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Visual localization on point and line combination in dynamic environments

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期136卷

作者： Wei, Yuhai Wei, Wu Wang, Dongliang Gao, Yong Liu, Xiongding South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510641 Guangdong Peoples R China Shantou Univ Sch Engn Shantou 515063 Peoples R China

Visual localization is the front-end part of visual simultaneous localization and mapping (VSLAM), and also the basis of environmental perception. Accurate visual localization can improve the map construction of complex dynamic environment, which can make robots and other carriers intelligent. To solve the visual localization problem in complex dynamic environment and overcome the localization error caused by the interference of moving targets, this paper proposes an efficient point and line feature combination method to locate the key features of static and dynamic regions. Firstly, the method uses batch frames to solve the motion compensation in dynamic environment, and optimizes the re-projection error of batch frames to locate low-speed and highspeed moving targets. Then a method of dividing the key feature regions of moving objects is proposed, and the dynamic gradient descent function is introduced to detect the point features of the key feature regions of moving objects. Finally, the edge of the dynamic target is expanded, and the key part of the static region is located by line features. The comparison results show that the point and line feature combination method proposed in this paper can be effectively applied to low-speed and high-speed dynamic scenes, and can accurately locate dynamic objects, with fast real-time performance.

关键词： Point and line feature combinations Dynamic target detection Visual localization Dynamic environment

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Modeling of Shape Memory Alloy Actuated System Using a Modified Rate-dependent Prandtl-Ishlinskii Hysteresis Model 5

Modeling of Shape Memory Alloy Actuated System Using a Modif...

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5th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Liang, Mingwei Feng, Ying South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China

ISBN: (纸本)9781728164793

Shape memory alloys (SMAs) are widely used as actuators to construct various robotics systems, such as, minimally invasive surgery robots, micro in-pipe robots and biomimetic micro robots. However, the nonlinear hysteresis behaviors in SMA actuators seriously degrade the control accuracy and desired performances of SMA-actuated control systems. In this paper, an experimental platform based on SMA actuator is conducted. To make the platform capable of achieving precise control, the complex hysteresis characteristics of the SMA actuators are studied. The SMA actuator in this study exhibits complicated nonlinear properties with saturation nonlinearity and asymmetric hysteresis influenced by both the duty cycle of the input voltage and the load mass, which are different from the classical rate-dependent hysteresis behaviors. To explore the nonlinear properties of the SMA actuated system, open-loop experiments under different duty cycles and loads are testified, and a modified Rate-dependent Prandtl-Ishlinskii (RDPI) model is developed to enhance modeling abilities. Fitting effects of the modified RDPI model is validated by the comparison between the modeling output and experimental data.

关键词： Biomimetics

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Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments

arXiv

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arXiv 2024年

作者： Feng, Zhaohan Chen, Jie Xiao, Wei Sun, Jian Xin, Bin Wang, Gang National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Department of Control Science and Engineering Tongji University Shanghai201804 China

In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Aerodynamic and Aeroacoustic Investigation for the Operation Arrangement of Contra-rotating Rotors

Aerodynamic and Aeroacoustic Investigation for the Operation...

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AIAA Science and Technology (SciTech) Forum

作者： Li, Pengyu Yang, Yannian Zhang, Haoyu Liu, Yu Chen, Xuanwu Li, Qingxi Southern Univ Sci & Technol Dept Mech & Aerosp Engn Shenzhen 518055 Guangdong Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Sch Automat Sci & EngnMinist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Tsinghua Univ Inst Aero Engine Beijing 100084 Peoples R China Avic Gen Aircraft Res Inst Zhuhai 519040 Guangdong Peoples R China

ISBN: (数字)9781624106996

ISBN: (纸本)9781624106996

Contra-rotating rotors have the advantage of improving the load capacity of multi-rotor Unmanned Aerial Vehicle (UAV) under the space limit, and thus it gains interest from industry and can be observed in a lot of applications. By alleviating the interference effect, the performance of the contra-rotating rotors can be augmented further. This research investigates the rotation speed combination of the upper and lower rotor to find the optimal operation conditions. The aerodynamic and acoustic results are measured by two force balances and microphones respectively in an anechoic chamber, respectively. The results indicate that the better performance occurs when the rotational speed of the lower rotor is higher than that of the upper rotor, because the aerodynamic loss of the lower rotor will significantly arise with the increase of the upper rotor RPM. The noise emission does not follow the same trend as the aerodynamic performance, which shows two local minimum values when decreasing the lower rotor RPM and increasing the upper rotor RPM.

关键词： Aerodynamics

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A complex-valued slow independent component analysis based incipient fault detection and diagnosis method with applications to wastewater treatment processes

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ISA TRANSACTIONS 2023年 135卷 213-232页

作者： Xu, Chong Huang, Daoping Cai, Baoping Chen, Hongtian Liu, Yiqi South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510640 Peoples R China Gannan Normal Univ Sch Phys & Elect Informat Ganzhou 341000 Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Sch Automat Sci & Engn Guangzhou 510640 Peoples R China China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Shandong Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada

