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Characteristic model-based control of aerial manipulator for improved interaction stability

Science China(Technological Sciences) 2025年第4期68卷 166-178页

作者： Bingkai XIU Zhan LI Yipeng YANG Huijun GAO Research Institute of Intelligent Control and Systems Harbin Institute of TechnologyHarbin150001China State Key Laboratory of Robotics and Systems(HIT) Harbin Institute of TechnologyHarbin150001China

Maintaining contact stability is crucial when the aerial manipulator interacts with the surrounding environment. In this paper, a novel output feedback framework based on a characteristic model is proposed to improve the contact stability of the aerial manipulator. First, only position measurements of the aerial manipulator are introduced to design the practical finite-time command filter-based force observer. Second, an attitude control architecture including characteristic modeling and controller design is presented. In the modeling part, input-output data is utilized to build the characteristic model with fewer parameters and a simpler structure than the traditional dynamic model. Different from conventional control methods, fewer feedback values,namely only angle information, are required for designing the controller in the controller part. In addition, the convergence of force estimation and the stability of the attitude control system are proved by the Lyapunov analysis. Numerical simulation comparisons are conducted to validate the effectiveness of the attitude controller and force observer. The comparative results demonstrate that the tracking error of x and θ channels decreases at least 10.62% and 10.53% under disturbances and the force estimation precision increases at least 45.19% in the different environmental stiffness. Finally, physical flight experiments are conducted to validate the effectiveness of the proposed framework by a self-built aerial manipulator platform.

关键词： aerial manipulator interaction stability contact force observer characteristic model

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Attar: RRAM-based in-memory attention accelerator with software-hardware co-optimization

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Science China(information Sciences) 2025年第3期68卷 371-387页

作者： Bing LI Ying QI Ying WANG Yinhe HAN Information Engineering College Capital Normal University Research Center for Intelligent Computing Systems Institute of Computing TechnologyChinese Academy of Sciences

The attention mechanism has become a pivotal component in artificial intelligence, significantly enhancing the performance of deep learning applications. However, its quadratic computational complexity and intricate computations lead to substantial inefficiencies when processing long sequences. To address these challenges, we introduce Attar, a resistive random access memory(RRAM)-based in-memory accelerator designed to optimize attention mechanisms through software-hardware co-optimization. Attar leverages efficient Top-k pruning and quantization strategies to exploit the sparsity and redundancy of attention matrices, and incorporates an RRAM-based in-memory softmax engine by harnessing the versatility of the RRAM crossbar. Comprehensive evaluations demonstrate that Attar achieves a performance improvement of up to 4.88× and energy saving of 55.38% over previous computing-in-memory(CIM)-based accelerators across various models and datasets while maintaining comparable accuracy. This work underscores the potential of in-memory computing to enhance the efficiency of attention-based models without compromising their effectiveness.

关键词： RRAM computing-in-memory attention pruning quantization

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Inexact proximal gradient algorithm with random reshuffling for nonsmooth optimization

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Science China(information Sciences) 2025年第1期68卷 219-237页

作者： Xia JIANG Yanyan FANG Xianlin ZENG Jian SUN Jie CHEN National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center School of Electronic and Information Engineering Tongji University

Proximal gradient algorithms are popularly implemented to achieve convex optimization with nonsmooth regularization. Obtaining the exact solution of the proximal operator for nonsmooth regularization is challenging because errors exist in the computation of the gradient; consequently, the design and application of inexact proximal gradient algorithms have attracted considerable attention from researchers. This paper proposes computationally efficient basic and inexact proximal gradient descent algorithms with random reshuffling. The proposed stochastic algorithms take randomly reshuffled data to perform successive gradient descents and implement only one proximal operator after all data pass through. We prove the convergence results of the proposed proximal gradient algorithms under the sampling-without-replacement reshuffling *** computational errors exist in gradients and proximal operations, the proposed inexact proximal gradient algorithms can converge to an optimal solution neighborhood. Finally, we apply the proposed algorithms to compressed sensing and compare their efficiency with some popular algorithms.

