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Obstacle-Avoidance Motion Planning Method of Manipulator Based on Deep Reinforcement Learning

Unmanned systems 2025年

作者： Liu, Yiyang Wu, Defan Fu, Yukai Bai, Hongfei Deng, Chao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China

In recent years, driven by the rapid advancement of industrial automation and intelligent manufacturing, the demand for manipulators to perform tasks in complex environments has significantly increased. Traditional motion planning methods face challenges of computational complexity and lack of adaptability when handling robots with redundant degrees of freedom. To address these issues, this paper proposes a novel obstacle avoidance motion planning method for manipulators based on deep reinforcement learning (DRL), which is especially important when unmanned systems operate in a GPS-free environment. The primary contributions are twofold: (i) we propose an innovative algorithm, termed PPO-HER, which incorporates hindsight experience replay (HER) into proximal policy optimization (PPO) during training, effectively leveraging failed experiences to enhance policy learning efficiency;(ii) an adaptive action exploration strategy is designed to accelerate training convergence and avoid local optima. Experimental results demonstrate that the PPO-HER algorithm and the adaptive action exploration strategy perform exceptionally well across three environments of varying difficulty, significantly improving network training speed and success rates, shortening planned paths, and enhancing autonomous obstacle avoidance capabilities. These findings provide new ideas for target location and tracking of unmanned systems, and lay the foundation for future learning and data-driven approaches in complex environments. © World Scientific Publishing Company.

关键词： Deep reinforcement learning

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Cooperative decision-making algorithm with efficient convergence for UCAV formation in beyond-visual-range air combat based on multi-agent reinforcement learning

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Chinese Journal of Aeronautics 2024年第8期37卷 311-328页

作者： Yaoming ZHOU Fan YANG Chaoyue ZHANG Shida LI Yongchao WANG School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air *** Multi-Agent Reinforcement Learning(MARL)shows outstanding performance in cooperative decision-making,it is challenging for existing MARL algorithms to quickly converge to an optimal strategy for UCAV formation in BVR air combat where confrontation is complicated and reward is extremely sparse and *** to solve this problem,this paper proposes an Advantage Highlight Multi-Agent Proximal Policy Optimization(AHMAPPO)***,at every step,the AHMAPPO records the degree to which the best formation exceeds the average of formations in parallel environments and carries out additional advantage sampling according to ***,the sampling result is introduced into the updating process of the actor network to improve its optimization ***,the simulation results reveal that compared with some state-of-the-art MARL algorithms,the AHMAPPO can obtain a more excellent strategy utilizing fewer sample episodes in the UCAV formation BVR air combat simulation environment built in this paper,which can reflect the critical features of BVR air *** AHMAPPO can significantly increase the convergence efficiency of the strategy for UCAV formation in BVR air combat,with a maximum increase of 81.5%relative to other algorithms.

关键词： Unmanned combat aerial vehicle(UCAV)formation Decision-making Beyond-visual-range(BVR)air combat Advantage highlight Multi-agent reinforcement learning(MARL)

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Metabolic mechanism in biosynthesis of polyhydroxyalkanoate from terephthalic acid by mixed microbial consortium

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Chemical Engineering Journal 2025年 515卷

作者： Pan, Lanjia Khan, Wardah Hayat Li, Jie Kubota, Kengo Wang, Qiang Wang, Yin State Key Laboratory of Regional and Urban Ecology Institute of Urban Environment Chinese Academy of Sciences Xiamen361021 China State Key Laboratory of Advanced Environmental Technology Institute of Urban Environment Chinese Academy of Sciences Xiamen361021 China University of Chinese Academy of Sciences China Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control CAS Haixi Industrial Technology Innovation Center in Beilun Ningbo315830 China Department of Frontier Sciences for Advanced Environment of Environmental Studies Tohoku University Sendai980-8579 Japan Chengdu BOE Display Technology Co. Ltd Chengdu610031 China

Biosynthesis of polyhydroxyalkanoates (PHA) from waste plastics offers a sustainable solution to mitigating plastic pollution for circular economy. Terephthalic acid (TPA), derived from polyethylene terephthalate (PET) hydrolysis, shows potential as a carbon source for PHA production. However, the isolation of microbial strains with high efficiency in converting TPA to PHA remain a significant challenge. In this study, a microbial consortium enriched from sludge during the flask experiments was employed for fed-batch fermentation in a 5 L bioreactor to facilitate the conversion of TPA to PHA. The fermentation process achieved a maximum PHA concentration of 2.25 g/L with a yield of 0.10 gPHA/gTPA. The dominant microorganisms and TPA transformation pathways were analyzed by genome annotation and intermediate metabolites identification. The consortium exhibited synergistic interactions, with member species producing specific enzymes that sequentially transformed TPA into protocatechuic acid or catechol, ultimately yielding acetyl-CoA as the PHA biosynthesis precursor. This study establishes a sustainable approach for upcycling waste PET plastics into biodegradable polymers. The revealed metabolic mechanisms provide fundamental insights for future improvement of PHA production. © 2025 Elsevier B.V.

