Learning a Robust Hybrid Descriptor for Robot Visual LocalizationRead the full article
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.
Chief Editor Professor Yangmin Li is based at The Hong Kong Polytechnic University, Hong Kong. His research interests include robotics, mechatronics, control and automation.
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Emotional State Analysis Model of Humanoid Robot in Human-Computer Interaction Process
The traditional humanoid robot dialogue system is generally based on template construction, which can make a good response in the set dialogue domain but cannot generate a good response to the content outside the domain. The rules of the dialogue system rely on manual design and lack of emotion detection of the interactive objects. In view of the shortcomings of traditional methods, this study designed an emotion analysis model based on deep neural network to detect the emotion of interactive objects and built an open-domain dialogue system of humanoid robot. In affective state analysis language processing, language coding, feature analysis, and Word2vec research are carried out. Then, an emotional state analysis model is constructed to train the emotional state of a humanoid robot, and the training results are summarized.
A MultiModal Detection Method for UHV Substation Faults Based on Robot Inspection and Deep Learning
Aiming at the problem of multi-modal fault detection of different equipment in ultrahigh voltage (UHV) substations, a method for based on robot inspection and deep learning is proposed. First, the inspection robot is used to collect the image data of different devices in the station and the source data is preprocessed by standard image augmentation and image aliasing augmentation. Then, the HSV color space model based on saliency area detection is used to extract equipment defect areas, which improves the accuracy of defect image classification. Finally, the traditional YOLOv3 network is improved by combining the residual network and the K-means clustering algorithm, and the detailed flow of the corresponding detection method is proposed. The proposed detection method and the other three methods were compared and analyzed under the same conditions through simulation experiments. The results show that the detection accuracy and recall rate of the method proposed in this study are the largest, which are 95.9% and 91.3%, respectively. The average detection accuracy under multiple intersection ratio thresholds is also the highest, and the performance is better than the other three comparison algorithms.
A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots
This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. Furthermore, word segmentation and word vector transformation are carried out through Jieba word segmentation tool and Skip-gram model, the semantic information between different data is deeply characterized, and the problem of very high vector sparsity is solved. Then, the corresponding features are obtained by using the feature extraction ability of DNN’s in-depth learning. On this basis, the user’s score on tourism service items is predicted through the model until a personalized recommendation list is generated. Finally, through simulation experiments, the recommendation accuracy and average reciprocal ranking of the proposed algorithm model and the other two algorithms in three different databases are compared and analyzed. The results show that the overall performance of the proposed algorithm is better than the other two comparison algorithms.
The Flight Mechanism of a Bird-like Flapping Wing Robot at a Low Reynolds Number
The flight mechanism of a bird-like flapping wing robot at a low Reynolds number was studied in this study for improving the robot performances. Both the physical model and the kinematic model were first established. The dynamic model of the robot at a low Reynolds number was built with the RANS (Reynolds-averaged Navier-Stokes) equations and the Spalart-Allmaras turbulence model. The flight experiments were carried out and the results were discussed. Lift and drag coefficient curves show that it generates upward lift and forward thrust in the phase that the wing flaps downwards, the rate of the coefficient curves is the biggest when the flapping direction changes. Pressure contours indicate that small vortexes with high pressure values appear at the wing edges. There are four velocity vortex groups in total at the front and back of the wing in the velocity contours. Some methods for improving the robot flight efficiency and the robot strength as well as the stitching position of the robot skin have been obtained from the above results. The methods provide the important guidance for the stable flights of the flapping wing robot with the high efficiency.
Support Vector Machine and Granular Computing Based Time Series Volatility Prediction
With the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining algorithms. In this paper, three different time-series information granulation methods are proposed for time-series information granulation from both time axis and theoretical domain: time-series time-axis information granulation method based on fluctuation point and time-series time-axis information granulation method based on cloud model and fuzzy time-series prediction method based on theoretical domain information granulation. At the same time, the granulation idea of grain computing is introduced into time-series analysis, and the original high-dimensional time series is granulated into low-dimensional grain time series by information granulation of time series, and the constructed information grains can portray and reflect the structural characteristics of the original time-series data, to realize efficient dimensionality reduction and lay the foundation for the subsequent data mining work. Finally, the grains of the decision tree are analyzed, and different support vector machine classifiers corresponding to each grain are designed to construct a global multiclassification model.
An Obstacle Detection and Distance Measurement Method for Sloped Roads Based on VIDAR
Environmental perception systems can provide information on the environment around a vehicle, which is key to active vehicle safety systems. However, these systems underperform in cases of sloped roads. Real-time obstacle detection using monocular vision is a challenging problem in this situation. In this study, an obstacle detection and distance measurement method for sloped roads based on Vision-IMU based detection and range method (VIDAR) is proposed. First, the road images are collected and processed. Then, the road distance and slope information provided by a digital map is input into the VIDAR to detect and eliminate false obstacles (i.e., those for which no height can be calculated). The movement state of the obstacle is determined by tracking its lowest point. Finally, experimental analysis is carried out through simulation and real-vehicle experiments. The results show that the proposed method has higher detection accuracy than YOLO v5s in a sloped road environment and is not susceptible to interference from false obstacles. The most prominent contribution of this research work is to describe a sloped road obstacle detection method, which is capable of detecting all types of obstacles without prior knowledge to meet the needs of real-time and accurate detection of slope road obstacles.