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Time-varying Quaternion Constrained Attitude control Using Barrier Lyapunov Function

Time-varying Quaternion Constrained Attitude Control Using B...

引用

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

作者： Srianish Vutukuri Arghya Chakravarty Radhakant Padhi Department of Aerospace Enginnering Indian Institute of Science Bengaluru India

ISBN: (纸本)9781665476881

A novel, robust attitude controller for rigid bodies in the presence of time-varying orientation constraints is presented in this paper. Using an error transformation, the dynamic attitude constraints are converted into time-varying quaternion constraints. Subsequently, a robust attitude control law is synthesized using the backstepping philosophy in which barrier Lyapunov functions (BLFs) are used to achieve asymptotic tracking and simultaneously avoid attitude constraint violation. this is accomplished by ensuring the boundedness of BLFs in the closed-loop Lyapunov stability analysis. Besides the nominal scenario, an adaptive control law is also formulated to tackle moment of inertial uncertainties and an unknown, time-varying, bounded disturbance. In this case, the attitude tracking errors are uniformly ultimately bounded, whose bounds can be adjusted by user-defined constants. Note that in both scenarios, the dynamic attitude constraints are not transgressed. Finally, the effectiveness of the proposed controller is demonstrated by carrying out extensive numerical simulations in the presence of parametric uncertainties and disturbance torques.

关键词： Uncertainty Backstepping Philosophical considerations Attitude control Quaternions Numerical simulation Stability analysis

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An Iterative Learning control for Nonlinear Systems without Arimoto's Assumptions

An Iterative Learning Control for Nonlinear Systems without ...

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

作者： Chiang-Ju Chien Chen Liu Xiaoe Ruan Dong Shen Department of Technology for Smart Living Huafan University New Taipei City Taiwan School of Mathematics and Statistics Xian Jiaotong University Xian China School of Mathematics Renmin University of China Beijing China

ISBN: (纸本)9781665476881

the iterative learning control problem for nonlinear systems without the requirements of Arimoto's assumptions is investigated in this paper. Typical Arimoto's assumptions for iterative learning control require that initial state error, system parameters, desired trajectory, external disturbance, and trial length are all iteration-invariant. When the assumptions are not satisfied, we call the system exists iteration-varying uncertainties. In general, it is a big challenge to design an iterative learning controller for systems with all the iteration-varying uncertainties. In this paper, we transform the iteration-varying uncertainties into an additive-type uncertainty and modify the traditional adaptive law so that a novel adaptive iterative learning controller can be applied to successfully solve the repetitive control problem. Compared with the existing works dealing with a similar issue of iteration-varying uncertainties, this proposed approach can be applied to the more general class of iteration-varying uncertain nonlinear systems and achieve better learning performance.

关键词： Uncertainty automation Adaptive systems Transforms control systems Trajectory Nonlinear systems

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Inflatable Robotic Manipulator for Space Debris Mitigation by Visual Servoing

Inflatable Robotic Manipulator for Space Debris Mitigation b...

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international conference on Autonomous Robots and Agents, ICARA

作者： Pierpaolo Palmieri Mario Troise Matteo Gaidano Matteo Melchiorre Stefano Mauro Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy

this paper describes the latest application of the inflatable robotic manipulator called POPUP. It consists of two inflatable links, made of fibers with high elastic module, electric motors and a gripper. this architecture allows volume and weight reduction, still maintaining the same payload compared to traditional solutions if the internal pressure is set properly. this robot, specifically designed for space application, here goes into action for debris mitigation by means of a vision system. First, some of the aspects related to the physical modelling are discussed to point out the working ranges of such a system, mainly defined by wrinkling effects. then, a debris capture procedure is simulated to investigate the possibility to catch the target despite the soft nature of the robot. the multibody model takes into account robot dynamics and contact forces with the target. A control strategy based on closed loop inverse kinematics and visual servoing is proposed. the strategy relies on joint velocity command, computed starting from signals of two camera systems, one observing from the robot base, another moving with the end-effector. this ensures the correct tracking of the target and allows to overcome potential errors deriving from a flexible kinematic chain. Results show the POPUP successfully reaching the target and dictate the next steps for a real-world application.

关键词： Target tracking Machine vision Space missions Kinematics Switches Cameras Visual servoing

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

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12th international conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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Modeling Competing Agents in Social Media Networks

Modeling Competing Agents in Social Media Networks

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

作者： Mohamed N. Zareer Rastko R. Selmic Department of Electrical and Computer Engineering Concordia University Montreal QC Canada

ISBN: (纸本)9781665476881

In this paper, we consider a discrete private and expressed (synchronous and asynchronous) opinion dynamics model with competitive relationships. Unlike the usual agent-based opinion dynamics models, competition between individuals is investigated in a social media network. the expressed opinions, or states of the individuals in the network, are represented by asynchronous dynamics, where each individual has a choice to express his/her opinion at each time step. Each agent uses a Q-earning algorithm to decide when to express its opinion with the purpose of swaying the opinions of other connected agents to a desired outcome. the private opinions, or states of the individuals, are derived from a combination of their own private opinions and the expressed opinions of connected agents. the dynamics of the social media environment are modeled by private and expressed, both asynchronous and synchronous, opinion dynamics model. the system is investigated for polarization or consensus under different conditions. To illustrate the results, simulations of the system dynamics are provided.

关键词： Analytical models Q-learning automation Social networking (online) System dynamics Simulation Heuristic algorithms

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Deep Learning Methods for Ship Classification: From Visible to Infrared Images

Deep Learning Methods for Ship Classification: From Visible ...

