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arXiv 2024年

作者： Li, Junfei Yang, Simon X. Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada

Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this paper, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot provide a satisfactory solution to real-time responses to sudden environmental changes. The neurodynamic model is incorporated into the feature learning method that can use environmental information to improve path planning strategies. Task assignment and collision-free rescue trajectory are generated through robot poses and the dynamic landscape of neural activity. A dual-channel scale filter, a neural activity channel, and a secondary distance fusion are employed to extract and filter feature neurons. After completion of the feature learning process, a neurodynamics-based feature matrix is established to quickly generate the new heuristic rescue paths with parameter-driven topological adaptability. The proposed FLBBINN aims to reduce the computational complexity of the neural network-based approach and enable the feature learning method to achieve real-time responses to environmental changes. Several simulations and experiments have been conducted to evaluate the performance of the proposed FLBBINN. The results show that the proposed FLBBINN would significantly improve the speed, efficiency, and optimality for rescue operations. © 2024, CC BY.

关键词： Multipurpose robots

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intelligent Collective Escape of Swarm Robots Based on a Novel Fish-inspired Self-adaptive Approach with Neurodynamic Models

arXiv

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arXiv 2024年

作者： Li, Junfei Yang, Simon X. The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada

Fish schools present high-efficiency group behaviors through simple individual interactions to collective migration and dynamic escape from the predator. The school behavior of fish is usually a good inspiration to design control architecture for swarm robots. In this paper, a novel fish-inspired self-adaptive approach is proposed for collective escape for the swarm robots. In addition, a bio-inspired neural network (BINN) is introduced to generate collision-free escape robot trajectories through the combination of attractive and repulsive forces. Furthermore, to cope with dynamic environments, a neurodynamics-based self-adaptive mechanism is proposed to improve the self-adaptive performance of the swarm robots in the changing environment. Similar to fish escape maneuvers, simulation and experimental results show that the swarm robots are capable of collectively leaving away from the threats. Several comparison studies demonstrated that the proposed approach can significantly improve the effectiveness and efficiency of system performance, and the flexibility and robustness in complex environments. © 2024, CC BY.

关键词： Fish

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YOLO-Based Detector for Enhancing Workplace Safety: Real-Time PPE Detection and Monitoring

YOLO-Based Detector for Enhancing Workplace Safety: Real-Tim...

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ACS/IEEE International Conference on Computer systems and Applications

作者： Souha Amri Laila Ouannes Anis Sahbani Najoua Essoukri Ben Amara Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Enova Robotics S.A. CNRS Institule for intelligent Systems and Robotics (ISIR) Sorbonne Université Paris France

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Ensuring the safety of workers in hazardous environments is crucial, given the potential severity of accidents. Indeed, Personal Protective Equipment (PPE), including safety helmets, vests, and boots, is vital in mitigating risks. However, adherence to PPE usage often falls short due to inadequate safety awareness among workers. To address this challenge, we aim to develop a robust safety monitoring system by leveraging advancements in deep learning and object detection technology. Our proposed method uses YOLOv7 and YOLOv8 algorithms for real-time detection and classification of safety equipment, including helmets, vests, and boots. For this aim, we carefully collect and annotate datasets from diverse sources like Roboflow and Kaggle. Then, we accurately evaluate performance metrics such as precision, recall, and speed through rigorous training and testing of various YOLOv7 and YOLOv8 configurations on annotated data batches. By conducting thorough comparative analyses, we aim to identify the most effective model for each PPE category, considering both detection accuracy and computational efficiency. Finally, the object detection system is deployed through a web interface to determine whether a person is protected or not.

关键词： Personal protective equipment Training Accuracy Computational modeling Employment Streaming media Helmets Real-time systems Safety Monitoring

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Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-LSTM-Attention Network from Surface Electromyography

Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Wang, Zhuo Chen, Hui Yang, Fangliang Wang, Xiangyang Wu, Xinyu Chen, Chunjie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

In various fields such as robotics, rehabilitation, biomechanics, human-machine interfaces, and clinical research, human motion prediction has extensive applications. A hybrid networks model based on Convolutional Neural Network-Long Short-Term Memory-Attention (CNN-LSTM-Attention) was employed in this paper to extract features from the high-dimensional space and time series of surface electromyography (sEMG) data for continuous prediction of knee angles. Five able-bodied subjects participated in the study, walking at a self-selected pace while six surface electromyography (sEMG) signals from muscles involved in knee flexion and extension, along with knee angles, were synchronously recorded. The performance was assessed using the root mean square error (RMSE), Pearson Correlation Coefficient (r), and coefficient of determination (R2) between estimated and measured knee joint angles. Compared to the CNN, LSTM, and CNN-LSTM algorithms, the adopted method estimated knee angles more accurately, achieving an average RMSE, ρ, and R2 of 3.09± 0.90,\ 0.9911 ± 0.0052, and 0.9817± 0.0108, respectively. As the advance time increased, the accuracy of knee joint angle estimation decreased, particularly when the advance time reached 100 ms, at which point the RMSE is 4.83 degress. In addition, the computing time of the prediction model was less than 1 ms, which was shorter than the advance time 100 ms it could predict, thereby facilitating real-time computation in practical environments. Compared to previous studies, the proposed method excelled at extracting features directly from raw signals, avoiding the need for handcrafted feature extraction. Consequently, the model effectively achieved continuous and accurate predictions of joint motion angles and demonstrated potential to reduce latency in the human-machine interface. © 2024 IEEE.

