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A Legged Small Celestial Body Landing Mechanism:Landing Simulation and Experimental Test

Space(Science & technology) 2023年第1期3卷 453-463页

作者： Zhijun Zhao Shuang Wu Jingdong Zhao Yaobing Wang Chuncheng Zhao Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System EngineeringBeijing 100094China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150006China Department of Aerospace Science and Technology Polytechnic University of MilanMilan 20133Italy

Landing mechanism tends to rebound and turn over,and the stability time is long when landing on the small celestial *** landing performance in different conditions is necessary to be evaluated to guide the ***,landing performance evaluation is realized by *** factors affecting the landing performance including cardan element damping,foot anchors,retro-rocket thrust,landing slope angle,and landing attitude are analyzed.A microgravity platform is built to test the landing mechanism,and the consistency between the simulation and the experiment is *** the basis of simulation and experiment,some landing suggestions are proposed to improve the landing performance.

关键词： thrust mechanism landing

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Research on Digital Twin system Platform Framework and key Technologies of Unmanned Ground Equipment

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Complex system Modeling and Simulation 2024年第2期4卷 109-123页

作者： Kunyu Wang Lin Zhang Cheng Xu Han Lu Zhen Chen Hongbo Cheng Rui Guo the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China the State Key Laboratory of Intelligent Manufacturing System Technology Beijing 100854China the Beijing Institute of Electronic System Engineering Beijing 100854China

As an emerging technology,digital twin is expected to bring novel application modes to the whole life cycle process of unmanned ground equipment,including research and development,design,control optimization,operation and maintenance,*** highly dynamic,complex,and uncertain characteristics of unmanned ground equipment and the battlefield environment also pose new challenges for digital twin *** from the new challenges faced by the digital twin of unmanned ground equipment,this paper designs a service-oriented cloud-edge-end collaborative platform architecture of the digital twin system of unmanned ground equipment,and further analyzes several key technologies supporting the implementation of the platform architecture.

关键词： unmanned ground equipment digital twin native cloud platform Modeling and Simulation(M&S) artificial intelligence

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A Fine-Grained Object Detection Model for Aerial Images Based on YOLOv5 Deep Neural Network

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Chinese Journal of Electronics 2023年第1期32卷 51-63页

作者： ZHANG Rui XIE Cong DENG Liwei Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration School of AutomationHarbin University of Science and Technology

Many advanced object detection algorithms are mainly based on natural scenes object and rarely dedicated to fine-grained objects. This seriously limits the application of these advanced detection algorithms in remote sensing object detection. How to apply horizontal detection in remote sensing images has important research significance. The mainstream remote sensing object detection algorithms achieve this task by angle regression, but the periodicity of angle leads to very large losses in this regression method, which increases the difficulty of model learning. Circular smooth label(CSL)solved this problem well by transforming the regression of angle into a classification form. YOLOv5 combines many excellent modules and methods in recent years, which greatly improves the detection accuracy of small *** use YOLOv5 as a baseline and combine the CSL method to learn the angle of arbitrarily oriented targets,and distinguish the fine-grained between instance classes by adding an attention mechanism module to accomplish the fine-grained target detection task for remote sensing images. Our improved model achieves an average category accuracy of 39.2% on the FAIR1M dataset. Although our method does not achieve satisfactory results,this approach is very efficient and simple, reducing the hardware requirements of the model.

关键词： Deep learning Neural networks Object detection Transformers Hardware Classification algorithms Task analysis

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An effective way for sustainable application catalyzed by haem peroxygenases using O2/small-molecule reducing agents

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Science China Chemistry 2025年第6期68卷 2199-2201页

作者： Yinjun Tang Chengzhou Zhu State Key Laboratory of Green Pesticide International Joint Research Center for Intelligent Biosensing Technology and Health College of Chemistry Central China Normal University

Enzymatic biocatalysis harnesses the efficiency, specificity,and environmental friendliness of enzymes to drive innovations in green synthesis, drug development, environmental remediation, and industrial production, m...

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Mechanism of material removal and chip formation of alumina dispersion strengthened copper in micro-milling

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Frontiers of Mechanical Engineering 2025年第1期20卷 27-43页

作者： Chang LIU Chunya WU Xiguang LI Bo HOU Jiahao WU Ruijiang SUN Mingjun CHEN School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

Alumina dispersion-strengthened copper (ADSC), as a representative particle-reinforced metal matrix composite (PRMMC), exhibits superior wear resistance and high strength. However, challenges arise in their processability because of the non-uniform material properties of biphasic materials. In particular, limited research has been conducted on the reinforcement mechanism and behavior of particles during material cutting deformation of PRMMC with nanoscale particles. In this study, a cutting simulation model for ADSC was established, separating the nanoscale reinforcement particles from the matrix. This model was utilized to analyze the interactions among particles, matrix, and tool during the cutting process, providing insights into chip formation and fracture. Particles with high strength and hardness are more prone to storing stress concentrations, anchoring themselves at grain boundaries to resist grain fibration, thereby influencing the stress distribution in the cutting deformation zone. Stress concentration around the particles leads to the formation of discontinuous chips, indicating that ADSC with high-volume fractions of particle (VFP) exhibits low cutting continuity, which is consistent with the results of cutting experiments. The tool tip that is in contact with particles experiences stress concentration, thereby accelerating tool wear. Cutting ADSC with 1.1% VFP results in tool blunting, which increases the radius of cutting edge from 0.5 to 1.9 μm, accompanied with remarkable coating delamination and wear. Simulation results indicate that the minimum uncut chip thickness increases from 0.04 to 0.07 μm as VFP increases from 0.3% to 1.1%. In conjunction with scratch experiments, MUCT increases with the augmentation of VFP. Computational analysis of the specific cutting force indicates that particles contribute to the material’s size effect. The results of this study provide theoretical guidance for practical engineering machining of ADSC, indicating its g

