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Dynamics and Control of a Reconfigurable Multi-Arm Robot for In-Orbit Assembly

IFAC-PapersOnLine 2022年第20期55卷 235-240页

作者： Hrishik Mishra Marco De Stefano Christian Ott Institute of Robotics and Mechatronics German Aerospace Center (DLR) 82234 Weßling Germany Automation and Control Institute (ACIN) Technical University of Vienna 1040 Vienna Austria

In this paper, a passivity-based controller is proposed for a reconfigurable multi-arm system, which is employed for in-orbit assembly. The reconfigurable system is composed of multiple anthropomorphic arms which can be connected to a torso or operate independently. The proposed control is applicable for all the morphologies of the multi-arm system while performing several operations required for the assembly task, such as walking on a telescope structure through dedicated mechanical interfaces. The constrained dynamics is exploited to derive a unified joint actuation control law for all the considered operations and morphologies. A Co-simulation framework was developed to simulate the operations for control prototyping, and simulation results prove the effectiveness of the proposed unified controller.

关键词： Motion Control Robots manipulators Passivity-based control

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Air-Ground Cooperative Path Planning for Doorstep Pickup Considering Multiple Uncertainty

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IEEE Sensors Journal 2025年

作者： Tian, Liang Chen, Yang Wu, Huaiyu Zhao, Xingang Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China

Air-ground heterogeneous robots collaboration can efficiently handle first-mile pickups and reduce reverse logistics costs. When an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV) collaborate for doorstep pickups, the UAV handles customer pickups while the UGV acts as a mobile warehouse, providing replenishment and transportation services. This setup enables long-distance, large-scale, and flexible logistics. However, real-world collaborative pickups face uncertainties in pickup times and parcel weights, complicating path planning. To address the challenge, this paper aims to minimize the total travel distance for UAV and UGV and formulates a path planning model with time windows under uncertainty and designs an adaptive genetic large neighborhood search algorithm for solution. Through numerical simulations and comparative analysis, the method’s scalability and adaptivity to uncertainty has been validated extensively. Real-world experiments confirm the method’s effectiveness and feasibility. These findings offer both theoretical guidance and practical insights for air-ground robot path planning, contributing to improved logistics efficiency and cost reduction. © 2001-2012 IEEE.

关键词： Helicopter services

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Mergulhão Bot: Design and Prototyping of a Low-Cost Hybrid Vehicle for Oil Leakage Detection

Mergulhão Bot: Design and Prototyping of a Low-Cost Hybrid ...

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IEEE Latin American robotics Symposium, LARS

作者： Guilherme C. De Oliveira Enrique F. Garcia Julio Cesar S. Dos Santos Adir A. Pedroso Leonardo C. Pereira Paulo L. J. Drews NAUTEC - Intelligent robotics and automation group FURG - Federal University of Rio Grande Rio Grande Brazil CDTEC - Technological Development Center UFPEL - Federal University of Pelotas Pelotas Brazil

Unmanned vehicles are increasingly popular and their demand increases annually. Since their invention in the 1950s, these vehicles have been widely used in various applications which include environmental monitoring. Oil spill in the oceans is an example of a serious problem for marine ecosystems that can be investigated with these vehicles. Currently, government authorities are analyzing satellite images to detect oil spots, but when oil submerges in the ocean becomes imperceptible by satellites. This oil can be silently moving to coastal regions, without containment maneuvers. Unmanned vehicles may be an effective alternative to remote sensing in these cases. In this scenario, this work presents a new design of a hybrid vehicle to move on the ocean surface and be able to submerge water, called the Mergulhão Bot. The robot was mechanically modeled using OnShape software, simulations were performed using ROS, Gazebo, and the UUV Simulator package.

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Conversational Complexity for Assessing Risk in Large Language Models

arXiv

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arXiv 2024年

作者： Burden, John Cebrian, Manuel Hernandez-Orallo, Jose Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Center for Automation and Robotics Spanish National Research Council Spain Valencian Research Institute for Artificial Intelligence Universitat Politècnica de València Spain