关键词： Clinical research

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Zero-Shot Kidney Stone Segmentation Based on Segmentation Anything Model for Robotic-Assisted Endoscope Navigation 16th

Zero-Shot Kidney Stone Segmentation Based on Segmentation A...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Meng, Hui Chen, Lingkai Zhu, Shiqiang Fei, Chentao Zhang, Yong Zheng, Miaojuan Han, Jianda Song, Wei School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences 1 Sub-lane Xiangshan Hangzhou310024 China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Hangzhou ElephantChain Network Technology Co. Ltd. Hangzhou China Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou310018 China College of Artificial Intelligence and Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information Systems Nankai University Tianjin300350 China

ISBN: (纸本)9789819964888

Biomedical robotics has had a significant impact on flexible ureteroscopy (FURS) for the management of nephrolithiasis. The biomedical robots provide a suitable and safe platform for FURS. However, operating the robots expertly still represents a challenge for surgeons. Vision-based navigation is an effective means to simplify the operations of robots by surgeons, which has great potential to improve surgical efficiency. To achieve vision-based navigation, the localization of kidney stones in endoscopic images is the first step. In this paper, we propose a stone segmentation method (SSM) based on zero-shot learning, which consists of segmentation anything model (SAM), an image preprocessing module, and a mask postprocessing module. The SAM segments all objects in endoscopic images, which lack semantic information. The preprocessing module is designed to suppress interference of reflective area in endoscopic images, and the mask postprocessing module is used to filter noise masks. The experimental results on our collected endoscopic images indicate that our method achieves close accuracy on stone segmentation compared with supervised U-Net and VGG_U-Net, which significantly outperforms the Ostu and the SAM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Endoscopy

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ViT-based Terrain Recognition System for wearable soft exosuit

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Biomimetic Intelligence & robotics 2023年第1期3卷 57-64页

作者： Fangliang Yang Chunjie Chen Zhuo Wang Hui Chen Yao Liu Gang Li Xinyu Wua Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhen 518055China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Shenzhen 518055China SIASUN Robot and Automation CO. LtdShenyang 110169China

Terrain classification and force assistance strategies in complex environments have always piqued the interest of many *** wearable soft exosuits,inaccurate terrain recognition can easily introduce undesired assist forces that can easily injure the *** of these problems,we introduced a depth camera into the exosuit system,perform classification of terrain based on a Vision Transformer(ViT),and optimized the control algorithm,which is known as a ViT-Based Terrain Recognition System(TTRS).First,we used the Transformer algorithm to achieve a considerable classification effect in terrain *** also introduced terrain recognition as prior knowledge into the force assistance strategy of the exosuit,providing different force assistance to the exosuit in different ***,we performed human experiments with seven able-bodied people(six males and one female).The promising results demonstrate that our classification accuracy can reach 99.2%under six different terrains and that it can smoothly switch the force–assist curve in different terrains to better adapt to the complex terrain and improve the walking *** aforementioned terrain recognition algorithms and force–assist strategies may positively influence the study of soft exosuit,powered prostheses,and orthotics.

关键词： Vision Transformer(ViT) Depth camera Wearable soft exosuit Force assistance strategy Terrain recognition

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Formation Control of Unmanned Underwater Vehicles With Unknown Absolute Position Using Rigid Graph-Based MPC

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IEEE Internet of Things Journal 2025年

作者： Fu, Bowen Zhu, Daqi Chen, Mingzhi Yang, Simon X. University of Shanghai for Science and Technology Shanghai200093 China University of Guelph Advanced Robotics and Intelligent Systems Laboratory School of Engineering GuelphONN1G2W1 Canada

Formation control of unmanned underwater vehicles (UUVs) is of great significance to the Internet of Underwater Things (IoUT). Currently, various methods are employed for UUV formation control. Among them, rigid graph-based approaches, which rely solely on relative information between UUVs, are particularly suitable for underwater environments. However, traditional rigid graph-based formation control methods often face challenges such as control jumps and weak robustness in dynamic underwater environments. To address these challenges, a novel rigid graph-based model predictive controller (RGMPC) is proposed in this paper. The proposed approach integrates model predictive control with traditional rigid graph-based methods, effectively avoiding issues such as control jumps and thrust saturation while enhancing robustness. Moreover, constraints are constructed using backstepping methods to theoretically ensure the closed-loop stability of the algorithm. Finally, the algorithm is validated through extensive simulations based on mathematical models and further tested in the Gazebo simulator, fully demonstrating its feasibility and effectiveness. © 2014 IEEE.

