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IEEE Latin American Robotics Symposium, LARS

作者： Igor Pardal Latini Wilson Eduardo Barioni Marco Teixeira Flavio Neves-Jr Lucia Valeria Ramos de Arruda Universidade Tecnológica Federal do Paraná (UTFPR) Curitiba Parana Brazil Automation and Advanced Control System Laboratory (LASCA) Graduate Program in Electrical and Computer Engineering (CPGEI)

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

In Industry 4.0, there is a constant pursuit for efficiency and productivity, and, searching to achieve this, the usage of multiple robots to attend to several tasks simultaneously is usual. Nowadays, there is plenty of robotic architectures, having their navigation system based on numerous different sensors. This work compares the line-follower robots, having a determined path, with the free-moving robots that navigate based on odometry, for load collecting and delivery tasks in specific locations. For this, four simulated experiments are run in two scenes, with two and four tasks, respectively. The comparison is based on a difference in robot quantity, with more line-followers than free-moving ones, aiming to collect values such as idle time, task wait time, and the number of tasks done for each machine. The results show advantages in using less but more intelligent robots with free movement. Such robots are not bound to one unique task and can use their idle time to roam and complete other processes. However, the task wait time is a concerning issue, as too few robots for overcharged situations can increase execution delays.

关键词： Service robots Navigation Robot sensing systems Time measurement Sensor systems Sensors Delays

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Comparative analysis of bioinspired algorithms for tuning a flatness-based controller: A ball and beam case study

Comparative analysis of bioinspired algorithms for tuning a ...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： M. A. Pastrana José Oniram de A. Limaverde Filho Luiz Henrique Oliveira Jaider Bautista Eugênio Fortaleza Daniel M. Muñoz Department of Mechanical Engineering Automation and Control Group (GRACO) University of Brasília Brasília Brazil Department of Electrical Engineering University of Brasília Brasília Brazil Faculty of Engineering Systems Undergraduate Program University of the Amazonia Florencia Colombia

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

This paper provides a comprehensive comparative analysis of the performance of four bioinspired algorithms (Differential Evolution, Grasshopper Optimization Algorithm, Moth-flame Optimization, and Particle Swarm Optimization) in designing a flatness-based controller for a ball and beam system simulated in MATLAB/Simulink environment. The assessment involves the tuning of feedback gains utilizing four distinct fitness functions: Integral Square Error, Integral Absolute Error, Integral Time Absolute Error, and an ad-hoc cost function. Hypothesis tests are employed to choose the optimal controllers from each combination for every bioinspired algorithm and a fitness function. Subsequently, a control performance analysis of these controllers for the ball and beam system is conducted, focusing on critical parameters such as overshoot, settling time, and steady-state error. This analysis demonstrates that all the controllers achieve a zero steady-state error. Noteworthy is the flatness-based controller tuned with the bioinspired Grasshopper Optimization Algorithm and utilizing the Integral Time Absolute Error fitness function, which stands out for achieving the fastest response with the lowest overshoot. This particular controller emerges as the most recommended choice for the specified application.

关键词： Particle beams Focusing Cost function MIMO Steady-state Performance analysis Particle swarm optimization MATLAB Tuning Computational intelligence

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A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

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Science China(Information Sciences) 2020年第12期63卷 266-268页

作者： Jing-Dong DIAO Jin GUO Changyin SUN School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education

Dear editor,With the rapid development of network and communication technology, group systems interrelated in terms of both time and space are commonly encountered, such as sensor networks, multi-agent systems, and sm... 详细信息

关键词： system identification event-triggered binary-valued observations packet loss convergence

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Tracking Performance of Networked systems with Limited Bandwidth and Colored Noise

Tracking Performance of Networked Systems with Limited Bandw...

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2022 Chinese automation Congress, CAC 2022

作者： Zhang, Bin Jiang, Xiaowei Sun, Li Zhang, Xianhe School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan430074 China School of Electrical Engineering and Automation Hubei Normal University Huangshi435002 China

ISBN: (纸本)9781665465335

This study investigated the optimal tracking performance (OTP) of multi-input multi-output (MIMO), discrete- time networked control systems (NCSs). The limits of tracking performance (TP) under the influences of bandwidth, encoding- decoding, and additive coloured Gaussian noise (ACGN) are derived using the techniques of coprime decomposition and all-pass decomposition. The results reveal the negative impact of non-minimum phase (NMP) zeros and unstable poles of the plant as well as network communication constraints on the TP of NCSs. Finally, a numerical simulation is discussed and verifies our conclusions. © 2022 IEEE.

关键词： Networked control systems

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing control of Nutrient Levels, Climate Conditions, and Plant Health

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Albert Good Fried Panggabean Rahmat Romadhon Ramot Lubis Endra Joelianto Doctoral Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Faculty of Informatics and Electrical Engineering Institut Teknologi Del Toba Indonesia Engineering Physics Study Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health.

