is constituted of two triangular rigid bodies, one fixed (the base), and one mobile (the platform) joined together by six kinematic chains (the legs) conforming a structure of the type known as 6-3-PUS. This paper first reports a procedure for computing the forward and inverse kinematics models of the Hexapod robot. After that, we show how to implement an operational space tracking controller which employs a two-loop structure: a resolved motion rate controller (RMRC) in the outer loop and a joint-velocity PI controller in the inner loop. Experimental results of the real-time implementation are included.

Kinematic modeling and control of the Hexapod parallel robot

Modeling and analyzing the dynamics of mechanical systems is important for purposes of simulation, design and control. In the case of parallel manipulators, dynamics modeling becomes a complex task mainly due to the constraints imposed by the closed kinematic chains, and several methods have been proposed for solving that problem. Some authors suggest first using a non-minimal set of coordinates and then computing a minimal dynamics model by means of the so-called projection method. This paper reports the use of such procedure for obtaining the dynamics model of a particular parallel mechanism of the type known as 6-3-
 $$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\text{P}}\text{US}$$
 . To compute the non-minimal dynamics we employed the Euler-Lagrange formulation. The computed kinematics and dynamics models were validated via numerical simulations. This modeling approach can be employed for similar mechanisms.

Kinematics and dynamics modeling of the 6-3- $$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\text{P}}\text{US}$$ P − US -type Hexapod parallel mechanism

Since their appearance in the early 1960’s, industrial robots have gained wide popularity as essential components in the construction of automated systems. Reduction of manufacturing costs, increase of productivity, improvement of product quality standards, and the possibility of eliminating harmful or repetitive tasks for human operators represent the main factors that have determined the spread of the robotics technology in the manufacturing industry. Industrial robots are suitable for applications where high precision, repeatability and tracking accuracy are required. These facts give a great importance to the analysis of the actual control schemes of industrial robots (Camarillo et al., 2008). It is common to specify the robotic tasks in terms of the pose (position and orientation) of the robot’s end–effector. In this sense, the operational space, introduced by O. Khatib (1987), considers the description of the end–effector’s pose by means of a position vector, given in Cartesian coordinates, and an orientation vector, specified in terms of Euler angles. On the other hand, the motion of the robot is produced by control signals applied directly to the joint actuators; further, the robot configuration is usually measured through sensors located in the joints. These facts lead to consider two general control schemes:

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Israel Soto

Papers

Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review

Modelling and Control of a Spherical Inverted Pendulum on a Five-bar Mechanism

Kinematic modeling and control of the Hexapod parallel robot

Kinematics and dynamics modeling of the 6-3- $$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\text{P}}\text{US}$$ P − US -type Hexapod parallel mechanism

Motion Control of Industrial Robots in Operational Space: Analysis and Experiments with the PA10 Arm