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控制理论与应用（英文版） 2010年第1期8卷 12-19页

作者： P.M.BALLAL A.C.TRIVEDI V.GIORDANO J.MIRELES Jr. F.L.LEWIS automation & robotics research institute University of Texas at Arlington

In unstructured environments, dynamic resource assignment is required for effective cooperation of robot teams. In some scenarios, robots are in charge of executing multiple missions simultaneously. This creates risks of deadlock due to the presence of shared resources among various missions. The main contribution of this paper is the development of a novel approach that combines the one-step look-ahead deadlock avoidance policy with dynamic resource assignment. The dynamicresource assignment is achieved using greedy resource assignment for multi-mission robot teams in the framework of a matrix-based discrete event controller. Simulation results are presented in MATLAB to discuss in detail the proposed control strategy. The paper also discusses the toolkit developed in LabVIEW which is used to implement this control framework using a suitable example.

关键词： Discrete event systems Wireless sensor networks Deadlock avoidance Petri nets

Luminescence: a new perspective for investigating perovskite precursors

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Science China Materials 2025年第1期 312-313页

作者： Haofeng Zheng Shaocong Hou School of Electrical Engineering and automation Wuhan University Wuhan University Shenzhen research institute

In the past two decades, perovskite materials have made significant progress in optoelectronic semiconductors, emerging as formidable candidates for the next generation of commercial solar cells, light-emitting diodes (LEDs), and *** the fundamental solution chemistry and photophysics in solution-processed perovskite devices is crucial for comprehending to achieve highly efficient optoelectronic devices.

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Cy-CNN: cylinder convolution based rotation-invariant neural network for point cloud registration

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Science China(Information Sciences) 2023年第5期66卷 73-87页

作者： Hengwang ZHAO Zhidong LIANG Yuesheng HE Chunxiang WANG Ming YANG Department of automation Shanghai Jiao Tong University research institute of robotics Shanghai Jiao Tong University

Point cloud registration is a challenging problem in the condition of large initial misalignments and noises. A major problem encountered in the registration algorithms is the definition of correspondence between two point clouds. Point clouds contain rich geometric information and the same geometric structure implies the same feature even if they are in diferent poses, which motivates us to seek a rotation-invariant feature representation for calculating the correspondence. This work proposes a rotation-invariant neural network for point cloud registration. To acquire rotation-invariant features, we firstly propose a rotationinvariant point cloud representation(RIPR) at the input level. Instead of using the original coordinates,we propose to use point pair features(PPF) and the transformed coordinates in the local reference frame(LRF) to represent a point. Then, we design a new convolution operator named Cylinder-Conv which utilizes the symmetry of cylinder-shaped voxels and the hierarchical geometry information of the surface of3D shapes. By specifying the cylinder-shaped structures and directions, Cylinder-Conv can better capture the local neighborhood geometry of each point and maintain rotation-invariance. Finally, we combine RIPR and Cylinder-Conv to extract normalized rotation-invariant features to generate the correspondence and perform a diferentiable singular value decomposition(SVD) step to estimate the rigid transformation. The proposed network presents state-of-the-art performance on point cloud registration. Experiments show that our method is robust to initial misalignments and noises.

关键词： deep learning point cloud registration rotation invariant computer vision

Gait Analysis of Quadruped Robot Using the Equivalent Mechanism Concept Based on Metamorphosis

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Chinese Journal of Mechanical Engineering 2019年第1期32卷 31-41页

作者： Kun Xu Peijin Zi Xilun Ding robotics institute School of Mechanical Engineering & Automation Beihang University

The previous research regarding the gait planning of quadruped robot focuses on the sequence for lifting o and placing the feet, but neglects the influence of body height. However, body height a ects gait performance significantly, such as in terms of the stride length and stability margin. We herein study the performance of a quadruped robot using the equivalent mechanism concept based on metamorphosis. Assuming the constraints between standing feet and the ground with hinges, the ground, standing legs and robot body are considered as a parallel mechanism, and each swing leg is regarded as a typical serial manipulator. The equivalent mechanism varies while the robot moves on the ground. One gait cycle is divided into several periods, including step forward stages and switching stages. There exists a specific equivalent mechanism corresponding to each gait period. The robot's locomotion can be regarded as the motion of these series of equivalent mechanisms. The kinematics model and simplified model of the equivalent mechanism is established. A new definition of the multilegged robot stability margin, based on friction coe cient, is presented to evaluate the robot stability. The stable workspaces of the equivalent mechanism in the step forward stage of trotting gait under di erent friction coe cients are analyzed. The stride length of the robots is presented by analyzing the relationship between the stable workspaces of the equivalent mechanisms of two adjacent step forward stages in one gait cycle. The simulation results show that the stride length is larger with increasing friction coe cient. We herein propose a new method based on metamorphosis, and an equivalent mechanism to analyze the stability margin and stable workspace of the multilegged robot.

