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Optimal TOA-Based Sensor-Anchor-Source Geometries and Estimation Bounds for Simultaneous Sensor and Source Localization

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IEEE Transactions on Signal Processing 2025年 73卷 1727-1743页

作者： Xu, Sheng Zhu, Bing Wu, Xinyu Doğançay, Kutluyil Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China University of South Australia UniSA STEM Mawson LakesSA5095 Australia

This paper focuses on optimal time-of-arrival (TOA) sensor placement for simultaneous sensor and source localization (SSSL) with the help of selected anchors at known positions in the environment. Firstly, the problem of sensor placement for SSSL is analyzed and formulated as an optimization task based on the approximate Cramér-Rao lower bound (CRLB), which is an approximation of the intractable true CRLB. Secondly, by minimizing the trace of the approximate CRLB, the optimal accuracy bounds for the estimated sensor and source positions are derived, which can serve as a useful evaluation metric for other studies. Thirdly, a systematic solution including both the analytical and algebraic methods is proposed to obtain the optimal sensor-anchor-source geometries for achieving the approximate bounds simultaneously. Significantly, the analytical sensor placement approach can quickly offer an optimal placement for some special cases, and the algebraic algorithm can provide a (sub-)optimal solution numerically for the general case. Furthermore, theoretical guidance for placing anchors in the localization area is provided. Finally, the theoretical findings and proposed algorithms are verified by computer simulations and experimental studies, demonstrating that the optimized sensor positions yield accurate performance. The results in this paper can be utilized as an evaluation tool and a performance improvement guidance for practical SSSL problems. © 1991-2012 IEEE.

关键词： Time of arrival

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Irrelevant Locomotion Intention Detection for Myoelectric Assistive Lower Limb Robot Control

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 655-665页

作者： Song, Xiaoyu Liu, Jiaqing Pan, Heng Rao, Haotian Wang, Can Wu, Xinyu Southern University of Science and Technology Shenzhen518055 China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

In this study, we propose a robust myoelectric intention recognition framework to recognize human locomotion mode and detect irrelevant locomotion intention. The framework is integrated into the control system of the lower limb exoskeleton robot for experimental validation. Most conventional electromyography(EMG) intention detection methods aim to accurately detect the target motion intentions but ignore the possible effects of irrelevant intentions. In traditional action intention recognition strategies, most researchers did not consider entering irrelevant action intentions into the model during training. Therefore, when using a classification model, if irrelevant action intentions are input, the model will still recognize it as a type of target action intention. That can lead to incorrect recognition results, which will cause the robot to perform wrong movements and pose a safety risk to the wearer. To detect and reject irrelevant motion intentions, we first used the dual-purpose autoencoder-guided temporal convolution network (DA-TCN) to obtain discriminative features of the surface EMG signal. Autoencoders (AE)/Variable Autoencoders (VAE) are then trained for each of the seven deep features of the target motion intention. In addition, irrelevant motion intentions are detected according to the value of their reconstruction error. The recall rate of this method for the detection of irrelevant motion intentions exceeds 99% and the accuracy rate exceeds 99%.At the same time, we replaced the TCN with the LSTM model and compared the performance of the two after adding irrelevant motion discrimination. We collected data on seven goals and three unrelated motor intentions from seven experimenters for testing and completed an online experimental validation. The motion recognition accuracy of all the experimenters can be maintained above 86%. © 2018 IEEE.

关键词： Limbs Legged locomotion Exoskeletons Robots Image color analysis Convolution Stairs Training Feature extraction Autoencoders

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Image Data Augmentation through Generative Adversarial Networks for Waste Sorting

Image Data Augmentation through Generative Adversarial Netwo...

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IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

作者： Alberto Bacchin Fabio Marangoni Alberto Gottardi Emanuele Menegatti Department of Information Engineering Intelligent Autonomous System Lab University of Padova Padua Italy IT+Robotics srl Vicenza Italy

