检索结果-内蒙古大学图书馆

Joint routing and charging problem of multiple electric vehicles: A fast optimization algorithm

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Yao, Canqi Chen, Shibo Yang, Zaiyue School of Mechatronics Engineering Harbin Institute of Technology Harbin150000 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

Logistics has gained great attentions with the prosperous development of e-commerce, which is often seen as the classic optimal vehicle routing problem. Meanwhile, electric vehicle (EV) has been widely used in logistic fleet to curb the emission of green house gases in recent years. Thus, solving the optimization problem of joint routing and charging of multiple EVs is in a urgent need, whose objective function includes charging time, charging cost, EVs travel time, usage fees of EV and revenue from serving customers. This joint problem is formulated as a mixed integer programming (MIP) problem, which, however, is NP-hard due to integer restrictions and bilinear terms from the coupling between routing and charging decisions. The main contribution of this paper lies at proposing an efficient two-stage algorithm that can decompose the original MIP problem into two linear programming (LP) problems, by exploiting the exactness of LP relaxation and eliminating the coupled term. This algorithm can achieve a near-optimal solution in polynomial time. In addition, another variant algorithm is proposed based on the two-stage one, to further improve the quality of solution. Compared with the state-of-the-art algorithm, extensive simulations are implemented to validate the effectiveness of the proposed algorithm. © 2021, CC BY-NC-SA.

关键词： Electric vehicles

MTPA-based Haptic Feedback Controller for Master Manipulator in Minimally Invasive Surgery

学校读者我要写书评

暂无评论

MTPA-based Haptic Feedback Controller for Master Manipulator...

IEEE International Conference on robotics and Biomimetics

作者： Wenda Zhao Jian Chen Yilun Hong Hongbin Liu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong China School of Biomedical Engineering and Imaging Sciences King's College London London UK

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Haptic feedback is critical for teleoperation in surgical robots, particularly in Natural Orifice Transluminal Endoscopic Surgery (NOTES). This paper introduces a haptic controller designed to enhance force generation efficiency and resolution, surpassing traditional motor control frameworks. We propose a Maximum Torque Per Ampere (MTPA)-based Field-Oriented Control (FoC) strategy for haptic kinesthetic feedback controller, optimizing the performance of a master manipulator's joint with a brushless DC (BLDC) motor under stalled and ultra-slow conditions. This approach enables a master manipulator with three degrees of freedom (DoF) for force feedback. Its effectiveness is validated through simulator and surgical trials, demonstrating improved haptic feedback in surgical applications.

关键词： Motor drives Torque Minimally invasive surgery Medical robotics Force feedback Orifices Manipulators Motors Haptic interfaces Adaptive control

Riemannian Optimization for Distance-Geometric Inverse Kinematics

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Marić, Filip Giamou, Matthew Hall, Adam W. Khoubyarian, Soroush Petrović, Ivan Kelly, Jonathan Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Dynamic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada

Solving the inverse kinematics problem is a fundamental challenge in motion planning, control, and calibration for articulated robots. Kinematic models for these robots are typically parametrized by joint angles, generating a complicated mapping between the robot configuration and the end-effector pose. Alternatively, the kinematic model and task constraints can be represented using invariant distances between points attached to the robot. In this paper, we formalize the equivalence of distance-based inverse kinematics and the distance geometry problem for a large class of articulated robots and task constraints. Unlike previous approaches, we use the connection between distance geometry and low-rank matrix completion to find inverse kinematics solutions by completing a partial Euclidean distance matrix through local optimization. Furthermore, we parametrize the space of Euclidean distance matrices with the Riemannian manifold of fixed-rank Gram matrices, allowing us to leverage a variety of mature Riemannian optimization methods. Finally, we show that bound smoothing can be used to generate informed initializations without significant computational overhead, improving convergence. We demonstrate that our inverse kinematics solver achieves higher success rates than traditional techniques, and substantially outperforms them on problems that involve many workspace constraints. Copyright © 2021, The Authors. All rights reserved.

关键词： Motion planning

Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Bin Zhu, Xiangyu Zhang, Xiaoyu Lei, Zhen Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China 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 China

Face anti-spoofing is crucial for ensuring the security and reliability of face recognition systems. Several existing face anti-spoofing methods utilize GAN-like networks to detect presentation attacks by estimating the noise pattern of a spoof image and recovering the corresponding genuine image. But GAN’s limited face appearance space results in the denoised faces cannot cover the full data distribution of genuine faces, thereby undermining the generalization performance of such methods. In this work, we present a pioneering attempt to employ diffusion models to denoise a spoof image and restore the genuine image. The difference between these two images is considered as the spoof noise, which can serve as a discriminative cue for face anti-spoofing. We evaluate our proposed method on several intra-testing and inter-testing protocols, where the experimental results showcase the effectiveness of our method in achieving competitive performance in terms of both accuracy and generalization. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

Heuristic Online Path Planning for Mobile Air Purification and Disinfection Robot In Regulating Indoor Air Quality

学校读者我要写书评

暂无评论

Heuristic Online Path Planning for Mobile Air Purification a...