关键词： proximal operator random reshuffling inexact computation compressed sensing nonsmooth optimization

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Automatic Control of Magnetic Helical Microrobots Docking with Target Objects in Liquid Environments

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Journal of Bionic Engineering 2025年第2期22卷 574-584页

作者： Fu Zhao Haoran Rong Lefeng Wang State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin150080China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin150080China

Bio-inspired magnetic helical microrobots have great potential for biomedical and micromanipulation applications. Precise interaction with objects in liquid environments is an important prerequisite and challenge for helical microrobots to perform various tasks. In this study, an automatic control method is proposed to realize the axial docking of helical microrobots with arbitrarily placed cylindrical objects in liquid environments. The docking process is divided into ascent, approach, alignment, and insertion stages. First, a 3D docking path is planned according to the positions and orientations of the microrobot and the target object. Second, a steering-based 3D path-following controller guides the helical microrobot to rise away from the container bottom and approach the target along the path. Third, based on path design with gravity compensation and steering output limits, alignment of position and orientation can be accomplished simultaneously. Finally, the helical microrobot completes the docking under the rotating magnetic field along the target orientation. Experiments verified the automatic docking of the helical microrobot with static targets, including connecting with micro-shafts and inserting into micro-tubes. The object grasping of a reconfigurable helical microrobot aided by 3D automatic docking was also demonstrated. This method enables precise docking of helical microrobots with objects, which might be used for capture and sampling, in vivo navigation control, and functional assembly of microrobots.

关键词： Magnetic actuation Helical microrobots Visual servo Motion control Docking

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Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 198-214页

作者： Fazhi Song Ning Cui Shuaiqi Chen Kai Zhang Yang Liu Xinkai Chen Jiubin Tan IEEE the Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology the State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology the Center of Ultraprecision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of UltraPrecision Intelligent Instrumentation Ministry of Industry and Information Technology the Department of Electronic and Information Systems Shibaura Institute of Technology

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency *** being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

关键词： Extended state observer learning control model uncertainties motion control stochastic noise

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Multi-Objective Path Planning for Parallel Parking Based on NSGA-II Algorithm 2

Multi-Objective Path Planning for Parallel Parking Based on ...

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2nd International Conference on Mechatronics and Automation technology, ICMAT 2023

作者： Tong, Fengshuo Zhang, Zheng Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China

ISBN: (纸本)9781643684826

In order to improve the safety, parking efficiency and smoothness of the parallel parking of driverless cars, for the parking process needs to meet the path curve curvature is small, parking distance is short, the lane occupation is less, through the analysis of the characteristics of the parallel parking working conditions, put forward a NSGA-II algorithm based on the parking starting point of multi-objective optimization method. Firstly, based on the analysis of vehicle kinematics model, the quintic polynomial curve is used to construct the parking path, and the analytical function between the parking start point and various indexes is established;then, the NSGA-II algorithm is used to optimize the constructed multi-objective model, so as to obtain the optimal optional set of the parking start point;finally, the results of the simulation experiments show that: in order to satisfy the various objectives of the synergistic optimization as well as constraints, the proposed method of the design of the parking start point can achieve the parking path curve of less curvature and shorter parking distance. © 2024 The Authors.

关键词： Multiobjective optimization

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Data-driven output regulation control for constrained linear systems

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Science China(information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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An Efficient Closed-Loop Adaptive Controller for a Small-Sized Quadruped Robotic Rat

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类生命系统（英文） 2024年第4期2024卷 223-235页

作者： Xiaolong Quan Rongjie Du Ruochao Wang Zhenshan Bing Qing Shi Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China Technical University of Munich MunichGermany

Large quadruped robots have shown potential for a wide range of everyday tasks due to their superior terrain ***,small-scale quadruped robots have limited payloads and thus cannot carry sufficient sensing and computational resources,which imposes limitations on their environmental *** address this challenge,we proposed an efficient closed-loop adaptive controller by simplified pose estimation and control strategy that utilizes only inertial measurement unit sensors,which drastically reduces the control ***,we integrated this control system into a small-scale quadruped robot,SQuRo,and conducted a series of experiments to verify the environmental adaptation performance of *** results demonstrated that SQuRo has achieved 6 kinds of robust motion:slope stabilization motion,linear tracking motion,autonomous fall recovery motion,uneven terrain walk,slope walk,and obstacle *** work paves the way for small-scale quadruped robots to autonomously perform tasks in challenging environments.