关键词： Smart agriculture Precision agriculture Reinforcement learning Humidity Aerospace electronics Nutrients Temperature control Informatics Monitoring Farming

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LiDAR Point Cloud Map Coding Framework Based on Quadric Surface Fitting and Scene Construction

LiDAR Point Cloud Map Coding Framework Based on Quadric Surf...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Xuebin Sun Jinxi Wang Xiaofei Yang School of Electrical Automation and Information Engineering Tianjin University Tianjin China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Department of Aerospace Laser Technology and System Shanghai Institute of Optics and Fine Mechanics Shanghai China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Due to rapid 3D acquisition advancements, LiDAR point clouds are prevalent in emerging applications like heritage preservation, mobile robotics, and remote sensing. Yet, the sheer data volume poses challenges for transmission and storage, especially in autonomous driving. This paper introduces an efficient coding framework for compressing large dense LiDAR point cloud maps. By recording timestamps and quaternion matrices during map construction, we reduce map compression to sequence compression. The scans are segregated into intra-frames and inter-frames. For intra-frames, a quadric surface fitting intra-prediction technique eliminates spatial redundancy. For inter-frames, by constructing the 3D scene using encoded intra-frames, the inter-frame can be simulated according to its posture and LiDAR intrinsic parameters. Coding focuses on the difference between actual and predicted point cloud data. Decoding reverses this process, reconstructing the point cloud map from the decoded sequence and quaternion matrices. KITTI dataset experiments show the point cloud map can be averagely compressed to 3.43% of its original size while maintaining centimeter-level precision. This outperforms both octree and Google Draco methods.

关键词： Point cloud compression Surface reconstruction Laser radar Three-dimensional displays Quaternions Fitting Robot sensing systems Surface fitting Encoding Surface treatment

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Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse Optimal control 13

Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse O...

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13th Asian control Conference, ASCC 2022

作者： Qiu, Tianyu Zhang, Han Wang, Jingchuan Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9788993215236

In this paper, a traditional one v.s. one pursuit and evasion scenario is considered. The evader aims to reach her target and avoid being captured by the pursuer, while the pursuer, without the knowledge of the evader's cost function, intends to capture the evader before she successfully escapes. We formulate this problem as a Model Predictive control Decoupled Pursuit and Evasion Game (MPC-DPEG) model with incomplete information. We apply Inverse Optimal control (IOC) technique to estimate the evader's cost function and give a Nash pursuit strategy for MPC-DPEG as the optimal strategy for the pursuer. Numerical examples reveal the improvement of pursuit performance of the Nash pursuit strategy based on the estimated cost function over naive pursuit strategies. © 2022 ACA.

关键词： Simulation Optimal control Games Predictive models Cost function Numerical models Predictive control

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Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Nailin Wang, Chongdi Yuan, Shenghai Wang, Jingchuan Wang, Danwei Chen, Weidong The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: (a) inaccurate depth input. Accurate depth input is impossible to get in real-world large-scale scenes. (b) inaccurate pose estimation. Existing methods are not robust enough with the growth of cumulative errors in large scenes and long sequence. (c) insufficient scene representation capability. A single global radiance field lacks the capacity to effectively scale to large-scale scenes. To this end, we propose an incremental joint learning framework, which can achieve accurate depth, pose estimation, and large-scale scene reconstruction. A vision transformer-based network is adopted as the backbone to enhance performance in scale information estimation. For pose estimation, a feature-metric bundle adjustment (FBA) method is designed for accurate and robust camera tracking in large-scale scenes. In terms of implicit scene representation, we propose an incremental scene representation method to construct the entire large-scale scene as multiple local radiance fields to enhance the scalability of 3D scene representation. Extended experiments have been conducted to demonstrate the effectiveness and accuracy of our method in depth estimation, pose estimation, and large-scale scene reconstruction. The code will be open-sourced upon paper acceptance. © 2024, CC BY-NC-SA.

关键词： Depth perception

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A Cloud-Edge-Based Multi-Label Learning and Fast Clustering Method for Emerging New PV Faults

A Cloud-Edge-Based Multi-Label Learning and Fast Clustering ...

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IEEE International Conference on Cyber Technology in automation, control, and Intelligent systems

作者： Qi Liu Bo Yang Yuxiang Liu Kai Ma Xinping Guan Department of Automation Ministry of Education Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Electrical Engineering Yanshan University Qinhuangdao China

Aiming at accurate identification of emerging new faults in PV systems, a novel PV fault diagnosis framework based on cloud-edge collaboration is designed. Model updates are deployed on the cloud servers, while real-time diagnosis is implemented at the edge devices. A multi-label learning model is developed to extract fault features efficiently, and a threshold scheme is further designed for feature enhancement. The obtained features are processed by K-Means clustering and the One-Class Support Vector Machine (OCSVM) methods, which can accurately diagnose existing types and identify unknown faults. For analysis and demonstration, the Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) methods are used for dimensionality reduction and visualization. Numerical results validate the effectiveness of the proposed method.

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NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Yanbo Xie, Hongle Wang, Hesheng Wang, Jingchuan Wang, Danwei Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis. © 2024, CC BY-NC-SA.

关键词： Geometry

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