In this study, prosthesis performance was examined in the direction of prosthesis comfort, which may be incorporated into clinical practice as considerations for the fabrication of patient-specific prostheses. The need to produce patient-specific prosthetics is very germane to assist in orthopedic and trans-radial amputation medicine. The prosthesis makes use of a relatively simple brain-computer interface that receives electroencephalogram (EEG) signals as input and drives actuators connected to cables to actuate the 3D-printed fingers and the wrist. Both mechanical and electrical simulations were carried out to investigate the response to loading conditions, after which sensitivity analysis was conducted to validate the prosthesis performance.

Ebenezer Olubunmi Ige

Adedotun Adetunla

Adedamola Awesu

Oluwaseun K. Ajayi

Sensitivity Analysis of a Smart 3D-Printed Hand Prosthetic

To better solve the problems associated with optimal pathfinding and dynamic obstacle avoidance in the path planning of mobile robots, a hybrid path planning scheme combining modified gray wolf optimization (MGWO) and situation assessment mechanism is proposed. Firstly, a MGWO algorithm is proposed to plan a global path. Secondly, different situational factors for robots in different regions are extracted from the fusion results of 2D laser measurements and image data, and a Bayesian network model of robot action selection is established. Then, the situational factors of the robot are used as evidence for reasoning. Based on the posterior probability value in the inference result, the grid to be moved is selected and the traveling direction of the robot is adjusted in order to take advantage of both global path planning and local dynamic obstacle avoidance. The simulation results show that the proposed MGWO has better optimization performance. When combined with a situation assessment mechanism, it realizes dynamic obstacle avoidance while keeping the path length as short as possible.

Yilun Liu

Xiaoming Li

A Hybrid Mobile Robot Path Planning Scheme Based on Modified Gray Wolf Optimization and Situation Assessment

With the development of social information technology, intelligent community as a new way of life is changing people&#x2019;s lives step by step. The community as a unit of residence in China has developed quite maturely, but the community services are not perfect, and the management is relatively mechanized. Therefore, improving work efficiency and enriching and perfecting community service is an increasingly important issue. Data mining is to extract valuable information by analyzing the internal connections, rules, and patterns of these data so as to provide more favorable decision support for community managers and provide users with more humane and modern community intelligence services. This research focuses on the implementation of community data processing systems based on data mining. Firstly, data preprocessing analysis is carried out, the realization of data storage and cache is studied, the process and characteristics of cluster analysis are studied in detail, and the simulation results of a community data processing system based on data mining are summarized. This study uses data mining technology to dig out the daily consumption data of users in the community mall, cluster the data, and analyze the consumption situation and consumption types of different types of users. In this study, data mining technology is used to mine the fault repair data of the community, and classification prediction technology is used to classify and predict different types of faults so as to help managers troubleshoot problems existing in the community environment.

Li Li

Implementation of a Community Data Processing System Based on Data Mining

Aiming at the problems of high computing energy consumption and long time in traditional UAV-assisted edge computing research work, a computing resource allocation strategy using biological evolutionary algorithms in UAV-assisted mobile edge computing is proposed by introducing UAV swarms and genetic algorithms. Firstly, it analyzes the communication model for uplink transmission, the calculation model for local computing tasks, and UAV to perform computing tasks. Secondly, the objective function and overall model of system are constructed by comprehensively considering multiple constraints. Then, improved genetic algorithm is introduced into the model. On the basis of data encoding, crossover, mutation, and termination operations, the optimization performance of algorithm is greatly improved by multiple iterations of fitness function. Finally, the energy consumption of proposed algorithm and other two algorithms under the same number of iterations are compared and analyzed by simulation experiments. The experimental results show that the optimal solution, average, and variance of proposed algorithm for energy consumption are 52.354, 50.326, and 0.224, respectively, and its performance is better than other two comparison algorithms.

Li Wang

Xiang Yao

Zhenqi Yuan

Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing

An improved method is proposed in this investigation to solve the problems of poor path quality and low navigation efficiency of the Informed-RRT<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.04990005pt" id="M2" height="10.1524pt" version="1.1" viewBox="-0.0498162 -10.1025 6.17869 10.1524" width="6.17869pt"><g transform="matrix(.0091,0,0,-0.0091,0,-5.741)"><use xlink:href="#g50-43"/></g></svg> algorithm in robot autonomous navigation. First, the greedy algorithm is introduced in the path planning procedure. When a new node is obtained, it will be judged whether it can directly reach the target point. Second, the search scope of the potential optimal parent node becomes the constructed path, instead of the node tree, which reduces the number of nodes to be searched and improves the navigation efficiency. Combined with the dynamic window approach (DWA), the improved algorithm is utilized to simulate the autonomous navigation process of the robot based on the Robot Operating System (ROS) platform. The simulation results show that compared with the original algorithm, the length of the global path is reduced by 5.15%, and the time of planning path and autonomous navigation is shortened by 78.34% and 21.67%, respectively.

