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2024 International Convention on Rehabilitation engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Cao, Wujing Zhang, Shuo Zou, Yupeng Guo, Xinqiang Li, Changyu Wang, Jingxin Kobsiriphat, Worawarit Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518005 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems China School of Mechanical Engineering China University of Petroleum China Jining Zhongke Intelligent Technology Co. Ltd. China Zhengzhou Central Hospital Zhengzhou University Department of Rehabilitation Medicine Zhengzhou China National Metal and Materials Technology Center Thailand

ISBN: (纸本)9798350355123

Squat gait is the most common abnormal gait in children with cerebral palsy. Gait training with lower limb rehabilitation robots can improve rehabilitation efficiency and reduce manual labor for them. A novel exoskeleton-based lower limb rehabilitation robot for children with cerebral palsy is proposed, considering the squat gait characteristics of children with cerebral palsy. The structure of the lower limb rehabilitation robot, which combines hip and knee with 4 active degrees of freedom exoskeleton and mobile walker, is designed and optimized. Static analysis of the key mechanical structure of the exoskeleton is conducted using ANSYS Workbench, and local structures are optimized to ensure the safety factor of the mechanical structure is greater than 1, guaranteeing reasonable structural strength and stiffness design. Dynamic simulation analysis based on the ADAMS model validates the reliability and dynamic walking effect of the lower limb exoskeleton rehabilitation robot, obtaining the curve relationship between joint angles and motion cycles, thus verifying the effectiveness of the mechanical system motion. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization

A Depression Level Classification Model Based on EEG and GCN...

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第43届中国控制会议

作者： Zhongyi Zhang Chenyang Xu Licheng Jin Huirang Hou Qinghao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Electroencephalography(EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network(GCN) to identify levels of depression. Additionally,to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art(SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module.

关键词： Depression Level EEG Graph Convolution Network Domain Generalization

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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第43届中国控制会议

作者： Tian-Shu Song Zhen-Peng Chen Hui-Rang Hou Hong-Yue Li Qing-Hao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Detecting ignitable liquids(ILs) at the scene of a fire is crucial for fire investigation. The electronic nose(e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network(FMG-Net).This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network(SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs(diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset.

关键词： Electronic Nose Fire Investigation Ignitable Liquids Convolutional Neural Network Cross Platform

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Parallel Path Planning for Multi-Robot Coordination

Parallel Path Planning for Multi-Robot Coordination

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Jorge Ribeiro Marina Brilhante Diogo Miguel Matos Carlos A. Silva Héber Sobreira Pedro Costa CRIIS - Centre of Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Flowbotic Mobile Systems S.A Viseu Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

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Evaluation of a Weather Plugin in Gazebo: A Case-Study of a Wind Influence on PX4-Based UAV Performance 9th

Evaluation of a Weather Plugin in Gazebo: A Case-Study of a ...

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9th International Conference on Interactive Collaborative robotics, ICR 2024

作者： Imamov, Niyaz Abbyasov, Bulat Tsoy, Tatyana Martínez-García, Edgar A. Magid, Evgeni Laboratory of Intelligent Robotic Systems Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlyovskaya St. Kazan420111 Russia Tikhonov Moscow Institute of Electronics and Mathematics HSE University Tallinn Street 34 Moscow Russia Institute of Engineering and Technology Department of Industrial Engineering and Manufacturing Autonomous University of Ciudad Juarez Manuel Díaz H. No. 518-B Zona Pronaf Condominio Chihuahua Cuidad Juárez32315 Mexico

ISBN: (纸本)9783031713590

Unmanned aerial vehicles (UAVs) are utilized in a wide range of real-world applications including search and rescue missions, 3D mapping, building inspection and reconstruction. In a real-world environment, severe weather conditions significantly impact UAV operations. Wind is one of the most dangerous weather factors that can destabilize and even destroy a UAV. Preparing UAV flight algorithms for real-world conditions in order to minimize risks of damage and malfunctioning is a crucial part of a UAV system development. This paper analyses a wind influence on UAV virtual model performance within the Gazebo simulator. The paper focuses on gazebo_wind_plugin package used for creating a windy virtual environment. PX4-LIRS package is used for simulating a UAV within the Gazebo. Three types of experiments were run to evaluate capabilities of the plugin to simulate typical issues that arise in real-world scenarios: a UAV wind resistance, a wind effect on a UAV tilt angle and on a flight velocity. Virtual experiments demonstrated that gazebo_wind_plugin could serve for PX4 UAV flight scenarios modeling in Gazebo simulation with a satisfactory level of realism. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network 42

AMG: A Depression Detection Model with Autoencoder and Multi...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Han-Guang Meng, Qing-Hao Jin, Li-Cheng Wang, Jian-Bang Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China China Southern Power Grid Company Limited Guangzhou510623 China

ISBN: (纸本)9789887581543

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Decision trees

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Global Trajectory Optimization for Autonomous Driving using Nonlinear Programming with Topology Classes*