ISBN: (数字)9798331516857

ISBN: (纸本)9798331516864

The growing volumes of solid waste present a significant challenge for sustainable management. An efficient robotics system for sorting waste materials is essential to improving recycling and contamination removal, but it is often limited by the extreme variation of items to recognize in a cluttered and dirty environment. To bring robots in such scenario, the system must be able to recognize and manipulate different objects, adapting to a high degree of variability. A system based on deep learning can achieve high performance and fulfill these requirements. However, learning models require extensive labeled data, which limits their applicability in this context. Indeed, the complexity of the real-world environment presents a significant challenge to effective data collection, highlighting the importance of data augmentation techniques for creating a suitable dataset for training models for object recognition in this context. To address this challenge, our study investigates the use of Generative Adversarial Networks (GANs) for synthetic data generation in waste-sorting systems. GANs are employed to produce synthetic images of waste streams with corresponding labels that accurately reflect the complexity and diversity of real-world waste. A primary con-cern in synthetic data generation is ensuring alignment between generated images and their labels. To address this, we introduce a novel method for controlling the GAN generation process, which enforces semantic coherence and preserves the intended structure of the labeled data. The experiments demonstrate that semantic segmentation models trained on datasets augmented with these synthetic images perform better in the semantic segmentation of waste.

关键词： Waste materials Semantic segmentation Semantics Diversity reception Generative adversarial networks Data augmentation Generators Data models Sorting Synthetic data

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Complete Gait Phases Recognition Based on Muscle Synergy Using PSO-CNN-LSTM Algorithm

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IEEE Sensors Journal 2025年第10期25卷 16775-16786页

作者： Zhang, Kewen Li, Xiaoling Chen, Xin Yu, Longjie Jin, Yinan Fan, Bingfei Du, Mingyu Bao, Guanjun Wu, Xinyu Cai, Shibo Zhejiang University of Technology College of Mechanical Engineering Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education Hangzhou China Zhejiang University of Technology College of Mechanical Engineering Hangzhou China ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship Fuyang District Hangzhou311400 China Chinese Academy of Sciences Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen China

Accurately recognizing gait phases, by applying proper instrumentation and measurement, is significant in walking rehabilitation training for patients with impaired mobility. In this study, seven phases of complete stand-walk-stand cycle as well as continuous daily walking were recognized based on muscle synergy and PSO-CNN-LSTM model. Firstly, the complete stand-walk-stand walking cycle were divided into starting phase, terminal swing phase, loading response phase, mid-stance phase, terminal stance phase, initial swing phase, and stopping phase, based on the features of surface electromyography (sEMG) collected by portable sEMG acquisition system and measured motion data of lower limb. Secondly, a muscle weight matrix and an activation sequence matrix were calculated by using non-negative matrix factorization (NMF). Finally, a PSO-CNN-LSTM network was designed to recognize the complete stand-walk-stand cycle by applying above mentioned matrices as input. Fourteen subjects volunteered to perform linear walking experiments under procedure to verify the feasibility of the proposed approach by comparing results with other classifiers and features. Experimental results show that the proposed approach was capable of achieving an average recognition accuracy of up to 85.384%. This work will offer promising gait recognition method for rehabilitation robots to achieve natural and flexible human-robot interaction. © 2001-2012 IEEE.

关键词： Non-negative matrix factorization

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Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework

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Cyborg and Bionic systems 2025年 6卷 0250页

作者： Luo, Mingxiang Yin, Meng Li, Jinke Li, Ying Kobsiriphat, Worawarit Yu, Hongliu Xu, Tiantian Wu, Xinyu Cao, Wujing Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Mechanical and Electrical Engineering Shenzhen Polytechnic University Shenzhen China National Metal and Materials Technology Center National Science and Technology Development Agency of Thailand Khlong Nueng Thailand University of Shanghai for Science and Technology Shanghai China

Lateral walking exercise is beneficial for the hip abductor enhancement. Accurate gait recognition and continuous hip joint angle prediction are essential for the control of exoskeletons. We propose a dual-task learning framework, the "Twin Brother" model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals. The EMG signals of 6 muscles from 10 subjects during lateral walking were collected. Four gait phases were recognized, and the hip angles of both legs were continuously estimated. The sliding window length of 250 ms and the sliding increment of 3 ms were determined by the requirements of response time and recognition accuracy of the real-time system. We compared the performance of CNN-LSTM, CNN, LSTM, support vector machine, NN, K-nearest neighbor, and the "Twin Brother" models. The "Twin Brother" model achieved a recognition accuracy (mean ± SD) of 98.81% ± 0.14%. The model’s predicted root mean square error (RMSE) for the left and right hip angles are 0.9183° ± 0.024° and 1.0511° ± 0.027°, respectively, where the R2 are 0.9853 ± 0.006 and 0.9808 ± 0.008. The accuracy of recognition and estimation are both better than comparative models. For gait phase percentage prediction, RMSE and R2 predicted by the model can reach 0.152° ± 0.014° and 0.986 ± 0.011, respectively. These results demonstrate that the method can be applied to lateral walking gait recognition and hip joint angle prediction. Copyright © 2025 Mingxiang Luo et al.

