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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Sun, Chenxi Huang, Chao Shou, Biying Huang, Jianwei Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Computer Science University of California Davis United States Department of Management Science City University of Hong Kong Hong Kong Hong Kong Shenzhen China

ISBN: (纸本)9798350396782

Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on developing FL algorithms, the effect of FL on the charging stations’ operation in terms of price competition has yet to be fully understood. This paper aims to fill this gap by modeling the strategic interactions between two charging stations and EV owners as a multi-stage game. Each station first decides its FL participation strategy and charging price, and then individual EV owners decide their charging strategies. The game analysis involves solving a non-concave problem and by decomposing it into a piece-wise concave program we manage to fully characterize the equilibrium. Based on real-world datasets, our numerical results reveal an interesting insight: even if FL improves QoS, it can lead to smaller profits for both stations. The key reason is that FL intensifies the price competition between charging stations by improving stations’ QoS to a similar level. We further show that the stations will participate in FL when their data distributions are mildly dissimilar. © 2023 IEEE.

关键词： charging stations competitive pricing Electric vehicles (EV) federated learning (FL) game theory

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Mechatronic Design of a Shank-Free Bilateral Exoskeleton for Loaded Walking

Mechatronic Design of a Shank-Free Bilateral Exoskeleton for...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Zhihao Zhou Ming Xu Zezheng Wang Han Gao Jingeng Mai Qining Wang Institute for Artificial Intelligence Peking University Beijing China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering Beijing China Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China Department of Advanced Manufacturing and Robotics College of Engineering and the Institute for Artificial Intelligence Peking University Beijing China University of Health and Rehabilitation Sciences Qingdao China Beijing Institute for General Artificial Intelligence (BIGAI) Beijing China

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

The shank-free exoskeleton is proposed to increase the strength and endurance of the human during loaded walking. It interfaced with the human via back-waist belts and thigh cuffs, without physical coupling with the biological human shank. Instead of the traditional ankle-foot design, we employed a robotic shank driven by a linear actuator to achieve gravity support. The bilateral design of the exoskeleton was symmetrical, and the mechatronic design of the exoskeleton was described in detail. Metabolic expenditure measurement experiments were conducted on three able-bodied subjects walking with 27.1 kg of carried mass (including 15.5 kg of the exoskeleton), and the results exhibited an average metabolic reduction of 10.94%, comparing exoskeleton worn powered versus unpowered. This study introduces a potential way for load-carrying augmentation.

关键词： Legged locomotion Couplings Actuators Mechatronics Exoskeletons Thigh Belts

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Coordinate transformation and connection feature for Skeleton-based action recognition

Coordinate transformation and connection feature for Skeleto...

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2021 China Automation Congress, CAC 2021

作者： Zheng, Junling Chan, Wensong Du, Shaoyi Zhao, Fei Tian, Zhiqiang School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics

ISBN: (纸本)9781665426473

Graph structure is an important part of Graph convolutional networks (GCNs), which can reflect the connection between each nodes of non-Euclidean data. A connection feature between nodes is hidden in graph structure, which can provide additional spatial features that represent the relationship between human joints. However many GCNs-based methods ignore these spatial features. We put forward a connection feature extraction module, which can obtain implicit connection between human joints, and extract the implicit spatial features from the structural connection and implicit connection of human joints. In order to enhance the temporal representation, we propose a long-range frame-difference feature extraction module. Furthermore, we also propose a coordinate transformation module, which can map joint from Cartesian coordinates to spherical coordinates to extract more representative features. Experiments show that our method outperforms several advanced methods. © 2021 IEEE

关键词： Feature extraction

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Robust Nonprehensile Object Transportation with Uncertain Inertial Parameters

arXiv

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arXiv 2024年

作者： Heins, Adam Schoellig, Angela P. The Learning Systems and Robotics Lab The Technical University of Munich Germany The University of Toronto Institute for Aerospace Studies Canada The Vector Institute for Artificial Intelligence Germany

