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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Zhang, Xuyang Luo, Shan The Robot Perception Lab Centre for Robotics Research Department of Engineering King's College London LondonWC2R 2LS United Kingdom The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force labels. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force labels is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

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Online Gyro Bias Estimation from Single Vector Measurements Using Regression Models

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IFAC-PapersOnLine 2023年第1期56卷 252-257页

作者： Bowen Yi Lei Wang Weidong Zhang Australian Centre for Field Robotics The University of Sydney NSW 2006 Australia State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China School of Information and Communication Engineering Hainan University Haikou 570228 China Department of Automation Shanghai Jiao Tong University Shanghai 200240 China

This paper addresses the problem of on-line consistent estimation of gyro bias using the measurements of a single vector and the biased angular velocity - both in the body-fixed frame. We propose two globally convergent gyro bias observers using new regression models, which are capable to deal with the cases of constant and time-varying reference vectors, respectively. Indeed, there are quite a few works discussing the latter case. To address this, we derive a nonlinear regression model, based on which a convexified gradient descent observer is designed, providing globally asymptotically convergent estimates to the gyro bias under some sufficient excitation conditions. The proposed schemes are illustrated by some numerical simulations.

关键词： Nonlinear systems observer parameter identification robotics

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Hybrid Disassembly Line Balancing for Human-robot Collaborative Remanufacturing

Hybrid Disassembly Line Balancing for Human-robot Collaborat...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Feng Guo XiWang Guo MengChu Zhou Weitian Wang ShuJin Qin Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu P R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at amplifying efficiency through the utilization of parallelization and human-robot collaboration in the context of a hybrid disassembly line. A single-objective mixed-integer programming model is developed to optimize disassembly profit. A discrete salp swarm algorithm is proposed to solve it since it is NP-hard. We have proposed and integrated uniform variation and two-point crossover operators in this algorithm. After comparing its results with those of the exact solver and other intelligent optimization methods, we conclude its competitive performance in both solution quality and efficiency.

关键词： Computer aided software engineering Automation Optimization methods Human-robot interaction Collaboration Programming

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

arXiv

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Jiang, Jiaqi Luo, Shan Robot Perception Lab Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Optimized 3D Drone Placement and Resource Allocation for LTE-Based M2M Communications 91

Optimized 3D Drone Placement and Resource Allocation for LTE...

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91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Fahim, Ahmed Gadallah, Yasser Robotics Control and Smart Systems American University in Cairo Department of Electronics and Communications Engineering New Cairo11835 Egypt

ISBN: (纸本)9781728152073

The deployment of drone-mounted communication systems has received increasing interest and attention recently as it allows significant improvement to the network access capacity and coverage. Many applications can benefit from such deployments in particular machine-to-machine (M2M) communications. In this study, we propose a technique for the rapid deployment of an LTE drone-mounted base station to serve a group of machine-type-communication devices (MTCDs) that are deployed in disaster situations or within remote applications. The objective is to maximize the number of served MTCDs while meeting their transmission delay requirements. This results in optimizing the 3D placement of the drone base station as well as the allocation of the available communication resources. We evaluated the proposed technique through several simulation experiments. The simulation results demonstrate significant improvement in the aggregate system throughput and the overall system deadline missing performance along with the increased number of served MTCDs compared to other drone placement approaches. © 2020 IEEE.

关键词： Machine-to-machine communication

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Efficient Distributed Learning in Stochastic Non-cooperative Games without Information Exchange

arXiv

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arXiv 2022年

作者： Li, Haidong Sheng, Anzhi Peng, Yijie Wang, Long Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China Center for Systems and Control College of Engineering Peking University Beijing China Guanghua School of Management Peking University Beijing China

In this work, we study stochastic non-cooperative games, where only noisy black-box function evaluations are available to estimate the cost function for each player. Since each player's cost function depends on both its own decision variables and its rivals' decision variables, local information needs to be exchanged through a center/network in most existing work for seeking the Nash equilibrium. We propose a new stochastic distributed learning algorithm that does not require communications among players. The proposed algorithm uses simultaneous perturbation method to estimate the gradient of each cost function, and uses mirror descent method to search for the Nash equilibrium. We provide asymptotic analysis for the bias and variance of gradient estimates, and show the proposed algorithm converges to the Nash equilibrium in mean square for the class of strictly monotone games at a rate faster than the existing algorithms. The effectiveness of the proposed method is buttressed in a numerical experiment. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

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Predictor-Based Constrained Fixed-Time Sliding Mode control of Multi-Uav Formation Flight

SSRN

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SSRN 2024年

作者： Khodaverdian, Maria Hajshirmohamadi, Shahram Hakobyan, Aleksandr Ijaz, Salman Center for Scientific Innovation and Education Aerial Robotics Laboratory of National Polytechnic University of Armenia Yerevan Armenia Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Key Laboratory of More Electric Aircraft and Control System Lab University of Nottingham Ningbo China

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicles formation flight. The proposed approach simultaneously addresses various practical requirements, such as optimality, constraints, fixed-time convergence, external interferences, and computational ***, a constrained performance index is constructed based on the prediction of the fixed-time sliding surface, the reaching law, and the control input. Secondly, optimal control input is calculated by solving the constrained quadratic optimization problem. An outstanding feature of this study is the theoretical guarantee of closed-loop system stability in the presence of external disturbances and ***, the comparative evaluation with sliding mode predictive control law and fixed-time sliding mode control methods demonstrates that the proposed approach achieves a straightforward control method with improved performance and acceptable accuracy. Finally, a test is conducted to demonstrate the algorithm's potential for hardware implementation by employing Hardware-in-the-Loop (HIL) test. © 2024, The Authors. All rights reserved.