关键词： controller adaptive closed-loop efficient quadruped robotic small-sized

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Integrated Batching, Pick Sequencing and Robot Assignment for Robotic Warehouse Automation 8

Integrated Batching, Pick Sequencing and Robot Assignment fo...

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8th IEEE International Conference on information and Communication technology, CICT 2024

作者： Gupta, Rishi Kant, Surya Singh, Shubhang Kala, Rahul Nandi, Gora Chand Indian Institute of Information Technology Allahabad Center for Intelligent Robotics Allahabad India ABV-Indian Institute of Information Technology and Management Gwalior Center for Autonomous Systems Gwalior India

ISBN: (纸本)9798331531539

Future warehouses will see robots autonomously picking up all the items ordered by the user and delivering them for packaging, sorting and delivery. This requires warehouses to schedule the robots to autonomously cater to all customer orders. In this paper, we solve the problem of batching orders, where several user orders are clubbed into one to be collectively picked by one robot, scheduling the visit of the robot to pick up the desired items and assigning one robot to carry the operation. Each robot is assumed to have a small payload capacity that restricts the number of orders that can be batched. Each item is assumed to be found at several locations, and the robot can select anyone of the locations. The proposed solution uses heuristics to create initial batches that are further optimised by using Genetic Algorithm. Genetic Algorithm also jointly optimises the batching and item pick sequencing. The selection of one out of several sites is done by using a Dynamic Programming approach. The solution is post processed by using a Dynamic Programming solution for optimally scheduling the robot visits within every batch. The experimental results show that the proposed solution scales to a large number of orders because of the use of heuristics, while the results are better than the use of greedy heuristic approaches. © 2024 IEEE.

关键词： Dynamic programming

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SDSLP-YOLO: An end-to-end model for simultaneously detecting strawberry and locating picking point

SDSLP-YOLO: An end-to-end model for simultaneously detecting...

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2024 China Automation Congress, CAC 2024

作者： Du, Xueyi Huang, Wenhui Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350368604

Accurately identifying strawberries and locating picking points are prerequisites for harvesting robots. To address the issues of multiple detections, missed detections, and false detections, as well as inaccurate picking point localization caused by large differences in fruit shape, complex backgrounds, and occlusions during the picking process, this paper proposes a method based on the YOLOv8-pose model. First, a dual-path feature extraction backbone network (LGFBNet) that combines convolution and Transformer is proposed. This module enhances the richness and expressiveness of features by coupling global and local features, effectively solving the issue of multiple detections caused by large differences in fruit shape. Secondly, CBAM is introduced into the neck of the network, allowing the network to focus more on the distinguishing features between the strawberries and the background, effectively addressing the missed and false detections caused by complex backgrounds. Finally, the NWD-CIoU weighted loss function is introduced, where the scale-balancing property of NWD compensates for the sensitivity of CIoU to positional shifts, reducing the additional missed detection rate caused by severe occlusions. Experimental results show that the proposed method achieves an average precision (mAP-obj) of 88.3% for strawberry detection and an average precision (mAP-kp) of 74.2% for keypoint detection, which are 1.7% and 3% higher than YOLOv8-pose, respectively. In summary, this method has significant theoretical and practical implications for tasks such as strawberry recognition and picking point localization in complex environments. © 2024 IEEE.

关键词： key point detection picking point positioning strawberry identification yolov8-pose

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