We consider the nonprehensile object transportation task known as the waiter’s problem—in which a robot must move an object balanced on a tray from one location to another—when the balanced object has uncertain inertial parameters. In contrast to existing approaches that completely ignore uncertainty in the inertia matrix or which only consider small parameter errors, we are interested in pushing the limits of the amount of inertial parameter uncertainty that can be handled. We first show how balancing constraints robust to inertial parameter uncertainty can be incorporated into a motion planning framework to balance objects while moving quickly. Next, we develop necessary conditions for the inertial parameters to be realizable on a bounding shape based on moment relaxations, allowing us to verify whether a trajectory will violate the balancing constraints for any realizable inertial parameters. Finally, we demonstrate our approach on a mobile manipulator in simulations and real hardware experiments: our proposed robust constraints consistently balance a 56 cm tall object with substantial inertial parameter uncertainty in the real world, while the baseline approaches drop the object while transporting it. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Soft Robot Kinematic Control Via Manipulability-Aware Redundancy Resolution

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Journal of Mechanisms and robotics 2025年第7期17卷 071013页

作者： Azizkhani, Milad Ha, Junhyoung Gunderman, Anthony L. Chen, Yue Department of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 Center for Healthcare Robotics Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul 02792 South Korea Department of Mechanical Engineering University of Arkansas Fayetteville AR 72701 Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332

This article addresses the kinematic control of a redundant soft robotic arm. Full pose kinematic control of soft robots is challenging because direct application of the classical controllers developed based on rigid robots to soft robots could lead to unreliable or infeasible motions. In this study, we explore the manipulability property of a soft robotic arm and develop an advanced resolved-rate controller that prioritizes position over orientation control and switches its modes and gains based on position and orientation manipulabilities, enabling stable motion even when the robot is close to the singular configurations. The simulation and experimental results indicate that our proposed method outperforms previous methods in terms of both accuracy and smoothness during operation.

关键词： Resolution (Optics) Kinematics bio-inspired Simulation Errors Redundancy (Engineering) continuum robotics Robots soft robotics Soft robots robotics kinematics Algorithms

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Few-shot Underwater Acoustic Target Recognition Based on Siamese Network 42

Few-shot Underwater Acoustic Target Recognition Based on Sia...

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42nd Chinese Control Conference, CCC 2023

作者： Yang, Haizhou Liu, Meiqin Zhang, Senlin Zheng, Ronghao Dong, Shanling Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9789887581543

Underwater acoustic target recognition is a widely investigated issue in the field of underwater acoustics. Many good results have been reported for underwater acoustic target recognition. However, in practical applications, the strong demand for labeled data for underwater acoustic target recognition is a big obstacle. In order to solve this problem, researchers have explored few-shot learning and unsupervised methods in various papers. A Siamese network is proposed which is composed of one-dimensional convolution and Long Short-Term Memory (LSTM) neural networks, called 1DCLSN. A structure for 1DCLSN is designed which combines contrastive information with label information and obtains satisfactory recognition results. In addition, the contrastive loss function with a different clustering term is modified to improve the performance. With only few labeled training samples, the performance of the proposed approach is better than those of other deep learning methods. The experiment shows the great potential of our method. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Underwater acoustics

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An Anti-Swing Controller for Aerial Transportation System with Flexible Suspending Rope 5

An Anti-Swing Controller for Aerial Transportation System wi...

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5th Asian Conference on artificial intelligence Technology, ACAIT 2021

作者： Zhang, Zhaopeng Liang, Xiao Zhang, Zhuang Han, Jianda College of Artificial Intelligence Nankai University Institute of Robotics and Automatic Information System Tianjin China

ISBN: (纸本)9781665426305

Numerous researches on aerial transportation uses the assumption that the payload is connected via a rigid rope. Although this assumption makes sense in most situations, it will still lose effectiveness occasionally. Therefore, this paper considers the dynamics and control of a system with flexible rope. Based on assumed mode method(AMM) and the assumption that the length of the rope remains unchanged, the dynamic model of this system is established by Euler-Lagrange techniques. Subsequently, through the energy-based analysis method, we design a nonlinear anti-swing controller. Asymptotic results are obtained with rigorous theoretical derivations provided by the Lyapunov-based stability analysis and LaSalle's invariance theorem. Two groups of simulation results are provided to demonstrate the superior performance of the proposed controller. © 2021 IEEE.