Jun Dai

Dongfang Li

Junwei Zhao

Yanqin Li

Autonomous Navigation of Robots Based on the Improved Informed-RRT<svg  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"   style="vertical-align:-0.04990005pt" height="10.1524pt" version="1.1" viewBox="-0.0498162 -10.1025 6.17869 10.1524" width="6.17869pt"><g transform="matrix(.0091,0,0,-0.0091,0,-5.741)"><path id="g50-43" d="M486 158C486 177 478 202 466 220C413 228 386 236 336 262C386 288 413 297 466 304C478 323 486 347 485 366C470 376 444 381 422 380C389 338 368 319 321 288C323 345 329 372 349 422C339 442 322 461 305 470C289 461 271 442 262 422C281 372 287 345 290 288C243 319 222 338 189 380C167 381 142 376 125 366C125 347 133 322 145 304C198 296 225 288 275 262C225 236 198 227 145 220C133 201 125 177 126 158C141 148 167 143 189 144C222 186 243 205 290 236C288 179 282 152 262 102C272 82 289 63 306 54C322 63 340 82 350 102C330 152 324 179 321 236C368 205 390 186 422 144C444 143 470 148 486 158Z" /></g></svg> Algorithm and DWA

Aiming at the insufficient feature extraction in the expression feature extraction stage of traditional convolutional neural network and the misclassification of mislabeled samples, an expression recognition and robot intelligent interaction method using deep learning is proposed. First, in image preprocessing, the dimension of the color image is reduced by image gray adjustment to reduce the amount of calculation, the shadow interference is eliminated by the average method, and the image is enhanced by histogram equalization. Second, multichannel convolution is used to replace the single convolution size in the second convolution layer in AlexNet, the Global Average Pooling layer is introduced to replace the fully connected layer, and Batch Normalization is introduced to improve the feature extraction ability of the model and avoid gradient explosion. Finally, the Focal Loss is improved by setting the probability threshold to avoid the impact of mislabeling samples on the classification performance of the model. The experimental results show that the recognition accuracy of the model on FER2013 data set is 98.36&#x0025;. The effectiveness of the algorithm is verified on the intelligent interactive system of service robot based on expression recognition. Compared with other expression recognition methods, the proposed method can extract more expression features and recognize facial expression more accurately.

Yifeng Zhao

Deyun Chen

Expression Recognition Using Improved AlexNet Network in Robot Intelligent Interactive System

Aiming at the problems that the traditional model is difficult to extract information features, difficult to learn deep knowledge, and cannot automatically and effectively obtain features, which leads to the problem of low recommendation accuracy, this paper proposes a personalized tourism route recommendation model of intelligent service robot using deep learning in a big data environment. Firstly, by crawling the relevant website data, obtain the basic information data and comment the text data of tourism service items, as well as the basic information data, and comment the text data of users and preprocess them, such as data cleaning. Then, a neural network model based on the self-attention mechanism is proposed, in which the data features are obtained by the Gaussian kernel function and node2vec model, and the self-attention mechanism is used to capture the long-term and short-term preferences of users. Finally, the processed data is input into the trained recommendation model to generate a personalized tourism route recommendation scheme. The experimental analysis of the proposed model based on Pytorch deep learning framework shows that its Pre@10, Rec@10 values are 88&#x0025; and 83&#x0025;, respectively, and the mean square error is 1.537, which are better than other comparison models and closer to the real tourist route of the tourists.

Xiang Huang

Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment

Effective noise removal has become a hot topic in image denoising research while preserving important details of an image. An adaptive threshold image denoising algorithm based on fitting diffusion is proposed. Firstly, the diffusion coefficient in the diffusion equation is improved, and the fitting diffusion coefficient is established to overcome the defects of texture detail loss and edge degradation caused by excessive diffusion intensity. Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. The training speed of the model is accelerated and the convergence of the algorithm is improved. Combined with batch standardization and residual learning technology, the image denoising network model based on deep residual learning of the convolutional network is designed with better denoising performance. Finally, the algorithm is compared with other excellent denoising algorithms. From the comparison results, it can be seen that the improved denoising algorithm in this paper can also improve the detail restoration of denoised images without losing the sharpness. Moreover, it has better PSNR than other excellent denoising algorithms at different noise standard deviations. The PSNR of the new algorithm is greatly improved compared with the classical algorithm, which can effectively suppress the noise and protect the image edge and detail information.

Shaobin Ma

Lan Li

Chengwen Zhang

Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning

About this journal

<p><em>Journal of Robotics </em>publishes original research articles as well as review articles on all aspects of automated mechanical devices, from their design and fabrication, to testing and practical implementation.</p>


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<p>Chief Editor&nbsp;Professor Yangmin Li is based at The Hong Kong Polytechnic University, Hong Kong. His research interests include robotics, mechatronics, control and automation.</p>


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Journal of Robotics - Table of contents 2022

title

Journal of Robotics publishes original research articles as well as review articles on all aspects of automated mechanical devices, from their design and fabrication, to testing and practical implementation.

Journal of Robotics