关键词： Predictive control systems

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Path Tracking Control for Helical Microrobots Based on Fusion of Geometric and Model-Free Methods 13

Path Tracking Control for Helical Microrobots Based on Fusio...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2023

作者： Zhong, Shihao Wang, Huaping Shi, Qing Hou, Yaozhen Qiu, Yukang Sun, Tao Huang, Qiang Fukuda, Toshio School of Medical Technology Beijing Institute of Technology Beijing100081 China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350315196

Helical micron-sized robots driven by magnetic fields have garnered significant interest, particularly in the field of tracking control. However, at the microscale, time-varying uncertain perturbations in both the environment and electromagnetic system can cause these robots to deviate from their preset state. Here, we propose a novel control method that combines geometric and model-free controllers in a complementary manner to achieve accurate path-tracking of a helical microrobot on underwater unstructured surfaces. The geometric controller acts as a feedforward controller handling path information and formulating guidance law, while the model-free controller responds directly to the lateral error. To compensate for unmodeled nonlinear dynamics and unknown disturbances and achieve system dynamic linearization, we employ an extended state observer. To validate the proposed method and determine suitable ranges for input parameters of the controllers, simulations were carried out. The effectiveness of our path-tracking control method is validated through experimental results. The control technique provides an initial investigation into addressing the effects of uncertainties and perturbation on the control capabilities of magnetic microrobots. © 2023 IEEE.

关键词： Controllers

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Transformer-Based Fusion of RGB and Depth Images for Terrain Recognition

Transformer-Based Fusion of RGB and Depth Images for Terrain...

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International Conference on Biomimetic Intelligence and robotics & Medical robotics Forum, ICBIR+MRF 2024

作者： Chen, Hui Chen, Chunjie Wang, Zhuo Yang, Fangliang Pan, Heng Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image classification, our approach utilizes both early fusion and late fusion methods to explore their impact on terrain classification. This approach achieved fast and accurate performance in recognizing terrain. We captured data for walking upstairs, downstairs, upward ramps, downward ramps and level ground and used the vision transformer for data fusion. This work compares the performance of unimodal RGB model, unimodal deep model, Pre-Fusion model and Post-Fusion model in terms of accuracy, recall and processing frequency, with accuracies of 0.954, 0.934, 0.976, and 0.976, respectively. The results show that the Post-Fusion model slightly outperforms the Pre-Fusion model in terms of accuracy and recall with more reliable classification performance. To understand the attention mechanism better, we've been visualizing the weights of the model, observing how different attention heads may capture different features of the data from various dimensional perspectives. The visualization helps in interpreting the model's focus and how it distinguishes between features of different nature, such as color texture and spatial depth. These findings can inform the ongoing refinement of terrain classification systems for applications in robotics and autonomous navigation. © 2024 The Authors.

关键词： RGB color model

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POMDP-Based Real-Time Path Planning for Manipulation of Multiple Microparticles via Optoelectronic Tweezers

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Cyborg and Bionic systems 2022年第1期2022卷 303-313页

作者： Jiaxin Liu Huaping Wang Menghua Liu Ran Zhao Yanfeng Zhao Tao Sun Qing Shi Intelligent Robotics Institute School of Mechatronical Engineering Bejing Institute of TechnologyBeijing 10081China Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China School of Medical Technology Beijing Institute of TechnologyBeijing 100081China Bejing Advanced Innovation Center for Itelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China

With high throughput and high flexibility,optoelectronic tweerers(OETs)hold huge potential for massively parallel ***,the trajectory of the virtual electrode has been planned in advance in most synchronous manipulations for multiple targets based on an optically induced dielectrophoresis(ODEP)mechanism,which is insuficient to ensure the stability and eficiency in an environment with potential collision *** this paper,a synchronously discretized manipulation method based on a centralized and decoupled path planner is proposed for transporting microparticles of diferent types with an OET *** approach based on the K uhn-Munkres algorithm is utilized to achieve the goal assignment between target microparticles and goal *** the assistance of a visual feedback module,a path planning approach based on the POMDP algorithm dynamically determines the motion strategies of the particle movement to avoid potential *** geometrical parameters of the virtual electrodes are optimized for different types of particles with the goal of maximum transport *** experiments of micropatterning with different morphologies and transporting multiple micropartides(e.g,polystyrene microspheres and 3T3 cells)to goal positions are *** results demonstrate that the proposed manipulation method based on optoelectronic tweezers is effective for multicell transport and promises to be used in biomedical manipulation tasks with high flexibility and efficiency.

关键词： manipulation positions assignment

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