Multivariate statistical process monitoring are the essential approaches to achieve better prognostics and health management (PHM) of process industries. However, incipient faults and complex behaviors (such as nonlinearity and dynamics) always render the traditional multivariate statistical process monitoring approaches inadequate. Thus, a complex-valued slow independent component analysis (CSICA) is proposed, which is able to extract optimized features from a complex-valued matrix containing both of raw data and their changing rates by resorting to a complex-valued independent component analysis operation and a batch of phase shifts. These features, named slow independent components (SICs), not only guarantee the statistical independence but also capture slowly-changing patterns, thus refining both dynamic and non-Gaussian information mostly related with incipient faults. The proposed algorithm together with novel statistics, Is2, If2 and SPE, as well as their control limits can sequentially detect incipient faults effectively. Then, together with the novel differential mapping reconstructed contribution plot (DM-RCP) and Granger causality analysis, the proposed method can accurately locate rooting causes of incipient faults. Finally, the proposed framework of process monitoring is validated through two data sets from a simulation platform and an oxidationditch-based wastewater treatment plant, respectively. The results demonstrate that the proposed method can achieve more accurate and efficient performances than conventional methods.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： component analysis Incipient faults Process monitoring Auto -correlation Granger causality analysis Wastewater treatment processes

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Efficient View Path Planning for autonomous Implicit Reconstruction

Efficient View Path Planning for Autonomous Implicit Reconst...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jing Zeng Yanxu Li Yunlong Ran Shuo Li Fei Gao Lincheng Li Shibo He Jiming Chen Qi Ye Zhejiang University Hangzhou China Fuxi AI Lab NetEase Hangzhou China State Key Laboratory of Industrial Control Technology and also the Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province College of Control Science and Engineering Zhejiang University China

Implicit neural representations have shown promising potential for 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.

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Adaptive Backstepping control for Piezoelectric Micro Actuating System with Input Saturation 5

Adaptive Backstepping Control for Piezoelectric Micro Actuat...

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5th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Li, Ying Feng, Ying South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China

ISBN: (纸本)9781728164793

Piezoelectric actuators have been attached more importance in the field of high-precision micro actuating due to their excellent performance. However, the presence of the hysteresis nonlinearity and input saturation problems in this smart material actuators have caused adverse effects on their performance, including reduced operational accuracy and damage to the actuators. To eliminate the negative effects of hysteresis and compensate for input saturation, an adaptive backstepping controller with an anti-windup auxiliary compensation system is developed in this paper. The proposed approach shows good tracking performance for the piezoelectric micro actuating system introducing, which also proves that the anti-windup technology can handle the input saturation effects well.

关键词： Piezoelectric actuators

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Design and Implementation of Radiofrequency Ablation System Based on Power Closed-Loop control 7

Design and Implementation of Radiofrequency Ablation System ...

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7th International Conference on Automation, control and Robotics Engineering (CACRE)

作者： Liu, Fuchun Deng, Haixing Huang, Zewen Zeng, Ming South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Guangzhou 510640 Peoples R China Univ South China Sch Elect Engn Hengyang 421001 Peoples R China

ISBN: (数字)9781665403870

ISBN: (纸本)9781665403870

As an emerging device used in surgery, Radiofrequency (RF) ablation is widely used in minimally invasive surgeries. In the process of surgery, the system needs to output constant power to ensure the quality of surgery when the tissue location or ambient temperature and humidity change. The hardware of the RF ablation system was designed in this paper, and the power closed-loop control was realized. The system uses a microcontroller STM32F303VCT6 and the dual ADC alternate sampling method to require the RF output signal. The RF output power is obtained by frequency-domain analysis of the sampled signal using the Discrete Fourier Transform (DFT) algorithm, and the power is controlled using a PID controller so that the actual output power quickly tracks the user-set power. The experimental results of power control show that the controller can adjust the power of the RF ablation system to the set value within 500 ms, without overshoot in the control process and within +/- 1 W of power fluctuation after stabilization. Meanwhile, the controller can realize power tracking of the ablation system when the load changes.

关键词： RF ablation LC resonant circuit RF sampling power closed-loop control

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Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning

arXiv

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arXiv 2023年

作者： Wang, Runqing Wang, Gang Sun, Jian Deng, Fang Chen, Jie The National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China

Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the quality of solutions still has room for improvement relative to that by the exact methods such as OR-Tools. To address this issue, this paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making. The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed. The DAN exploits the complicated relationships to construct production-adaptive operation and machine features to support high-quality decision-making. Experimental results using synthetic data as well as public benchmarks corroborate that the proposed approach outperforms both traditional PDRs and the state-of-the-art DRL method. Moreover, it achieves results comparable to exact methods in certain cases and demonstrates favorable generalization ability to large-scale and real-world unseen FJSP tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions

ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Recons...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Anjun Chen Xiangyu Wang Kun Shi Shaohao Zhu Bin Fang Yingfeng Chen Jiming Chen Yuchi Huo Qi Ye State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Tsinghua University Beijing China Fuxi AI Lab NetEase Hangzhou China State Key Lab of CAD&CG Zhejiang Lab Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province

3D human reconstruction from RGB images achieves decent results in good weather conditions but degrades dramatically in rough weather. Complementary, mmWave radars have been employed to reconstruct 3D human joints and meshes in rough weather. However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images. In this paper, we present ImmFusion, the first mmWave-RGB fusion solution to reconstruct 3D human bodies in all weather conditions robustly. Specifically, our ImmFusion consists of image and point backbones for token feature extraction and a Transformer module for token fusion. The image and point backbones refine global and local features from original data, and the Fusion Transformer Module aims for effective information fusion of two modalities by dynamically selecting informative tokens. Extensive experiments on a large-scale dataset, mmBody, captured in various environments demonstrate that ImmFusion can efficiently utilize the information of two modalities to achieve a robust 3D human body reconstruction in all weather conditions. In addition, our method's accuracy is significantly superior to that of state-of-the-art Transformer-based LiDAR-camera fusion methods.

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