IEEE International Conference on robotics and Biomimetics

作者： Qingnan Zhong Kai Chen Jianghua Duan Jun Ma Robotics and Human Machine Interface Laboratory HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology China

In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting surfaces and purifying the air. This paper focuses on a mobile robot equipped with air purification and UV-C LED disinfection to combat airborne pathogens and environmental pollution. Through the onboard sensors, the proposed algorithm uses gaussian process model to predict the high potential air pollution area in real-time without the requirement of a distributed array of sensors, and it avoids environmental modifications. Then we propose the hybrid artificial potential field (HAPF) by integrating the predicted result with the artificial potential field method, to generate a heuristic path that can navigate the robot to the affected areas with obstacle avoidance. Experimental testing in simulated and real-world environments demonstrates the effectiveness of the proposed approach in rapidly planning purification paths, marking significant progress in automated air purification, and disinfection technology.

关键词：

Rhythms: Real-Time Data-Driven Human-Machine Synchronization for Proactive Ergonomic Risks Mitigation in the Context of Industry 4.0 and Beyond

学校读者我要写书评

暂无评论

SSRN

SSRN 2022年

作者： Ling, Shiquan Yuan, Yanglang Yan, Danqi Leng, Yuquan Rong, Yiming Huang, George Q. Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

Human-machine work system (HMWS) is a composite system that combines the accuracy and strength of machines in repetitive work with the advantages of humans in cognitive ability, flexibility, and adaptability to perform various tasks with a common goal, which is getting smarter in the era of Industry 4.0 (I4.0) and has received extensive attention from academia and industry. However, due to a lack of real-time information sharing during the implementation and operation of HMWS, the uncertainty brought by human instability and the increasing complexity caused by smart networking environments in the context of I4.0 bring difficulties to the synchronous coordination of this distributed human-centric manufacturing system, while the failure of synchronization will lead to serious violation of overall system integrity, resulting in unrealistic decision-making results and even harmful to human health, which also goes against the propose of Industry 5.0 (I5.0) to put human well-being at the center of the production process to achieve human-centric smart manufacturing in the future. In this context, this research presents a real-time data-driven hyper objects orchestrating for the human-machine synchronization ( RHYTHMS ) based on a service-oriented human-to-machine architecture (SOH2M) and a model reference adaptive fuzzy control for real-time information sharing and synchronous coordination of the smart HMWS. Based on these, a real-life assembly case is carried out by a full-scale HMWS prototype to quantitatively analyze the benefits of the proposed approach in proactive ergonomic risks mitigation (PERM), contributing to the human-centric value-oriented I5.0 era. © 2022, The Authors. All rights reserved.

关键词： Synchronization

Few-shot learning based histopathological image classification of colorectal cancer

学校读者我要写书评

暂无评论

Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

Accurate Parameter Estimation of Time-varying Nuclear Radiation Field with Mobile Sensors

学校读者我要写书评

暂无评论

Accurate Parameter Estimation of Time-varying Nuclear Radiat...

第40届中国控制会议

作者： Bo Chen Zhicheng Li Zaiyue Yang Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology

In this paper, the optimization strategy of mobile sensors and the parameter estimation of time-varying nuclear radiation field are investigated. Based on the measurement model with white Gaussian noise, Fisher information matrix is derived as an information metric. At each time step, finding the best mobile sensor coordinates that contain the amount of maximal information in the 4-Dimension space is formulated as a constrained nonlinear optimization problem, which is able to minimize the trace of Cramer-Rao Lower Bound. Further, an efficient heuristic algorithm called Augmented Lagrangian Genetic Algorithm is adopted to solve the above optimization problem. Then, the parameters of nuclear radiation field are estimated by a maximum likelihood estimator. The proposed sensor coordinates moving strategy has better estimation performance than random coordinates strategy, which is demonstrated via simulation results.

关键词： Optimal sensor coordinates Fisher information matrix(FIM) Cramer-Rao Lower Bound(CRLB) Augmented Lagrangian Genetic Algorithm(ALGA) Maximum likelihood estimate(MLE)

Catch Planner: Catching High-Speed Targets in the Flight

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Yu, Huan Wang, Pengqin Wang, Jin Ji, Jialin Zheng, Zhi Tu, Jie Lu, Guodong Meng, Jun Zhu, Meixin Shen, Shaojie Gao, Fei The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China The Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Institute of Zhejiang University Hangzhou310027 China Hong Kong The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China School of Electrical Engineering Zhejiang University Hangzhou310027 China The Hong Kong University of Science and Technology Guangzhou China

Catching high-speed targets in the flight is a complex and typical highly dynamic task. However, existing methods require manual setting of catching height or time, resulting in lacks of adaptability and flexibility and cannot deal with multiple targets. To bridge this gap, we propose a planning-with-decision scheme called Catch Planner. For sequential decision making, a lightweight policy search method based on deep reinforcement learning is proposed. It is jointly trained with the motion planning and decoupled from physics to speed up training. For motion planning, we propose a trajectory optimization method that jointly optimizes the highly coupled catching time and terminal state. The core is the flexible-terminal constraint transcription. It converts the three unique constraints of catching into differentiable metrics, including equality constraints for terminal position and time, and inequality constraints that enable reasonable terminal position offset and attitude relaxation. In addition, sparse parameterization based on MINCO class considers both dynamic feasibility and collision avoidance constraints. As a result, a generally constrained quadrotor planning problem is transformed into an unconstrained optimization that can be solved reliably and efficiently. We also propose an online iterative optimization method for predicting differentiable trajectories of targets. Catch Planner provides a new paradigm for the combination of learning and planning, where all algorithms can be run in real time onboard at 100hz. Extensive experiments are carried out in real-world and simulated scenes to verify the robustness and expansibility when facing a variety of high-speed flying targets. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making