关键词： Acetobacter

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Differentially Private Gradient-Tracking-Based Distributed Stochastic Optimization over Directed Graphs

arXiv

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arXiv 2025年

作者： Chen, Jialong Wang, Jimin Zhang, Ji-Feng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China School of Automation and Electrical Engineering Zhongyuan University of Technology Zheng Zhou450007 China

This paper proposes a new differentially private gradient-tracking-based distributed stochastic optimization algorithm over directed graphs. Specifically, privacy noises are added to each agent’s state and tracking variable to prevent information leakage, and then perturbed states and tracking variables are transmitted to neighbors. We design two novel schemes of the iteration step-sizes and the sampling number for the algorithm. By using the sampling parameter-controlled subsampling method, both schemes enhance the differential privacy level, and achieve the finite cumulative privacy budget even over infinite iterations. The convergence rate of the algorithm is shown for both nonconvex with the Polyak-Lojasiewicz condition and strongly convex objectives: Scheme (S1) achieves the polynomial convergence rate, and Scheme (S2) achieves the exponential convergence rate. The trade-off between the privacy and the convergence rate is presented. The algorithm’s effectiveness and superior performance over the existing works are demonstrated through numerical examples of distributed training on benchmark datasets"MNIST" and"CIFAR-10". Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Detection of Penicillin via Surface Plasmon Resonance Biosensor

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Chemical Research in Chinese Universities 2012年第5期28卷 792-796页

作者： ZHANG Ying JIN Wei YANG Meng-chao ZHANG Ti-qiang ZHOU Chao XIE Fei SONG Qi REN Hao JIN Qin-han MU Ying Research Center for Analytical Instrumentation Institute of Cyber-systems and ControlState Key Laboratory of Industrial Control TechnologyDepartment of Control Science and TechnologyZhejiang UniversityHangzhou 310058PR.China

A method of using Au colloid to capture the decomposed product of penicillin, penicillamine, on a surface plasmon resonance（SPR） biosensor for the quantitative determination of penicillin was developed. Based on the de-composition of penicillin to generate penicillamine and penilloaldehyde, a high sensitive biosensor for detecting penicillin was also developed. In our experiment, it was penicillamine rather than penicillin that has been measured. This is because penicillamine contains a functional group that makes it self-assembling on Au colloid to increase the molecular weight so as to improve the surface plasmon resonance signal. On a UV-Vis spectrophotometer, a high concentration of penicilliamine-Au complex was determined, indicating that penicillamine was already well combined with Au colloid. The method, using the combination of Au colloid with penicillamine, proved to detect penicillin.

关键词： Surface plasmon resonance（SPR） Biosensor Penicillamine Au colloid

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： cyber attacks

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Identification of abnormal operating conditions and intelligent decision system

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机械工程前沿 2011年第4期6卷 456-462页

作者： Xiuliang LI Junjie JIANG Hongye SU Jian CHU National Key Laboratory of Industrial Control Technology Institute of Cyber-systems and Control Zhejiang University Hangzhou 310027 China

In earth pressure balance (EPB) shield construction,the “plastic flow state” is difficult to form using the soil dug in the capsule because it can cause three abnormal operating conditions,including occlusion,caking in the capsule,and spewing at the outlet of the dump *** abnormal operating conditions can,in turn,trigger failure in tunneling,cutter-device damage,and even catastrophic incidents,such as ground *** present paper effectively integrates the mechanism of abnormal operating conditions and knowledge of soil conditioning,and establishes a uniform model of identifying abnormal conditions and intelligent decision support system based on the belief rule-base *** model maximizes knowledge in improving the soil,construction experience,and data to optimize the model ***,a numerical simulation with specific construction data is presented to illustrate the effectiveness of the algorithm.

关键词： earth pressure balance (EPB) shield machine soil pressure cabin abnormal operating condition belief rule-base system

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A proposal on centralised and distributed optimisation via proportional–integral–derivative controllers(PID)control perspective

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IET cyber-systems and Robotics 2023年第4期5卷 40-48页

作者： Jiaxu Liu Song Chen Shengze Cai Chao Xu School of Mathematical Sciences Zhejiang UniversityHangzhouChina The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouChina

Motivated by the excellent performance of proportional–integral–derivative controllers(PIDs)in the field of control,the authors injected the philosophy of PID into optimi-sation and introduced two types of novel PID optimisers from a continuous-time view,which benefit from the idea that discrete-time optimisation algorithm can be modelled as a continuous dynamical system/controlled *** centralised optimisation,the au-thors discuss the idea of the first-order PID optimiser and the second-order accelerated PID ***,this framework is extended into distributed optimisation settings,and a distributed PID optimiser is ***,some numerical examples are given to verify our ideas.

关键词： control deep learning deep neural network machine learning

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A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

引用

13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

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Delay-dependent stabilization of singular Markovian jump systems with state delay

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控制理论与应用（英文版） 2009年第3期7卷 231-236页

作者： Zhengguang WU Hongye SU Jian CHU National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou Zhejiang 310027 China

This paper deals with the delay-dependent stabilization problem for singular systems with Markovian jump parameters and time delays. A delay-dependent condition is established for the considered system to be regular, impulse free and stochastically stable. Based on the condition, a design algorithm of the desired state feedback controller which guarantees the resultant closed-loop system to be regular, impulse free and stochastically stable is proposed in terms of a set of strict linear matrix inequalities （LMIs）. Numerical examples show the effectiveness of the proposed methods.

关键词： Singular time-delay systems Markovian jumping parameters Delay-dependent Stochastic stability Linear matrix inequality （LMI）

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