关键词： Sliding mode control

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LS-SVM Combined with ZNN for Predicting the Continuous Motion Joint Angle of Lower Limb 10

LS-SVM Combined with ZNN for Predicting the Continuous Motio...

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10th IEEE Data Driven control and Learning Systems Conference, DDCLS 2021

作者： Zhang, Xin Liu, Keping Li, Chunxu Yi, Jiang Duan, Xiaoqin Sun, Zhongbo Changchun University of Technology Department of Control Engineering Changchun130000 China University of Plymouth Centre for Robotics and Neural Systems PlymouthPL48AA United Kingdom Medicine of the Second Hospital of Jilin University Department of Rehabilitation Changchun130000 China

ISBN: (纸本)9781665424233

In this paper, a method fusing least squares support vector machine (LS-SVM) with Gaussian kernel function and zeroing neural network (ZNN) is proposed to forecast the continuous motion of lower limb. The surface electromyography (sEMG) signal contains human behavior information and directly reflects the movement intention. In the experiment, the sEMG signal of the lower limbs when the tester is doing leg extension exercise is collected and the real knee joint angle is recorded at the same time. Then the raw sEMG is subjected to a series of preprocessing, and the corresponding muscle activation is gained by calculation. The muscle activation is used as input to the LS-SVM model, and the output to the model is the knee joint angle. LS-SVM transforms the original problem into solving linear equations. When the amount of data is relatively large, the traditional solution method is very time-consuming, and the proposed ZNN method is able to solve the problem, thus speeding up the convergence speed and greatly reducing the learning time. Finally, the back propagation neural network (BPNN) model is utilized to form a comparative experiment. The numerical results indicate that a more stable and better performance is reflected in the raised method, which provides a valuable reference for the research of joint continuous estimation. © 2021 IEEE.

关键词： Numerical methods

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Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 753-765页

作者： Jun Ye Spandan Roy Milinko Godjevac Vasso Reppa Simone Baldi Department of Maritime and Transport Technology Delft University of TechnologyDelft 2628 CDThe Netherlands Robotics Research Center International Institute of Information Technology HyderabadHyderabad-500032India Future Proof Shipping Delft 3013 ALThe Netherlands School of Mathematics and School of Cyber Science and Engineering Southeast UniversityNanjing 210096China the Delft Center for System and Control Delft University of TechnologyDelft 2600 AA

Construction crane vessels make use of dynamic positioning(DP)systems during the installation and removal of offshore structures to maintain the vessel’s *** have reported cases of instability of DP systems during offshore operation caused by uncertainties,such as mooring ***"robustification"for heavy lift operations,i.e.,handling such uncertainties systematically and with stability guarantees,is a long-standing challenge in DP design.A new DP method,composed by an observer and a controller,is proposed to address this challenge,with stability guarantees in the presence of *** test the proposed method on an integrated cranevessel simulation environment,where the integration of several subsystems(winch dynamics,crane forces,thruster dynamics,fuel injection system etc.)allow a realistic validation under a wide set of uncertainties.

关键词： Construction crane vessels dynamic positioning system offshore robust control uncertainty

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Direct LiDAR-Inertial Odometry and Mapping: Perceptive and Connective SLAM

arXiv

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arXiv 2023年

作者： Chen, Kenny Nemiroff, Ryan Lopez, Brett T. Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States The Department of Mechanical and Aerospace Engineering University of California Los Angeles Los AngelesCA United States The Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los AngelesCA United States

This paper presents Direct LiDAR-Inertial Odometry and Mapping (DLIOM), a robust SLAM algorithm with an explicit focus on computational efficiency, operational reliability, and real-world efficacy. DLIOM contains several key algorithmic innovations in both the front-end and back-end subsystems to design a resilient LiDAR-inertial architecture that is perceptive to the environment and produces accurate localization and high-fidelity 3D mapping for autonomous robotic platforms. Our ideas spawned after a deep investigation into modern LiDAR SLAM systems and their inabilities to generalize across different operating environments, in which we address several common algorithmic failure points by means of proactive safe-guards to provide long-term operational reliability in the unstructured real world. We detail several important innovations to localization accuracy and mapping resiliency distributed throughout a typical LiDAR SLAM pipeline to comprehensively increase algorithmic speed, accuracy, and robustness. In addition, we discuss insights gained from our ground-up approach while implementing such a complex system for real-time state estimation on resource-constrained systems, and we experimentally show the increased performance of our method as compared to the current state-of-the-art on both public benchmark and self-collected datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

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