关键词： Convolutional neural networks

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Sentiment analysis and text summarization of online reviews: A survey

Sentiment analysis and text summarization of online reviews:...

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2016 International Conference on Communication and Signal Processing, ICCSP 2016

作者： Gupta, Pankaj Tiwari, Ritu Robert, Nirmal Robotics and Intelligent System Lab ABV-IIITM Gwalior India

ISBN: (纸本)9781509003969

Sentiment analysis and text summarization has evoke the interest of many scientists and researchers in last few years, since the textual data has become useful for many real world applications and problems. Sentiment analysis is a machine learning approach in which machine learns and analyze the sentiments, emotions etc about some text data like reviews about movies or products. These reviews are increasing day by day, due to which summarization of reviews comes in role where summarized form of text in needed, which provides useful information from the large number of reviews. It is very difficult for a human being to extract useful data or summarize it from the very large document. In Text summarization, importance of sentences is decided based on linguistic features of sentences. This paper provides the comprehensive overview of recent and past research on sentiment analysis and text summarization and provides excellent research queries and approaches for future aspects. © 2016 IEEE.

关键词： Sentiment analysis

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Building topological map using omnidirectional image matching

Building topological map using omnidirectional image matchin...

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International Conference on system Science and Engineering

作者： Gu, Shuang Chen, Qijun Robotics and Intelligent System Lab. Tongji University Shanghai China

ISBN: (纸本)9781612844718

This paper addresses the problem of constructing a globally consistent topological map by a vision-based mobile robot in an indoor environment. The vertices of the map represent the appearance of certain places and the edges represent the relative position between two vertices. An omnidirectional image matching algorithm is proposed to solve two problems: detecting loop closure and calibrating odometry feedback, which uses matched radial lines in successive frames to estimate the rotational angles of the robot. The map construction task consists of two steps: when no loop closure is detected, the robot use image matching results to calibrate the odometry data. After loop closure is detected, an energy-based spring model is used to refine the structure of the global map by minimize the energy of the model. Experimental evidence of the correctness of this method is given in unmodified indoor environments. © 2011 IEEE.

关键词： Image matching

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When Shannon Meets Nonstochastic Information: Communication and Estimation

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IEEE Transactions on Automatic Control 2025年

作者： Zhang, Wentao Zuo, Zhiqiang Zhao, Rui Wang, Yijing Hu, Guoqiang Zhang, Hui Wang, Yaonan Hunan University School of Robotics Changsha410082 China Nanyang Technological University Continental-NTU Corporate Lab Singapore639798 Singapore Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore National Engineering Research Center for Robot Visual Perception and Control Changsha410082 China Hunan University College of Electrical and Information Engineering Changsha410082 China

This notes focuses on the issue of whether or not the state of a plant can be estimated when the inputs of communication channel involve noise and disturbance simultaneously. It turns out to calculate the maximum capacity of stationary memoryless multiple quasi-access channel under nonstochastic information theory. It is shown that the maximum information rate over such a channel is determined by a convex hull spanned by the zero-error capacities which are specified on the underlying sample spaces. Moreover, the state of a plant could be estimated provided that the maximal information rate is strictly greater than the topology entropy. © 1963-2012 IEEE.

关键词： Communication channels (information theory)

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Cooperative Multi-robot Target Searching and Tracking Using Velocity Inspired Robotic Fruit Fly Algorithm

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SN Computer Science 2021年第6期2卷 474页

作者： Garg, Vikram Robotics and Intelligent System Lab ABV- Indian Institute of Information Technology and Management Gwalior India

Target searching and tracking using a swarm of robots is a classical problem in the domain of robotics. The cooperation among the swarm robots has got increasing attention lately due to the different versions of the problem and complex environment. In this paper, a centralized control algorithm is proposed which utilizes cooperation among the swarm of robots for searching and tracking targets which is the velocity inspired robotic fruit fly algorithm (VRFA). The particle velocity concept of the particle swarm optimization is added to the fruit fly algorithm to improve the parameters such as local extremum and low convergence. The simulation results of the proposed technique in different scenarios demonstrate the effectiveness of the algorithm and its ability to keep tracking the targets until the exit condition matched. At last, the simulation result is shown with different environments and parameter settings. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cooperation Fruit fly optimization intelligent search Swarm intelligence Target searching Target tracking VRFA

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