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IEEE Conference on Vehicular Technology (VTC)

作者： Peifeng Zhang Chi Xu Changqing Xia Xi Jin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

Task offloading and resource allocation are fundamental and crucial for the edge computing-enhanced industrial Internet of things, where the security and credibility among massive heterogeneous devices are being challenged. This paper first proposes a novel blockchain consensus scheme named replicated and Byzantine fault tolerant, which can enhance the trust among nodes with the low communication cost. Then, with the objective of minimizing the task completion time, which includes credible verification, task offloading and transaction record, a joint task offloading and resource allocation problem with respect to blockchain verification ratio, offloading decision, communication and computing resources is formulated. Due to its non-convexity and the decentralized characteristic of blockchain, a multi-agent deep reinforcement learning algorithm with deterministic policy gradient is proposed to appropriate the optimal solution. Experiment results confirm the effectiveness of the proposed scheme in guaranteeing the timeliness and security.

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A History Information-Based A* Algorithm for the Path Planning with Multiple Alternative Destinations

A History Information-Based A* Algorithm for the Path Planni...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ke Wang Wei Liang Huaguang Shi Jialin Zhang Qi Wang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

As a cornerstone technology in automation, path planning plays a crucial role across various domains, including logistics, autonomous vehicles, and robotics. A novel path planning challenge, focusing on routes with multiple alternative destinations, has emerged as a focal point in industrial settings. Traditional approaches such as breadth-first and heuristic searches lack specific optimizations for this challenge, resulting in suboptimal efficiency. This paper introduces a new approach to the path planning problem with multiple alternative destinations that utilizes historical data. It features three heuristic strategies: leveraging historical information for search acceleration, pruning based on the length of the current shortest path, and searching in ascending order of the Manhattan distance. These strategies enhance the search efficiency by leveraging historical information for faster searches and pruning based on real-time search outcomes. Simulation results show that the proposed algorithm outperforms the existing works in terms of the computational cost.

关键词： Service robots Navigation Simulation Focusing Path planning Real-time systems Computational efficiency

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PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Zhu, Fei Zhang, Xu-Yao Cheng, Zhen Liu, Cheng-Lin Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics 999077 Hong Kong Institute of Automation of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China

Class-incremental learning (CIL) aims to continually recognize new classes while preserving the discriminability of previously learned ones. Most existing CIL methods are exemplar-based, relying on the storage and replay of a subset of old data during training. Without access to such data, these methods typically suffer from catastrophic forgetting. In this paper, we identify two fundamental causes of forgetting in CIL: representation bias and classifier bias. To address these challenges, we propose a simple yet effective dual-bias reduction framework, which leverages self-supervised transformation (SST) in the input space and prototype augmentation (protoAug) in the feature space. On one hand, SST mitigates representation bias by encouraging the model to learn generic, diverse representations that generalize across tasks. On the other hand, protoAug tackles classifier bias by explicitly or implicitly augmenting the prototypes of old classes in the feature space, thereby imposing stronger constraints to preserve decision boundaries. We further enhance the framework with hardness-aware prototype augmentation and multi-view ensemble strategies, yielding significant performance gains. The proposed framework can be easily integrated with pre-trained models. Without storing any samples of old classes, our method performs comparably to state-of-the-art exemplar-based approaches that rely on extensive data storage. We hope to draw the attention of researchers back to non-exemplar CIL by rethinking the necessity of storing old samples. © 1979-2012 IEEE.

关键词： Self-supervised learning

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Dynamic Identification for a Manipulator Model based on Stribeck Friction using the Quasi-Newton Iterative Method

Dynamic Identification for a Manipulator Model based on Stri...