关键词： Controllers

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CoreNet: Conflict Resolution Network for Point-Pixel Misalignment and Sub-Task Suppression of 3D LiDAR-Camera Object Detection

arXiv

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arXiv 2025年

作者： Li, Yiheng Yang, Yang Lei, Zhen Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences 999077 Hong Kong

Fusing multi-modality inputs from different sensors is an effective way to improve the performance of 3D object detection. However, current methods overlook two important conflicts: point-pixel misalignment and sub-task suppression. The former means a pixel feature from the opaque object is projected to multiple point features of the same ray in the world space, and the latter means the classification prediction and bounding box regression may cause mutual suppression. In this paper, we propose a novel method named Conflict Resolution Network (CoreNet) to address the aforementioned issues. Specifically, we first propose a dual-stream transformation module to tackle point-pixel misalignment. It consists of ray-based and point-based 2D-to-BEV transformations. Both of them achieve approximately unique mapping from the image space to the world space. Moreover, we introduce a task-specific predictor to tackle sub-task suppression. It uses the dual-branch structure which adopts class-specific query and Bbox-specific query to corresponding sub-tasks. Each task-specific query is constructed of task-specific feature and general feature, which allows the heads to adaptively select information of interest based on different sub-tasks. Experiments on the large-scale nuScenes dataset demonstrate the superiority of our proposed CoreNet, by achieving 75.6% NDS and 73.3% mAP on the nuScenes test set without test-time augmentation and model ensemble techniques. The ample ablation study also demonstrates the effectiveness of each component. The code is released on https://***/liyih/CoreNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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Ultra-wideband Time Difference of Arrival Indoor Localization: From Sensor Placement to System Evaluation

arXiv

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arXiv 2024年

作者： Zhao, Wenda Goudar, Abhishek Tang, Mingliang Schoellig, Angela P. University of Toronto Institute for Aerospace Studies Vector Institute for Artificial Intelligence Canada Technische Universität München Munich Institute for Robotics and Machine Intelligence Germany University of California Berkeley United States

Wireless indoor localization has attracted significant research interest due to its high accuracy, low cost, lightweight design, and low power consumption. Specifically, ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has emerged as a scalable positioning solution for mobile robots, consumer electronics, and wearable devices, featuring good accuracy and reliability. While UWB TDOA-based localization systems rely on the deployment of UWB radio sensors as positioning landmarks, existing works often assume these placements are predetermined or study the sensor placement problem alone without evaluating it in practical scenarios. In this article, we bridge this gap by approaching the UWB TDOA localization from a system-level perspective, integrating sensor placement as a key component and conducting practical evaluation in real-world scenarios. Through extensive real-world experiments, we demonstrate the accuracy and robustness of our localization system, comparing its performance to the theoretical lower bounds. Using a challenging multi-room environment as a case study, we illustrate the full system construction process, from sensor placement optimization to real-world deployment. Our evaluation, comprising a cumulative total of 39 minutes of real-world experiments involving up to five agents and covering 2608 meters across four distinct scenarios, provides valuable insights and guidelines for constructing UWB TDOA localization systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Ultra-wideband (UWB)

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Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation

Leveraging GNSS and Onboard Visual Data from Consumer Vehicl...

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

作者： Balázs Opra Betty Le Dem Jeffrey M. Walls Dimitar Lukarski Cyrill Stachniss Woven by Toyota Inc University of Bonn Germany Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This paper addresses the challenge of road graph construction for autonomous vehicles. Despite recent advances, creating a road graph remains labor-intensive and has yet to achieve full automation. The goal of this paper is to generate such graphs automatically and accurately. Modern cars are equipped with onboard sensors used for today's advanced driver assistance systems like lane keeping. We propose using global navigation satellite system (GNSS) traces and basic image data acquired from these standard sensors in consumer vehicles to estimate road-level maps with minimal effort. We exploit the spatial information in the data by framing the problem as a road centerline semantic segmentation task using a convolutional neural network. We also utilize the data’s time series nature to refine the neural network’s output by using map matching. We implemented and evaluated our method using a fleet of real consumer vehicles, only using the deployed onboard sensors. Our evaluation demonstrates that our approach not only matches existing methods on simpler road configurations but also significantly outperforms them on more complex road geometries and topologies. This work received the 2023 Woven by Toyota Invention Award.

关键词： Global navigation satellite system Technological innovation Visualization Accuracy Roads Weaving Sensor systems Sensors Automobiles Vehicles

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