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Design of a High Speed 360-degree Panoramic Video Acquisition System Based on FPGA and USB 3.0

IEEE Sensors Journal 2023年 1-1页

作者： Lyu, Congyi Peng, Jianqing Zhou, Weiguo Yang, Shanshan Liu, Yunhui Intelligent Robotics Institute Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Mechatronical Engineering Beijing Institute of Technology Beijing China

This paper presents a FPGA-based hardware architecture for capturing real-time panoramic images and the parallel implementation of an image stitching algorithm based on the scale-invariant feature transform (SIFT) features. First, the system transfers the images captured by five CMOS sensors to the designed FPGA board through the FMC adapter plate. Then, the system employs the FPGA-based parallel computing to extract the SIFT features and detect the best matching region and points for image registration. Finally, in order to obtain the panoramic image with smooth changes at the junctions, images of the adjacent images are fused. Using the designed FPGA platform makes it possible to extract the SIFT and stitch the images in real-time, which is not realized in most panoramic imaging systems. The panoramic system, developed using the Xilinx Zynq®-7000 all programmable SOC, can capture video of 5*640*480 images at the speed of 60 fps. To enable high-speed image transfer from the imaging system to the host computer, the USB3.0 interface with the transmission speed of 359MB/s has been developed. The real-time efficiency of the parallel system will be demonstrated by the simulations and experiments. IEEE

关键词： Real time systems

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Intelligent Collective Escape of Swarm Robots Based on a Novel Fish-inspired Self-adaptive Approach with Neurodynamic Models

arXiv

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arXiv 2024年

作者： Li, Junfei Yang, Simon X. The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada

Fish schools present high-efficiency group behaviors through simple individual interactions to collective migration and dynamic escape from the predator. The school behavior of fish is usually a good inspiration to design control architecture for swarm robots. In this paper, a novel fish-inspired self-adaptive approach is proposed for collective escape for the swarm robots. In addition, a bio-inspired neural network (BINN) is introduced to generate collision-free escape robot trajectories through the combination of attractive and repulsive forces. Furthermore, to cope with dynamic environments, a neurodynamics-based self-adaptive mechanism is proposed to improve the self-adaptive performance of the swarm robots in the changing environment. Similar to fish escape maneuvers, simulation and experimental results show that the swarm robots are capable of collectively leaving away from the threats. Several comparison studies demonstrated that the proposed approach can significantly improve the effectiveness and efficiency of system performance, and the flexibility and robustness in complex environments. © 2024, CC BY.

关键词： Fish

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Safety-Aware Trajectory Tracking using High-Order Control Barrier Functions

Safety-Aware Trajectory Tracking using High-Order Control Ba...

引用

IEEE Conference on Decision and Control

作者： Panagiotis Rousseas Dimitra Panagou Kostas Kyriakopoulos Control Systems Laboratory School of Mechanical Engineering National Technical University of Athens Greece Department of Robotics and the Department of Aerospace Engineering University of Michigan MI USA Center of AI & Robotics (CAIR) New York University Abu Dhabi

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

A novel method on safety-aware trajectory tracking is presented in this work. The proposed scheme employs Control Barrier Functions (CBFs) along with a latent-space representation over the barrier and its derivatives. While such representations have also been employed in the literature, the innovation of the proposed method lies in formulating a novel tracking problem on the latent space, enabling for the shape of linear class $-\mathcal{K}$ functions that are included in the safety condition induced by the control barrier function to be chosen constructively. Additionally, the aforementioned safety condition is modified based on the latent-space tracking through the addition of an extra term. This enables maintaining safety guarantees and is demonstrated to provide decreased tracking errors compared to the nominal control barrier function scheme. Numerical simulations validate the efficacy of the proposed scheme, as well as its ability for online implementation.

关键词： Technological innovation Trajectory tracking Shape Aerospace electronics Numerical simulation Safety

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Chance Constrained Routing and Charging Problem of Electric Vehicles

Chance Constrained Routing and Charging Problem of Electric ...