Large Language Models (LLMs) present a dual-use dilemma: they enable beneficial applications while harboring potential for harm, particularly through conversational interactions. Despite various safeguards, advanced LLMs remain vulnerable. A watersed case in early 2023 involved journalist Kevin Roose’s extended dialogue with Bing, an LLM-powered search engine, which revealed harmful outputs after probing questions, highlighting vulnerabilities in the model’s safeguards. This contrasts with simpler early jailbreaks, like the "Grandma Jailbreak," where users framed requests as innocent help for a grandmother, easily eliciting similar content. This raises the question: How much conversational effort is needed to elicit harmful information from LLMs? We propose two measures: Conversational Length (CL), which quantifies the conversation length used to obtain a specific response, and Conversational Complexity (CC), defined as the Kolmogorov complexity of the user’s instruction sequence leading to the response. To address the incomputability of Kolmogorov complexity, we approximate CC using a reference LLM to estimate the compressibility of user instructions. Applying this approach to a large red-teaming dataset, we perform a quantitative analysis examining the statistical distribution of harmful and harmless conversational lengths and complexities. Our empirical findings suggest that this distributional analysis and the minimisation of CC serve as valuable tools for understanding AI safety, offering insights into the accessibility of harmful information. This work establishes a foundation for a new perspective on LLM safety, centered around the algorithmic complexity of pathways to harm. © 2024, CC BY.

关键词： Risk assessment

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Multi-Feature-Based Online Remaining Useful Life Prediction of Lithium-Ion Batteries in Stages Using Cascaded Data-Driven Algorithm

SSRN

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SSRN 2023年

作者： Tao, Liujun Wu, Huaiyu Zheng, Xiujuan Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China

Accurate prediction of remaining useful life (RUL) is very important for the safe and stable operation of battery systems. Considering that it is difficult to directly measure the capacity of lithium-ion batteries during operation, this paper proposes an online hybrid cascaded data-driven prediction algorithm for RUL. Firstly, health indicators (HIs) are extracted from the charge-discharge voltage and incremental capacity curves. Then, the correlation between HIs and battery capacity degradation is quantified by grey relational analysis. And kernel principal component analysis is used to eliminate redundant information of HIs. Secondly, the kernel extreme learning machine based on sparrow search algorithm (SSA-KELM) is selected as the first-level capacity prediction model to establish the relationship between HIs and capacity. The bidirectional long short-term memory network is selected as the secondary-level capacity prediction model, which fuses the preliminary capacity predictions of SSA-KELM. Finally, the battery dataset from NASA and Oxford are used to verify the proposed method. The results demonstrate that the proposed method has strong generalization ability and high prediction accuracy. Quantitatively, the RMSE and MAPE of NASA batteries are within 0.03, while the errors of Oxford batteries are within 0.003. The RUL prediction errors of all lithium-ion batteries are within two cycles. © 2023, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Physics-of-Failure based Model for Industrial Robot Reliability Prediction 17

Physics-of-Failure based Model for Industrial Robot Reliabil...

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17th IEEE International Conference on Mechatronics and automation, ICMA 2020

作者： Zhang, Jiafan Song, Wei Robotics and Discrete Automation Research Center China Abb Shanghai 201319 China

ISBN: (纸本)9781728164151

Millions of industrial robots are being used around the world for diversified applications, like material handling, machine tending, spot-welding, arc-welding and so on. The reliability and quality of industrial robots is becoming more and more highlighted. Over decades, many studies have appeared to address this problem. In this work, a Physics-of-Failure (PoF) based reliability prediction model of industrial robot is initiated. By leveraging the comprehensive knowledge and understanding of the processes and the mechanisms that induce failures quantitatively formulate the contributions of each component and their subcomponents in terms of design parameters, working loads and environmental conditions, thereof enables to predict the reliability of the overall industrial robot. © 2020 IEEE.

关键词： Industrial robots

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Application of unmanned underwater vehicles in polar research

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Advances in Polar Science 2021年第3期32卷 173-184页

作者： ZENG Junbao LI Shuo LIU Ya State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Center for Ocean Mega-Science Chinese Academy of SciencesQingdao 266071China

The importance of polar ice as vital components of the global ocean-climate system is widely *** this paper,we demonstrate the importance and urgency of polar research,describe the primary characteristics of sea ice and ice shelves,and outline the current status and difficulties associated with sub-ice *** highlight the importance of Unmanned Underwater Vehicles(UUVs)as important tools for oceanographic *** present recent progress in UUV deployment in sub-ice research in the Arctic and the Antarctic,and review the latest international developments in UUV structure,navigation,payload,and field ***,Chinese polar UUVs and their deployments in the polar regions are presented in *** technologies and solutions regarding polar application of UUVs(e.g.,sub-ice navigation and positioning,energy supply and data transmission,and sub-ice guidance and recovery)are *** the current worldwide attention on polar science,the potential future directions of UUV-related polar research(e.g.,observations under Antarctic ice shelves,long-range surveys beneath Arctic sea ice and application of intelligent technology)are discussed.