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IEEE International Conference on robotics and Biomimetics

作者： Feng Xiao Feilong Zhang Bing Han Hualiang Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China

The performance of dynamic control is intimately tied to modeling accuracy. However, traditional estimation methods and friction models, such as the least squares method and the Coulomb plus viscous model, fail to reflect the actual characteristics accurately. Particularly, the linear nature of the Coulomb plus viscous model overlooks the nonlinear static features of joint friction at slower velocities. To improve the rationality of the model structure, we integrate the Stribeck friction model into the Coulomb plus viscous model. However, introducing such nonlinearities compromises the applicability of the least squares method. As a countermeasure, we propose a new strategy that combines the least square method and the Quasi-Newton iterative method to identify the parameters of the modified nonlinear model. Additionally, the design of the excitation trajectory is critical to achieve high identification accuracy. We utilized the inverse of the smallest singular value of the observation matrix as the objective function. By minimizing it with the interior point method, we generate the excitation trajectory well-suited to stimulate dynamic characteristics. Then we leverage the discrepancies between the measured and estimated torques to assess the precision of the dynamic parameters of the manipulator. Remarkably, our proposed algorithm reduces the mean absolute error of the estimated torque by over 20.40%. Finally, an experiment of the industrial manipulator by hand guiding grab and drag is performed and shows that the proposed approach can provide the manipulator with comprehensive torque compensation.

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RealisHuman: A Two-Stage Approach for Refining Malformed Human Parts in Generated Images

arXiv

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arXiv 2024年

作者： Wang, Benzhi Zhou, Jingkai Bai, Jingqi Yang, Yang Chen, Weihua Wang, Fan Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong Alibaba Group China

In recent years, diffusion models have revolutionized visual generation, outperforming traditional frameworks like Generative Adversarial Networks (GANs). However, generating images of humans with realistic semantic parts, such as hands and faces, remains a significant challenge due to their intricate structural complexity. To address this issue, we propose a novel post-processing solution named RealisHuman. The RealisHuman framework operates in two stages. First, it generates realistic human parts, such as hands or faces, using the original malformed parts as references, ensuring consistent details with the original image. Second, it seamlessly integrates the rectified human parts back into their corresponding positions by repainting the surrounding areas to ensure smooth and realistic blending. The RealisHuman framework significantly enhances the realism of human generation, as demonstrated by notable improvements in both qualitative and quantitative metrics. Code is available at https://***/Wangbenzhi/RealisHuman. © 2024, CC BY.

关键词： Generative adversarial networks

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Hierarchical Whole-body Control of the cable-Suspended Aerial Manipulator endowed with Winch-based Actuation

Hierarchical Whole-body Control of the cable-Suspended Aeria...

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IEEE International Conference on robotics and automation (ICRA)

作者： Yuri S. Sarkisov Andre Coelho Maihara G. Santos Min Jun Kim Dzmitry Tsetserukou Christian Ott Konstantin Kondak Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany Robotics and Mechatronics Group University of Twente Enschede The Netherlands Dextrous Robotics Inc. Memphis United States Instituto Tecnológico de Aeronáutica São José dos Campos Brazil Intelligent Robotic Systems Lab Korea Advanced Institute of Science and Technology (KAIST) Daejeon Korea Automation and Control Institute TU Wien Vienna Austria

During operation, aerial manipulation systems are affected by various disturbances. Among them is a gravitational torque caused by the weight of the robotic arm. Common propeller-based actuation is ineffective against such disturbances because of possible overheating and high power consumption. To overcome this issue, in this paper we propose a winch-based actuation for the crane-stationed cable-suspended aerial manipulator. Three winch-controlled suspension rigging cables produce a desired cable tension distribution to generate a wrench that reduces the effect of gravitational torque. In order to coordinate the robotic arm and the winch-based actuation, a model-based hierarchical whole-body controller is adapted. It resolves two tasks: keeping the robotic arm end-effector at the desired pose and shifting the system center of mass in the location with zero gravitational torque. The performance of the introduced actuation system as well as control strategy is validated through experimental studies.

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Enhancing Force Sensing Capabilities in Exoskeleton Interfaces Using Compliant Actuator-Sensor Units - A User Study