引用

2022 Chinese Automation Congress, CAC 2022

作者： Chen, Shibo Yao, Canqi Miao, Haoyu Li, Zhicheng Yang, Zaiyue The Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Internet of Things Research Institute Shenzhen Polytechnic Shenzhen518055 China

ISBN: (纸本)9781665465335

Different from traditional vehicles, the routing problem of electric vehicles (EVs) is closely coupled with their charging. Hence, operating EV requires a joint optimization of both routing and charging. Moreover, the travel time and energy consumption uncertainties also bring challenges to the EV operation in practice. In this paper, we study the routing and charging problem of EVs under the chance constrained optimization framework, where uncertainties are tackled with probabilistic constraints. An iterative algorithm is proposed, which transforms the original challenging mixed-integer nonlinear program into the iteration of two linear programs. The finite convergence of this algorithm is also proved. Numerical results show that our proposed algorithm can achieve near-optimal solutions in rather short computation times. © 2022 IEEE.

关键词： Electric vehicles

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Recognition of Dynamic Grasping Motion through Feature Extraction for Teleoperation 5

Recognition of Dynamic Grasping Motion through Feature Extra...

引用

5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Dai, Yanping Chen, Wenyuan Xi, Ning Wang, Wenxue Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Emerging Technologies Institute University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong

ISBN: (纸本)9798350307320

Detecting human movement intentions and transmitting human operational capabilities to robots is of great significance for teleoperations. However, the difference in kinematic structure between human hands and robotic manipulators brings difficulties to teleoperation mapping. In this paper, we present an approach to hand grasp recognition and mapping based on feature extraction to drive heterogeneous remote manipulator. The hand grasp data set HUST recorded with a data glove was used for this study. The data were preprocessed and their features were extracted by PCA. A neural network-based classifier was obtained through offline training of human grasping activities. We compared different network models and selected the network with the best performance based on the training time and the task requirements of teleoperation. The experimental results showed that the recognition accuracy of dynamic grasping motion with feature extraction is higher than that without feature extraction and can be up to 97.47%. Finally, we validated the feasibility of this method to tele-operate an Barrett hand through online experiments. Our proposed method provides an effective approach of mapping natural human grasping operations to heterogeneous robotic teleoperation systems. © 2023 IEEE.

关键词： Feature extraction

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POMDP-Based Real-Time Path Planning for Manipulation of Multiple Microparticles via Optoelectronic Tweezers

引用

Cyborg and Bionic systems 2022年第1期2022卷 303-313页

作者： Jiaxin Liu Huaping Wang Menghua Liu Ran Zhao Yanfeng Zhao Tao Sun Qing Shi Intelligent Robotics Institute School of Mechatronical Engineering Bejing Institute of TechnologyBeijing 10081China Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China School of Medical Technology Beijing Institute of TechnologyBeijing 100081China Bejing Advanced Innovation Center for Itelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China

With high throughput and high flexibility,optoelectronic tweerers(OETs)hold huge potential for massively parallel ***,the trajectory of the virtual electrode has been planned in advance in most synchronous manipulations for multiple targets based on an optically induced dielectrophoresis(ODEP)mechanism,which is insuficient to ensure the stability and eficiency in an environment with potential collision *** this paper,a synchronously discretized manipulation method based on a centralized and decoupled path planner is proposed for transporting microparticles of diferent types with an OET *** approach based on the K uhn-Munkres algorithm is utilized to achieve the goal assignment between target microparticles and goal *** the assistance of a visual feedback module,a path planning approach based on the POMDP algorithm dynamically determines the motion strategies of the particle movement to avoid potential *** geometrical parameters of the virtual electrodes are optimized for different types of particles with the goal of maximum transport *** experiments of micropatterning with different morphologies and transporting multiple micropartides(e.g,polystyrene microspheres and 3T3 cells)to goal positions are *** results demonstrate that the proposed manipulation method based on optoelectronic tweezers is effective for multicell transport and promises to be used in biomedical manipulation tasks with high flexibility and efficiency.