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robotics, Intelligent control and Artificial Intelligence (RICAI), international conference on

作者： Tianci Liu Hengjia Qin Zhuo Zhan Yunpeng Liu Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Laboratory of Image Understanding and Computer Vision Shenyang Liaoning Province China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Shenyang Liaoning Province China

Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. the rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-to-end convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the V AIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the V AIS database.

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FAST AND VALID $\mathrm{H}/\alpha$ DECOMPOSITION COMBINED WIth MODEL-BASED DECOMPOSITION FOR POLSAR DATA

FAST AND VALID $\mathrm{H}/\alpha$ DECOMPOSITION COMBINED WI...

引用

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

作者： Gaofeng Liu Ming Li Peng Zhang Math College Leshan Normal University Leshan China National Laboratory of Radar Signal Processing Xidian University Xian China

ISBN: (纸本)9781665476881

First, since time consumption for extracting $\alpha$ will become quite tedious for very large images by pixelwise eigendecomposition, we proposed a fast $\alpha$ angle's solution. Second, $\alpha$ may be not unique that results in the invalidity of $\mathrm{H}/\ \alpha$ decomposition. So we early proposed a sound discriminant to distinguish $\alpha$ is unique or not. third, since depolarization is serious and $\alpha$ is unstable in high entropy zone, grass and flourishing canopy etc may be misclassified as double bounce scattering. therefore we proposed a fast algorithm that $\mathrm{H}/\alpha$ decomposition is combined with the model-based decomposition, which overcomes the shortcomings of the invalidity of $\mathrm{H}/\alpha$ decomposition and the misclassification in high-entropy zone, and its time consumption is much shorter than $\mathrm{H}/\alpha$ decomposition.

关键词： automation Scattering Entropy Data models Polarimetric synthetic aperture radar Classification algorithms Robots

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GNSS-Augmented LiDAR SLAM for Accurate Vehicle Localization in Large Scale Urban Environments

GNSS-Augmented LiDAR SLAM for Accurate Vehicle Localization ...

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

作者： Krzysztof Ćwian Michał R. Nowicki Piotr Skrzypczyński Institute of Robotics and Machine Intelligence Poznan University of Technology Poznan Poland

ISBN: (纸本)9781665476881

Although accurate and reliable localization is a prerequisite for autonomous driving, in urban environments neither the Global Navigation Satellite System (GNSS) nor the Simultaneous Localization and Mapping (SLAM) ensure satisfying results in terms of both local accuracy and global consistency. Hence, we contribute in this paper a method to augment the existing LiDAR-based SLAM systems with GNSS measurements, applying the factor graph formulation of the problem. We contribute a tightly coupled GNSS/LiDAR SLAM considering constraints from LiDAR and GNSS measurements, and propose a filtering procedure to cope with GNSS measurements that introduce non-Gaussian noise. We evaluate our approach on the challenging UrbanNav dataset, considering different LiDAR SLAM algorithms and different GNSS receivers, and showing that our solution outperforms previous approaches to GNSS/LiDAR integration.

关键词： Location awareness Global navigation satellite system Visualization Simultaneous localization and mapping Laser radar Urban areas Receivers

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Collision-Free Welding Torch Pose Determination through Imitation Learning with Expert Demonstrations

Collision-Free Welding Torch Pose Determination Through Imit...

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IEEE international conference on control and robotics Engineering (ICCRE)

作者： Teham Bhuiyan Huy Flach Jens Lambrecht Stefan Gasper Jochen Funder Jobst Bickendorf Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany AUTOCAM Informationstechnik GmbH Dortmund Germany

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

In collaboration with our industrial partners at Autocam [1], we developed this Deep Learning (DL) based assistance system for robotic welding. the system is tailored to address the challenges encountered in real-world industrial settings, where conventional programming methods face limitations, particularly in constrained spaces. Autocam provided us with authentic industrial datasets and 3D models. Our approach focuses on automating the identification of collision-free torch poses, crucial for navigating intricate environments commonly encountered in industrial robotic operations. To capture the intricacies of these environments, we utilize four virtual cameras, each strategically positioned to provide a unique perspective. this multi-perspective observation facilitates the development of a robust relationship between 3D space and collision dynamics. the DL model is trained using a combination of Imitation Learning and Deep Reinforcement Learning (DRL). this training process not only empowers the agent to autonomously learn collision-free poses but also allows for the assimilation of existing solutions derived from real-world industrial scenarios. By leveraging computer vision and combining the strengths of both imitation learning and DRL, our model aspires to offer a more adaptive and efficient solution for automation in challenging spatial configurations within industrial robotic operations.

关键词： Training three-dimensional displays Service robots Navigation Welding Imitation learning Robot vision systems

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Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks

Allo-centric Occupancy Grid Prediction for Urban Traffic Sce...

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

作者： Rabbia Asghar Lukas Rummelhard Anne Spalanzani Christian Laugier Univ. Grenoble Alpes Inria Grenoble France

ISBN: (纸本)9781665476881

Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used ego-centric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the traffic scene in a fixed frame, referred as allo-centric occupancy grid. this allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'. We study the allo-centric grid prediction with different video prediction networks and validate the approach on the real-world Nuscenes dataset. the results demonstrate that the allo-centric grid representation significantly improves scene prediction, in comparison to the conventional ego-centric grid approach.

关键词： automation Navigation Roads Vehicle dynamics Robots Autonomous vehicles

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