Global Trajectory Optimization for Autonomous Driving using ...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Maryam Nezami Ngoc Thinh Nguyen Georg Schildbach Institute for Electrical Engineering in Medicine University of Lübeck (UzL) Lübeck Germany Institute for Robotics and Cognitive Systems UzL Lübeck Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Obstacle avoidance is an essential characteristic of autonomous driving cars. This paper presents a new method for efficiently determining possible routes that a vehicle can follow for overtaking obstacles and classifying them into different topology classes of paths. Impossible routes are eliminated based on the physical constraints of the vehicle. To evaluate each topology class and pick the one with the lowest cost as the globally optimal path, we run several topology-aided Nonlinear Programming (NLP) problems. For each topology-aided NLP problem to overtake the obstacles from the prespecified side determined by the topology class, corresponding auxiliary halfspace constraints are activated in the optimization problem. Through simulation results and comparisons with a standard NLP planner, we show that the overall computation time for solving several topology-aided NLP problems is faster than that of the standard NLP planner. Additionally, in complex driving scenarios, where the standard planner gets stuck in a sub-optimal solution due to internal faults or a local optimum, our proposed topology-aided planner can still find the globally optimal path.

关键词： Costs Simulation Roads Programming Topology Planning Collision avoidance Standards Autonomous vehicles Trajectory optimization

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Latent Action Priors for Locomotion with Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Hausdörfer, Oliver von Rohr, Alexander Lefort, Éric Schoellig, Angela P. The Technical University of Munich Germany TUM School of Computation Information and Technology Department of Computer Engineering Learning Systems and Robotics Lab Germany Munich Institute of Robotics and Machine Intelligence Germany

Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algorithms, the resulting solutions are often brittle and appear unnatural. This is especially true for learning direct joint-level torque control, as inductive biases are difficult to integrate into the learning process. We propose an inductive bias for learning locomotion that is especially useful for torque control: latent actions learned from a small dataset of expert demonstrations. This prior allows the policy to directly leverage knowledge contained in the expert’s actions and facilitates more efficient exploration. We observe that the agent is not restricted to the reward levels of the demonstration, and performance in transfer tasks is improved significantly. Latent action priors combined with style rewards for imitation leads to a closer replication of the expert’s behavior. Videos and code are available at https://***/view/ latent-action-priors. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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RADAR: robotics Assembly by Demonstration via Augmented Reality

RADAR: Robotics Assembly by Demonstration via Augmented Real...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Wenhao Yang Shi Bai Yunbo Zhang Department of Industrial and Systems Engineering Lamar University Beaumont TX USA iServe Robotics Sunnyvale CA USA Department of Industrial and Systems Engineering Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

With the widespread adoption of robots in high-mix, low-volume manufacturing, and the challenges posed by long-horizon assembly tasks, we introduce the RADAR system—an integrated human-robot collaboration system for Robotic Assembly by Demonstration via Augmented Reality. Existing frameworks lack a comprehensive, cross-task framework for effective assembly collaboration, limiting their applicability in complex tasks. We designed the RADAR system’s conceptual model, detailing its workflow and components. The system integrates human input into robotic metal beam assembly through augmented reality interactions and interfaces. We also developed a task planner that dynamically adjusts human-robot assembly tasks at coarse-fine resolutions. Validating through practical scenarios, particularly the RAMP assembly benchmark, showed that human involvement significantly enhances assembly precision and success rates, proving RADAR’s effectiveness and efficiency in human-robot collaborative assembly.

关键词： Robotic assembly Sequential analysis Systematics Collaboration Radar Production Benchmark testing Planning Assembly Augmented reality

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A Look-Up Table Based Inverter Nonlinearity Compensation Method Considering Parameter Inconsistency of Upper and Lower Bridge Arms

A Look-Up Table Based Inverter Nonlinearity Compensation Met...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yonghe Huang Dianguo Xu Yan Xu Yong Yu Junyu Zhao Xubo Gong School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China The State key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

In motor drive systems, voltage source inverter (VSI) nonlinearity will produce voltage error, cause current harmonics, and affect the control performance. Among the VSI nonlinearity factors, the parasitic capacitance is the fundamental cause of the nonlinear relationship between voltage error and phase current. If the parameter inconsistency of the upper and lower bridge arm is not considered, the voltage error is an odd function. However, in practical applications, the parameters of the upper and lower bridge arms are usually slightly different, and the voltage error will no longer be an odd function. Considering the influence of parameter inconsistency, this article proposes an accurate look-up table (LUT) based method for VSI nonlinearity compensation. By injecting positive staircase current and negative staircase current separately in self-commissioning process, an accurate voltage error LUT is obtained through numerical fitting methods. The proposed method does not require any parameter of power devices. Experimental results verify the effectiveness of the proposed method.

关键词： Industrial electronics Motor drives Accuracy Voltage source inverters Fitting Bridge circuits Table lookup

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