Enhancing Force Sensing Capabilities in Exoskeleton Interfac...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Hosam Alagi Nikola Fischer Kai Behrends Iris Ftirst-Walter Jürgen Becker Michael Beigl Franziska Mathis-Ullrich Björn Hein Intelligent Process Automation and Robotics Laboratory (IPR) Institute for Anthropomatics and Robotics (IAR) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute of Telematics (TM) - Pervasive Computing Systems (TECO) at Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institut fuer Technik der Informationsverarbeitung (ITIV) at Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany Karlsruhe University of Applied Sciences Karlsruhe Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Reliable interaction force sensing between an exoskeleton and the human body is crucial for personalized support and efficient control. Poor donning and disturbances during movement lead to lower force measurement quality and thus to unfavorable use. In this work, we present an active smart human-robot interface by utilizing compliant actuator-sensor units (CASUs) that can be worn over clothing. These units combine capacitive force sensing and actuation using Shape-Memory Alloy (SMA) to actively compensate disturbances and improve force measurement by tightening the interface and reestablishing optimal contact. We present a pilot user study with five subjects, investigating the performance of an exoskele-ton interface featuring three integrated CASUs to compensate for non-ideal donning configurations and displacements during daily activities of walking, sitting down, and climbing the stairs. The results for a passive interface show a decrease in holding forces and force signal quality during activities. Our active interface, on the other hand, effectively compensates for disturbances, restoring high signal quality by the mean of peak-to-peak value up to 300% which leads to more distinct signals.

关键词： Legged locomotion Force measurement Cooling Force Exoskeletons Metals Stairs Robot sensing systems Sensors Reliability

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IEEE International Conference on Image Processing

作者： Chaowei Han Gaofeng Meng Chunlei Huo State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation Chinese Academy of Sciences School of Information Engineering Capital Normal University Beijing China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Content-based video retrieval aims to retrieve near-duplicate entries from a database of a given query video. It plays an important role in combating video piracy. Robustness to video temporal dynamics is crucial for a representation model in video retrieval, as frames extracted from two copied videos are hardly temporally aligned in actual situations. However, current image retrieval datasets have difficulty in evaluating this robustness. To address this issue, we collect Similar Frame Dataset (SFD), which consists of 32,923 query-target pairs with 128,240 distraction images. The task of SFD is to retrieve the target frame from all items given a query frame. SFD is constructed by sampling frames from Kinetics-700 action classification dataset. An object detection model (Faster R-CNN) and a Multimodal Large Language Model (BLIP2) are used during sampling to select those valid frames. Besides, we propose Adjacent Frames Contrastive Learning (AFCL) framework. In AFCL, adjacent frames are sampled from unlabeled videos as positive pairs. An image representation model with robustness to changing frames can be trained under AFCL framework and achieve the state-of-the-art performance on SFD. The code will be released at https://***/Chuan-shanjia/Similar-Frame-Dataset.

关键词： Codes Databases Scalability Large language models Image retrieval Contrastive learning Object detection

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Determination of the Characteristics of Gears of Robot-Like systems by Analytical Description of their Structure

Determination of the Characteristics of Gears of Robot-Like ...

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

作者： Stefan Landler Raúl Molina Blanco Michael Otto Birgit Vogel-Heuser Markus Zimmermann Karsten Stahl Technical University of Munich Germany TUM School of Engineering and Design Department of Mechanical Engineering Munich Institute of Robotics and Machine Intelligence (MIRMI) Institute of Machine Elements Gear Research Center (FZG) Institute of Automation and Information Systems (AIS) Laboratory for Product Development and Lightweight Design (LPL)

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

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Optimization method of Data interaction in power IoT based on particle swarm algorithm 2

Optimization method of Data interaction in power IoT based o...

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2nd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Wang, Tianyu Miao, Weiwei Zeng, Zeng Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China State Grid Jiangsu Electric Power Co. Ltd. Information Telecommunication Branch Nanjing China

ISBN: (纸本)9781665408868

The ubiquitous power Internet of things (IoT) constructs an intelligent service system with comprehensive perception, efficient response and flexible processing around all departments of the power system and with the support of communication technologies such as mobile Internet and artificial intelligence. With the rapid development of smart IoT perceptual devices and edge computing, it has become possible for perceptual devices and edge IoT agents to cooperate in processing tasks. The task completion time can be greatly reduced through the cooperative processing among multiple entities in the IoT. However, as the scale of the Internet of things and number of to-be-processed tasks expanding year by year, the simple task data interaction between devices can not meet the needs of the completion time. Therefore, it is an urgent problem to optimize data transmission and data interaction in the process of collaborative task processing among IoT devices. Motivated by this point, we propose an optimization method of data interaction in power IoT based on particle swarm algorithm. Evaluation on example IoT networks shows the effectiveness of our method to reduce overall task completion time. © 2022 IEEE.

关键词： Internet of things

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