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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

共 5311 条记录，以下是431-440 订阅

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A Real-Time Waste Segregation Using Machine vision for Recycling Facilities 7

A Real-Time Waste Segregation Using Machine Vision for Recyc...

引用

7th IEEE international Electrical and Energy conference, CIEEC 2024

作者： Qaisar, Muhammad Waqas Fang, Jingyang Masood, Umar School of Control Science and Engineering Shandong University Jinan China School of Electrical Engineering Namal University Mianwali Pakistan

ISBN: (纸本)9798350359558

Recycling facilities are often challenged by the demanding task of waste segregation, a process traditionally requiring substantial time and manpower, and lacking efficient automated solutions. Addressing this, our study explores the use of convolutional neural networks (CNNs), particularly the ResNet-18 model, known for its balance of accuracy and computational efficiency compared to its deeper counterparts like ResNet-50 or ResNet-101. ResNet-18 provides sufficient complexity for precise image classification while enabling faster processing speeds essential for real-time applications. Our method employs this CNN to effectively categorize waste into five distinct types, streamlining the sorting process. In this paper, we introduce a custom-designed hardware system, incorporating motor control for the physical segregation of waste. this system operates in tandem with the ResNet-18's classification results, ensuring that each type of waste is accurately sorted into designated bins. Our integrated approach, which pairs the image classification capabilities of ResNet-18 with a practical hardware automation system, aims to improve the efficiency and accuracy of waste segregation in recycling facilities. © 2024 IEEE.

关键词： Convolution

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Path Following control for Underactuated Marine Robots Based on Direct Dynamic Programming of Desired Yaw Angle

Path Following Control for Underactuated Marine Robots Based...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Jiang Long Chao Yang Bo Geng Xing Liu Training Center Institute of Materials China Academy of Engineering Physics Mianyang China College of Mechanical and Electrical Engineering Harbin Engineering University China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

For the problem of straight-line cruise, a control method for the path following of marine robots based on direct dynamic programming of the desired yaw angle is proposed in this paper for underactuated marine robots. Different from the typical path following control method, i.e., the line of sight algorithm (LOS), the desired yaw angle of marine robots is dynamically programmed directly according to the lateral error on the desired path in the Serret-Frenet (SF) frame, Hence, the lateral error of the robot is guaranteed to converge to zero. Meanwhile, the longitudinal error of the robot is reduced by timely adjusting the virtual velocity on the tangent line of the desired path point. Furthermore, the control law is obtained based on the programmed desired yaw angle and prescribed longitudinal velocity, together with RBF neural network estimation. Finally, the control scheme proposed in this paper is used on a marine robot for simulation verification by comparing with an extent improved LOS path following control method. the simulation results demonstrate that the longitudinal and lateral following accuracy and the smoothness of the control signals have better performance when applying the proposed control scheme.

关键词： Accuracy Simulation Heuristic algorithms Design methodology Neural networks Estimation Programming Dynamic programming Marine robots Convergence

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Linear Encryption Techniques for Counteracting Information-based Stealthy Attacks

Linear Encryption Techniques for Counteracting Information-b...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Jun Shang Hanwen Zhang Weixiong Rao Yiguang Hong Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Sci-ence and Technology Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

this study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. this research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. the efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

关键词： Technological innovation automation Wireless networks Encryption Numerical models Kalman filters State estimation Robots Intelligent sensors

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Learning-based Optimal control of Constrained Switched Linear Systems using Neural Networks 18

Learning-based Optimal Control of Constrained Switched Linea...

引用

18th international conference on Informatics in control, automation and robotics (ICINCO)

作者： Markolf, Lukas Stursberg, Olaf Univ Kassel Dept Elect Engn & Comp Sci Control & Syst Theory Wilhelmshoher Allee 73 D-34121 Kassel Germany

ISBN: (纸本)9789897585227

this work considers (deep) artificial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. the proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.