关键词： Quadruped robot Gait transference Metamorphosis Metamorphic mechanism Stability Stride length

Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical Engineering and automation Luoyang Institute of Science and Technology School of Mechanical Engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li institute of Biomechanics and Medical Engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University institute of Nuclear and New Energy Technology Tsinghua University Shanghai Aerospace Control Technology institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology College of automation Engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

Adaptive dynamic programming for online solution of a zero-sum differential game

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控制理论与应用（英文版） 2011年第3期9卷 353-360页

作者： Draguna VRABIE Frank LEWIS United Technologies research Center East Hartford automation and robotics research institute University of Texas at Arlington

This paper will present an approximate/adaptive dynamic programming(ADP) algorithm,that uses the idea of integral reinforcement learning(IRL),to determine online the Nash equilibrium solution for the two-player zerosum differential game with linear dynamics and infinite horizon quadratic *** algorithm is built around an iterative method that has been developed in the control engineering community for solving the continuous-time game algebraic Riccati equation(CT-GARE),which underlies the game *** here show how the ADP techniques will enhance the capabilities of the offline method allowing an online solution without the requirement of complete knowledge of the system *** feasibility of the ADP scheme is demonstrated in simulation for a power system control *** adaptation goal is the best control policy that will face in an optimal manner the highest load disturbance.

关键词： Approximate/Adaptive dynamic programming Game algebraic Riccati equation Zero-sum differential game Nash equilibrium

Indoor Positioning System Using Axis Alignment and Complementary IMUs for Robot Localization

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机器人 2017年第3期39卷 316-323页

作者： ZHOU Qinqin LEI Siyu YU Zhangguo LIN Hsien-I CHEN Xuechao ZHANG Weimin Intelligent robotics institute Beijing Institute of Technology Graduate institute of automation Technology Taipei University of Technology

An indoor positioning method for robots is presented to improve the precision of displacement measurement using only low-cost inertial measurement units(IMUs).Firstly,a high-fidelity displacement estimation for linear motion is proposed.A new robot motion model is designed as well as an axis alignment that only uses a single axis of the *** integral error of velocity is eliminated by a new subsection calculation *** complementary IMUs are combined by assigning them different weights to obtain high accuracy displacement ***,an orientation estimation based on a fusion filter for the steering motion is *** show that the proposed method significantly improves the accuracy of linear motion measurement and is effective for the indoor positioning of a robot.

关键词： axis alignment displacement estimation indoor positioning orientation estimation inertial measurement unit

Evaluation Method on Steering for the Shape-shifting Robot in Different Configurations

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Chinese Journal of Mechanical Engineering 2016年第1期29卷 21-32页

作者： CHANG Jian LI Bin WANG Chong ZHENG Huaibing LI Zhiqiang State Key Laboratory of robotics Shenyang Institute of AutomationChinese Academy of Sciences

The evaluation method on steering is based on qualitative manner in existence, which causes the result inaccurate and fuzziness. It reduces the efficiency of process execution. So the method by quantitative manner for the shape-shifting robot in different configurations is proposed. Comparing to traditional evaluation method, the most important aspects which can influence the steering abilities of the robot in different configurations are researched in detail, including the energy, angular velocity, time and space. In order to improve the robustness of system, the ideal and slippage conditions are all considered by mathematical model. Comparing to the traditional weighting confirming method, the extent of robot steering method is proposed by the combination of subjective and objective weighting method. The subjective weighting method can show more preferences of the experts and is based on five-grade scale. The objective weighting method is based on information entropy to determine the factors. By the sensors fixed on the robot, the contract force between track grouser and ground, the intrinsic motion characteristics of robot are obtained and the experiment is done to prove the algorithm which is proposed as the robot in different common configurations. Through the method proposed in the article, fuzziness and inaccurate of the evaluation method has been solved, so the operators can choose the most suitable configuration of the robot to fulfil the different tasks more quickly and simply.

关键词： shape-shifting robot steering mobility quantitative manner

Application of Optimal-Jerk Trajectory Planning in Gait-balance Training Robot

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Chinese Journal of Mechanical Engineering 2022年第1期35卷 50-61页

作者： Fu Yuan Diansheng Chen Chenghang Pan Jun Du Xiaodong Wei Min Wang robotics institute Mechanical Engineering and AutomationBeihang UniversityBeijing 100191China

To accommodate the gait and balance disorder of the elderly with age progression and the occurrence of various senile diseases,this paper proposes a novel gait balance training robot(G-Balance)based on a six degree-of-freedom parallel *** the platform movement and IMU wearable sensors,two training modes,i.e.,active and passive,are developed to achieve vestibular *** reality technology is applied to achieve visual *** the active training mode,the elderly actively exercises to control the posture change of the platform and the switching of the virtual *** the passive training mode,the platform movement is combined with the virtual scene to simulate bumpy environments,such as earthquakes,to enhance the human anti-interference *** achieve a smooth switching of the scene,continuous speed and acceleration of the platform motion are required in some scenarios,in which a trajectory planning algorithm is *** paper describes the application of the trajectory planning algorithm in the balance training mode and the optimization of jerk(differential of acceleration)based on cubic spline planning,which can reduce impact on the joint and enhance stability.

关键词： Gait and balance training robot Balance training mode Optimal trajectory planning