关键词： unmanned underwater vehicle polar ocean scientific investigation key technologies development trend

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FDNet: Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Fourier Series and DenseNet

FDNet: Remaining Useful Life Prediction for Lithium-Ion Batt...

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Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Siwen Chen Xiujuan Zheng Shanghai United Imaging Healthcare Co. Ltd Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

To enhance the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, this study proposes a novel RUL prediction model, termed FDNet, which integrates Discrete Fourier Series (DFS) and Densely Connected Convolutional Networks (DenseNet). In contrast to traditional methods that rely on one-dimensional (1D) numerical representations of time-series data, this model utilizes a two-dimensional (2D) visual representation for direct RUL prediction. First, the DFS is employed to convert 1D time-series signals into a 2D time-frequency domain, enabling the extraction of high-dimensional features from the original data. Then, these 2D images are subsequently input into DenseNet, which learns the mapping between the image features and the RUL targets. Finally, the proposed model is validated using the NASA lithium-ion battery aging dataset, and the results indicate that FDNet achieves superior prediction accuracy and exhibits notable robustness.

关键词： Lithium-ion batteries Time-frequency analysis Accuracy NASA Predictive models Feature extraction Robustness Numerical models Fourier series Convolutional neural networks

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LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection

arXiv

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arXiv 2022年

作者： Wei, Yi Wei, Zibu Rao, Yongming Li, Jiaxin Zhou, Jie Lu, Jiwen Department of Automation Tsinghua University China Beijing National Research Center for Information Science and Technology China Gaussian Robotics China

In this paper, we propose the LiDAR Distillation to bridge the domain gap induced by different LiDAR beams for 3D object detection. In many real-world applications, the LiDAR points used by mass-produced robots and vehicles usually have fewer beams than that in large-scale public datasets. Moreover, as the LiDARs are upgraded to other product models with different beam amount, it becomes challenging to utilize the labeled data captured by previous versions' high-resolution sensors. Despite the recent progress on domain adaptive 3D detection, most methods struggle to eliminate the beam-induced domain gap. We find that it is essential to align the point cloud density of the source domain with that of the target domain during the training process. Inspired by this discovery, we propose a progressive framework to mitigate the beam-induced domain shift. In each iteration, we first generate low-beam pseudo LiDAR by downsampling the high-beam point clouds. Then the teacher-student framework is employed to distill rich information from the data with more beams. Extensive experiments on Waymo, nuScenes and KITTI datasets with three different LiDAR-based detectors demonstrate the effectiveness of our LiDAR Distillation. Notably, our approach does not increase any additional computation cost for inference. Code is available at https://***/weiyithu/LiDAR-Distillation. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Point Cloud based Motion State Estimation Method for Autonomous Driving

Point Cloud based Motion State Estimation Method for Autonom...

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2023 International Conference on Advanced Mechatronic Systems, ICAMechS 2023

作者： Jiang, Tao Zhu, Enzhi Wu, Sidong Zhang, Linshuai Gu, Shuoxin Xu, Lin Wang, Qian Tang, Xianggui Chengdu Univ. of Traditional Chinese Med. Unmanned Syst. Intelligent Percept. Contr. Technol. Eng. Laboratory of Sichuan Province Chengdu University of Information Technology School of Intelligent Medicine Chengdu China Chengdu University of Information Technology School of Automation Chengdu China Chengdu Univ. of Info. Technol. Unmanned Syst. Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology School of Automation Chengdu China Chengdu University of Traditional Chinese Medicine School of Automation Chengdu Univ. of Info. Technol. Intl. Jt. Res. Ctr. of Robotics and Intell. Syst. of Sichuan Prov. School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province School of Automation Chengdu China

ISBN: (纸本)9798350307474

Generally, motion vector information primarily stems from moving objects, while static objects contribute minimally to the estimation task. Traditional approaches for motion vector estimation typically rely on scene flow methods that depend on deep models to extract features from individual points at a high cost. These methods then acquire flow information through complex matching mechanisms or feature decoding. Such approaches are computationally expensive and exhibit substantial latency. Moreover, they neglect the importance of motion objects in motion vector estimation and the interference from static objects. Therefore, this paper introduces a novel method that first performs point cloud motion segmentation and subsequently estimates motion vectors. This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks. © 2023 IEEE.

关键词： Errors

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