关键词： Neural Networks Intelligent control Hybrid Systems Approximate Dynamic Programming

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Time-Varying Multi-Goal Path Planning with Multi-Tree RRT* Algorithm for Quadruped Robots

Time-Varying Multi-Goal Path Planning with Multi-Tree RRT* A...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Haidong Wang Xiaogang Xiong Haixiang Zhou Ke Li Yunjiang Lou School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China Beijing Research Institute of Precise Mechatronics and Controls Beijing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Quadruped robots are being widely deployed in various scenarios with uneven terrains, such as rescue and supervision, due to their ability to climb obstacles and carry heavy loads. However, these robots struggle when faced with complex tasks that require reaching time-varying multiple target locations or landmarks. the visiting order of the landmarks and the total travel cost can significantly impact their overall work efficiency. the situation is worsened by limited on-board computing resources, restricted battery storage, and real-time computing demand for navigation systems. To address this issue, we propose a novel approach of multi-goal path planning specifically designed for quadruped robots. Our system extends the conventional Rapidly-Exploring Random Tree (RRT) algorithm to optimize the visiting order of multi-goal while taking into account the kinematics and safety-distance of quadruped robots. Simulation and experimental results demonstrate that our proposed multi-goal path planning system is more efficient than traditional methods in navigating complex tasks while reaching each sub-target position.

关键词： Costs automation Navigation Virtual links Merging Kinematics Path planning Real-time systems Batteries Quadrupedal robots

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SMA: A Suitable-Matching Area Selection Dataset 7

SMA: A Suitable-Matching Area Selection Dataset

引用

7th international conference on Advanced Algorithms and control Engineering, ICAACE 2024

作者： Jiang, Supeng Liu, Yunpeng Zou, Depeng Qin, Hengjia Luo, Haibo Key Laboratory Of Opto-Electronic Information Processing Chinese Academy Of Sciences Shenyang110016 China 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 The Third Military Representative Office Of The Air Force Equipment Department In Shenyang Shenyang110016 China

ISBN: (纸本)9798350361445

Scene matching is a commonly used guidance technology in the final guidance stage of missiles. Suitable-matching area selection is the core technology of reference map preparation and one of the critical factors affecting the performance of the scene-matching system. Since there is no public dataset for scene-matching suitable area selection, it is difficult to compare related selection algorithms uniformly. In this paper, we analyze the scene categories that may be encountered in the scene-matching process and create a dataset SMA, which contains six scene types and various scene contents. this dataset can be used for matching performance analysis and evaluation of scene areas. We analyze the information content, stability, uniqueness, and significance of the images and conduct matching experiments by simulating real-time images to prove the matching performance of the calibration area. © 2024 IEEE.

关键词： Navigation

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Assessing the Stability of Linear Systems with Random Delays

Assessing the Stability of Linear Systems with Random Delays

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Jianqi Chen Junfeng Wu Qi Mao Jie Chen Center for Advanced Control and Smart Operations Nanjing University Suzhou China School of Data Science The Chinese University of Hong Kong Shenzhen China Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing Normal University Nanjing China Department of Electrical Engineering City University of Hong Kong Hong Kong China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

this paper explores the stability problems of discrete-time linear time-invariant (LTI) systems subject to random time delays. We first develop a general mean-square small-gain stability condition for feedback systems containing structured stochastic multiplicative uncertainties. Subsequently, we apply this mean-square small-gain condition to certain LTI random delay systems, where delays exhibit random delay lengths. Necessary and sufficient mean-square stability criteria are derived. these criteria, applicable to systems with either single or multiple sources of random delays, typically involve computing the spectral radius of a constant matrix. this computation allows us to determine whether a system's state variance matrix converges asymptotically despite the presence of random delays.