关键词： alumina dispersion-strengthened copper particle-reinforced metal matrix composites material removal mechanism micro-milling chip formation minimum uncut chip thickness

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Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

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Journal of systems Engineering and Electronics 2024年第2期35卷 361-373页

作者： CHEN Chen QUAN Wei SHAO Zhuang School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced ***, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

关键词： target threat assessment gated recurrent unit(GRU) self-attention(SA) fractional Fourier transform(FRFT)

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Learning Agile Quadrotor Flight in Restricted Environments with Safety Guarantees

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年第9期9卷 1-8页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Wu, Xinyu Department of Control Science and Engineering Harbin Institute of Technology Shenzhen and Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the increasing requirement for agile and efficient controllers in safety-critical scenarios, controllers that exhibit both agility and safety are attracting attention, especially in the aerial robotics domain. This paper focuses on the safety issue of Reinforcement Learning (RL)-based control for agile quadrotor flight in restricted environments. To this end, we propose a unified Adaptive Safety Predictive Corrector (ASPC) to certify each output action of the RL-based controller in real-time, ensuring its safety while maintaining agility. Specifically, we develop the ASPC as a finite-horizon optimal control problem, formulated by a variant of Model Predictive Control (MPC). Given the safety constraints determined by the restricted environment, the objective of minimizing loss of agility can be optimized by reducing the difference between the actions of RL and ASPC. As the safety constraints are decoupled from the RL-based control policy, the ASPC is plug-and-play and can be incorporated into any potentially unsafe controllers. Furthermore, an online adaptive regulator is presented to adjust the safety bounds of the state constraints with respect to the environment changes, extending the proposed ASPC to different restricted environments. Finally, simulations and real-world experiments are demonstrated in various restricted environments to validate the effectiveness of the proposed ASPC. IEEE

关键词： Reinforcement learning

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Position-aware pushing and grasping synergy with deep reinforcement learning in clutter

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CAAI Transactions on Intelligence technology 2024年第3期9卷 738-755页

作者： Min Zhao Guoyu Zuo Shuangyue Yu Daoxiong Gong Zihao Wang Ouattara Sie Intelligent Robotics Laboratory Faculty of Information TechnologyBeijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent Systems BeijingChina Laboratory of Biomechatronics and Intelligent Robotics(BIRO) Department of Mechanical and Aerospace EngineeringNorth Carolina State UniversityRaleighNorth CarolinaUSA

The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in *** effectively perform grasping and pushing manipu-lations,robots need to perceive the position information of objects,including the co-ordinates and spatial relationship between objects(e.g.,proximity,adjacency).The authors propose an end-to-end position-aware deep Q-learning framework to achieve efficient collaborative pushing and grasping in ***,a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high-quality affordance maps of operating positions with features of pushing and grasping *** addition,the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial re-lationships between objects in cluttered *** further enhance the perception capacity of position information of the objects,the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function.A series of experiments are carried out in simulation and real-world which indicate that the method improves sample efficiency,task completion rate,grasping success rate and action efficiency compared to state-of-the-art end-to-end *** that the authors’system can be robustly applied to real-world use and extended to novel *** material is available at https://***/NhG\_k5v3NnM}{https://***/NhG\_k5v3NnM.

关键词： deep learning deep neural networks intelligent robots

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A hardware acceleration Framework of Reconfigurable Edge Modules for Convolutional Neural Networks 50

A hardware acceleration Framework of Reconfigurable Edge Mod...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Qiao, Yiming Jia, Zidi Li, Shixiang Ren, Lei Beihang University Beijing China Beihang University Zhongguancun Laboratory State Key Laboratory of Intelligent Manufacturing System Technology Beijing China

ISBN: (纸本)9781665464543

Edge computing exploits node devices situated in close proximity to terminals to deliver distributed computing services directly to users, with FPGAs serving as the predominant platform. With the amplification in volume and intricacy of deep learning models, effectively deploying these models on FPGA devices introduces substantial challenges. To counter this predicament, this paper introduces an FPGA-based hardware acceleration framework for reconfigurable edge devices tailored for Convolutional Neural Networks (CNNs). Initially, a method for dynamically quantizing network models is devised to markedly curtail model memory consumption. Subsequently, a meticulously engineered hardware acceleration unit is formulated to attain augmented computing parallelism via meticulous temporal redesign. Finally, model deployment on FPGA devices for inference verification is showcased utilizing fully connected and convolutional neural networks as exemplars. On the MNIST dataset, the FPGA inference unit attains remarkable accuracy and computational efficiency in comparison to CPUs and GPUs. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Reinforcement Learning-based Multi-Objective Scheduling for Distributed Heterogeneous Hybrid Flow Shops with Blocking Constraints

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Engineering 2025年第3期46卷 278-291页

作者： Xueyan Sun Weiming Shen Jiaxin Fan Birgit Vogel-Heuser Fandi Bi Chunjiang Zhang State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Institute of Automation and Information Systems Technical University of MunichGarching 85748Germany

This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision *** FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data *** proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning *** experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.

关键词： Multi-objective Markov decision process Multi-agent deep reinforcement learning Proximal policy optimization Distributed hybrid flow-shop scheduling Blocking constraints

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