Real-Time Classification of Hand Motions Using Electromyography Collected from Minimal Electrodes for Robotic Control

In recent years, advances in human robot interaction (HRI) has shown massive potential for universal control of robots. Among them, electromyography (EMG) signals generated by motions of muscles have been identified as an important and useful source. Powered by recently emerged machine learning algorithms, real-time classification has been proved applicable to control robots. However, collecting EMG signals with minimum number of electrodes for real-time classification and robotic control is still a challenge. In this paper, we demonstrate that twenty five robotic commands in a robotic arm can be controlled in real time by using the EMG signals collected from only two pairs of active surface electrodes on each forearm of human subjects. To achieve this task, a variety of tested ML models for this classification were tested. Among them, the Gaussian Naïve Bayes (GNB) achieved an accuracy of >96%. This unprecedented level of classification accuracy of the EMG signals collected from the least number of active electrodes suggest that by combination of optimized electrode configuration and a suitable ML model, the capability of robotic control can be maximized.

Assistive Robots Designed for Eldery Care and Caregivers

As per the World Population prospects (19th revision), in 2019 every 11th person (11% of the population) was aged 65 or older and by 2050 every 6th person (16% of the world population) will be aged 65 or older. This rapid growth in people aged 65 and above has and will continue to pose some health management concerns, especially in the elderly with chronic ailments. The need for care provision for the elderly has provoked an exploration of various solutions to address elderly care management. Non-pharmacological interventions that utilize technology, such as robotics, are solutions that have proven to prolong independence and delay the admission of elderly into assisted care facilities. This paper will explore the various types of robotic solutions that are currently available to offer elderly care. This study will look at robotic solutions that are humanoid, animal-like, and robots that do not resemble humans or animals and their applications in elderly care. The various applications of robotics and the respective types of robots utilized in the provision of care in elderly care will be discussed as well.

Blockchain Technology with Applications to Distributed Control and Cooperative Robotics: A Survey

As a disruptive technology, blockchain, particularly its original form of bitcoin as a type of digital currency, has attracted great attentions. The innovative distributed decision making and security mechanism lay the technical foundation for its success, making us consider to penetrate the power of blockchain technology to distributed control and cooperative robotics, in which the distributed and secure mechanism is also highly demanded. Actually, security and distributed communication have long been unsolved problems in the field of distributed control and cooperative robotics. It has been reported on the network failure and intruder attacks of distributed control and multi-robotic systems. Blockchain technology provides promise to remedy this situation thoroughly. This work is intended to create a global picture of blockchain technology on its working principle and key elements in the language of control and robotics, to provide a shortcut for beginners to step into this research field.

A Real-Time Upper-Body Robot Imitation System

An upper-body robot imitation (UBRI) system is proposed and developed to enable the human upper body imitation by a humanoid robot in real time. To achieve the imitation of arm motions, a geometry-based analytical method is presented and applied to extracting the joint angles of the human and mapping to the robot. Comparing to the traditional numerical methods of inverse kinematic computations, the geometrical analysis method generates a lower computational cost and maintains good imitation similarity. To map the human head motions to the head of the humanoid robot, a face tracking algorithm is employed to recognize the human face and track the human head poses in real time. A hand extraction and hand state recognition algorithm is proposed to achieve the hand motion mapping. At last, the completion rate and similarity evaluation experiments are conducted to verify the effectiveness of the proposed UBRI system.

Design and Development of Pipe Inspection Robot Meant for Resizable Pipe Lines

Daily tasks mixed with the various applications in the robotic field. Since past, pipes have been used as the safe fluid transmitter. But gradually, these pipes affected by fatigue, cracking, leakage, sediment and breaking down. Also, sometimes humid environment and chemical products existing in the soil, causes rust and fatigue the pipes. All these problems lead to redundancy and impose high expenses for installation and maintenance. One of the recent inspection ways, is using the robot controller which leads to help and reduces the inspection time and preventive repairs activity. Besides, sometimes there are some unpleasant situation such as unfit pipes. It is obvious that in these conditions, doing inspection in toxic arena, narrow and meandering ways is impossible by human. So, designing a pipe inspection robot can be helpful in such circumstances. In this design, first, the former study, the way of operation, movement, mechanisms and advantages of each robot have studied. Then, by considering important parameters in designing, and sketching, making robot with the help of CATIA took place. This structure enjoys a regular mechanism design. It also has a proportional pipe diameter with the possibility of crossing through the slope routes. On the other hand, recording and processing of visual report, needs a camera and GUI toolbox written in Matlab. So image processing can help to exact investigation. One of the main difference of this research work over the various test on the platform, is hiring the mentioned toolbox which helps the operator to have the double investigation inside of the pipes. Moreover, the adjustable mechanism to pipe diameter, polyhedral movement and ascension power, relatively high efficiency in order to use frictional power and reducing repair and pause time are the advantages of this design. Also, observing the inner side of the pipe on the monitor, leads to reducing images. Moreover, its investigation by the introduced toolbox, causes more effective observation, more quickly diagnosis and analysis.