关键词： manipulation positions assignment

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A Survey of Deep Learning-based Hand Pose Estimation 8

A Survey of Deep Learning-based Hand Pose Estimation

引用

8th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Wang, Shuaibing Wang, Shunli Kuang, HaoPeng Li, Fang Qian, Ziyun Li, Mingcheng Zhang, Lihua Fudan University Academy for Engineering & Technology China Ministry of Education Engineering Research Center of Ai and Robotics China Jilin Provincial Key Laboratory of Intelligence Science & Engineering China Ai & Unmanned Systems Engineering Research Center of Jilin Province China

ISBN: (纸本)9781665477352

Hand pose estimation is one of the representative tasks in computer vision. Solving the hand pose estimation problem is essential for various fields such as virtual reality, augmented reality, mixed reality, and human-computer interaction. Due to the significant development of deep learning techniques, the hand pose estimation task has reached significant performance on many hand pose estimation datasets. However, the hand pose estimation task still faces many challenges due to the lack of large-scale labeled data, severe occlusion, low hand resolution, and background clutter. To better understand the hand pose estimation task, this paper presents a comprehensive survey of outstanding papers over the last five years. The paper first introduces 19 common hand pose estimation datasets, then extensively discusses some of the mainstream approaches in hand pose estimation, including fully supervised, semi-supervised, weakly supervised, and self-supervised learning methods. Finally, we extensively discuss the future evolution trends of hand pose estimation. © 2022 IEEE.

关键词： Computer vision

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An Efficient Learning Control Framework with Sim-to-Real for String-Type Artificial Muscle-Driven Robotic systems

arXiv

引用

arXiv 2024年

作者： Tao, Jiyue Zhang, Yunsong Rajendran, Sunil Kumar Zhang, Feitian The Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering the State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China The BSS Technologies Inc. Takoma ParkMD20912 United States

Robotic systems driven by artificial muscles present unique challenges due to the nonlinear dynamics of actuators and the complex designs of mechanical structures. Traditional model-based controllers often struggle to achieve desired control performance in such systems. Deep reinforcement learning (DRL), a trending machine learning technique widely adopted in robot control, offers a promising alternative. However, integrating DRL into these robotic systems faces significant challenges, including the requirement for large amounts of training data and the inevitable sim-to-real gap when deployed to real-world robots. This paper proposes an efficient reinforcement learning control framework with sim-to-real transfer to address these challenges. Bootstrap and augmentation enhancements are designed to improve the data efficiency of baseline DRL algorithms, while a sim-to-real transfer technique, namely randomization of muscle dynamics, is adopted to bridge the gap between simulation and real-world deployment. Extensive experiments and ablation studies are conducted utilizing two string-type artificial muscle-driven robotic systems including a two degree-of-freedom robotic eye and a parallel robotic wrist, the results of which demonstrate the effectiveness of the proposed learning control strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Design and Control for WLR-3P:A Hydraulic Wheel-Legged Robot

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Cyborg and Bionic systems 2023年第1期4卷 243-259页

作者： Xu Li Haoyang Yu Haibo Feng Songyuan Zhang Yili Fu State Key Laboratory of Robotics and System College of Mechatronics EngineeringHarbin Institute of TechnologyHarbin 150001China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Intelligent Robot Research Center Zhejiang LaboratoryHangzhou 311100China

The robot used for disaster rescue or field exploration requires the ability of fast moving on flat road and adaptability on complex *** hybrid wheel-legged robot(WLR-3P,prototype of the third-generation hydraulic wheel-legged robot)has the characteristics of fast and efficient mobility on flat surfaces and high environmental adaptability on rough *** this paper,3 design requirements are proposed to improve the mobility and environmental adaptability of the *** meet these 3 requirements,2 design principles for each requirement are put ***,for light weight and low inertia with high stiffness,3-dimensional printing technology and lightweight material are ***,the integrated hydraulically driven unit is used for high power density and fast response ***,the microhydraulic power unit achieves power autonomy,adopting the hoseless design to strengthen the reliability of the hydraulic *** is more,the control system including hierarchical distributed electrical system and control strategy is *** mobility and adaptability of WLR-3P are demonstrated with a series of ***,the robot can achieve a speed of 13.6 km/h and a jumping height of 0.2 m.

关键词： robot Robot terrain

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