关键词： Linear systems Asymptotic stability Uncertainty Delay systems Delay effects Stability criteria Stochastic processes Delays Numerical stability Time-varying systems

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Learning-based Nonlinear Model Predictive control Using Deterministic Actor-Critic with Gradient Q-learning Critic

Learning-based Nonlinear Model Predictive Control Using Dete...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Amine Salaje thomas Chevet Nicolas Langlois ESIGELEC IRSEEM Universite de Rouen Normandie Rouen France

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In this paper, we present an off-policy reinforcement learning (RL) method used to tune the optimal weights of a nonlinear model predictive control (NMPC) scheme. the objective is to find the optimal policy minimizing the closed-loop performance of point stabilization with obstacle avoidance control task. the parameterized NMPC scheme serves to approximate the optimal policy and update the parameters via compatible off-policy deterministic actor-critic with gradient Q-learning critic (COPDAC-GQ). While efficient, this algorithm requires a heavy computational complexity when combined with NMPC, as two optimal control problems have to be solved at each time instant. We therefore propose two different methods to reduce the real-time computational cost of the algorithm. First, a neural network is used to learn the subsequent state-action features of the advantage function. then, we propose to use the information delivered by the NMPC scheme to approximate the subsequent state-action features in the critic. Whichever method is used removes the need of a secondary NMPC, significantly improving the training speed. the results show that there is no difference between the original method and the proposed methods in terms of the learned policy and the control performance, whereas the real-time computational burden is almost halved with the proposed methods.

关键词： Training Q-learning Neural networks Optimal control Approximation algorithms Real-time systems Mobile robots Computational complexity Collision avoidance Predictive control

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Real-time Robust Trajectory control for Vehicle Platoons: A Linear Matrix Inequality-based Approach 18

Real-time Robust Trajectory Control for Vehicle Platoons: A ...

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18th international conference on Informatics in control, automation and robotics (ICINCO)

作者： Bozzi, Alessandro Zero, Enrico Sacile, Roberto Bersani, Chiara Univ Genoa DIBRIS Dept Informat Bioengn Robot & Syst Engn Genoa Italy

ISBN: (纸本)9789897585227

this paper proposes a solution to dynamically adjust vehicle platoon trajectories. the goal of the control algorithm is to keep the optimal interdistance between adjacent vehicles proceeding at cruising speed on a straight road. After a proposal of the interdistance required between neighboring vehicles, a robust decentralized controller based on a linear control law provides the speed profile for each component of the platoon. Its objective is to minimize the divergence in space in respect to the planned trajectories while assuring a safe span between adjacent members of the platoon. the results on a limited instance demonstrate the effectiveness of the proposed approach.

关键词： Robust control Trajectory Planning Linear Matrix Inequality Vehicle Platooning

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MDSTC: A Dynamic Approach to Multi-Robot Coverage Path Planning

MDSTC: A Dynamic Approach to Multi-Robot Coverage Path Plann...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Weimin Mo Zhiyun Lin School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Maximizing the efficiency of multi-robot systems is one of the primary objectives in solving the multi-robot coverage path planning (mCPP) problem. During coverage tasks, unexpected imbalances in the multi-robot systems, such as changes in speed, can lead to suboptimal utilization of the system's capabilities, subsequently reducing the efficiency of task execution. In this paper, we developed a multi-robot dynamic spanning tree coverage (MDSTC) algorithm, an online area-division-based approach that adjusts region allocation among robots based on their coverage status through an exchange-based mechanism to enhance multi-robot system efficiency. To validate the effectiveness of the proposed algorithm, we conducted extensive numerical simulations. the results demonstrate that our algorithm achieves a superior solution in scenarios where robot efficiency remains balanced, closely matching the performance of state-of-the-art mCPP methods. Furthermore, when differences in efficiency arise among the robots during task execution, the proposed algorithm provides a dynamic solution that enhances overall coverage efficiency.

关键词： automation Heuristic algorithms Numerical simulation Dynamic scheduling Path planning Multi-robot systems Resource management Robots

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