Research on Fault Diagnosis of Air Conditioner Based on Deep Learning

The essence of intelligent fault diagnosis is to classify the feature of faults by machine learning. It is difficult and key to extract fault characteristics of signals efficiently. The general feature extraction methods include time frequency domain feature extraction, Empirical Mode Decomposition (EMD), Wavelet Transform and Variational Mode Decomposition (VMD). However, these methods require a certain prior experience and require reasonable analysis and processing of the signals. In this paper, in order to effectively extract the fault characteristics of the air conditioner's vibration signal, the stacked automatic encoder (SAE) is used to extract the feature of air conditioner’s vibration signal, and the Softmax function is used to identify the air conditioner's working condition. The SAE performs unsupervised learning on the signal, and Softmax function performs supervised learning on the signal. The number of hidden layers and the number of hidden layer's nodes are determined through experiments. The effects of learning rate, learning rate decay, regularization, dropout, and batch size on the correct rate of the model in supervised learning and unsupervised learning are analyzed. Thereby realizing the fault diagnosis of the air conditioner. The recognition correct rate of deep learning model reached 99.92\%. The deep learning fault diagnosis method proposed in this paper is compared with EMD and SVM, VMD and SVM two kind of fault diagnosis methods.

Two New Beetle Antennae Search (BAS) Algorithms and Their Comparative Investigation

The Beetle Antennae Search (BAS) algorithm is a meta-heuristic search algorithm, which has efficient search capabilities. This paper presents two different variant algorithms based on the BAS algorithm, which are the BAS with fitness value (BASF) algorithm and BAS with local fast search (BASL) algorithm. The test results of 23 benchmark functions will be used to verify the reliability and accuracy of these algorithm. These benchmark functions include unimodal and multimodal high-dimensional functions, as well as fixed-dimensional multimodal functions. The test results show that the improved algorithm can search for the optimal solution globally and accurately with its own search strategy without environment parameters. Stability and accuracy are significantly improved while the calculation time does not change much.

Switching frequency control method based on short prediction horizon control algorithm of Quasi-Z source inverter

In order to overcome the problem of large inductance current ripple and poor output current quality caused by lower switching frequency in model predictive control (MPC). A short prediction horizon control algorithm is proposed, and the relationship between the prediction horizon and the switching frequency is analyzed, which also considered the influence of the switching frequency of the inverter. In the proposed algorithm the optimal vector is selected based on the historical switching state. Finally the proposed method can effectively reduce the inductor current ripple and improve the output current quality of the inverter, which is proved by simulation and experiment.

Forecasting of Chinese Hydropower Generation Using WASD-Neuronet

Hydropower resource is one of the renewable energy sources. With the increasing Chinese economy, people are paying much more attention to sustainable development. The increasing hydropower load is the basis of the development of power industry. Due to the characteristics of electrical energy, predicting the hydropower accurately is a potentially beneficial way to plan hydropower reasonably. This paper presents a neural network method to predict hydropower generation whose data is influenced by several factors such as social economic, population and climate. By using the past 52-year rough data, a 3-layer feedforward neuronet equipped with the weights and structure determination (WASD) method is constructed for the prediction of the Chinese hydropower generation in this paper. By processing mass of data, we could basically predict the hydropower generation using such a WASD neuronet. To a large extent, the trend of developing Chinese hydropower generation in the next years will keep growing.

Clustering Control of Multi-Objective Problems with Application to E-commerce

In the existing logistics distribution methods, the demand of customers is not considered. The goal of these methods is to maximize the vehicle capacity, which leads to the total distance of vehicles to be too long, the need for large numbers of vehicles and high transportation costs. To address these problems, a method of multi-objective clustering of logistics distribution route based on hybrid ant colony algorithm is proposed in this paper. Before choosing the distribution route, the customers are assigned to the unknown types according to a lot of customers attributes so as to reduce the scale of the solution. The discrete point location model is applied to logistics distribution area to reduce the cost of transportation. A mathematical model of multi-objective logistics distribution routing problem is built with consideration of constraints of the capacity, transportation distance, and time window, and a hybrid ant colony algorithm is used to solve the problem. Experimental results show that, the optimized route is more desirable, which can save the cost of transportation, reduce the time loss in the process of circulation, and effectively improve the quality of logistics distribution service.