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arXiv 2024年

作者： Biswas, Debojyoti Sontag, Eduardo D. Cowan, Noah J. Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Department of Bioengineering Northeastern University BostonMA United States Department of Mechanical Engineering Johns Hopkins University BaltimoreMD United States

We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium point. To overcome this fundamental limitation, we propose a simple control scheme that includes a low-amplitude periodic forcing function akin to so-called "active sensing" in biology, together with nonlinear output feedback. Our analysis shows that this approach leads to the emergence of an exponentially stable limit cycle. These findings offer a provably stable active sensing strategy and may thus help to rationalize the active sensing movements made by animals as they perform certain motor behaviors. © 2024, CC BY-NC-SA.

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Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning

arXiv

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arXiv 2023年

作者： Shi, Haojie Zhu, Qingxu Han, Lei Chi, Wanchao Li, Tingguang Meng, Max Q.-H. Tencent Robotics X China The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

In nature, legged animals have developed the ability to adapt to challenging terrains through perception, allowing them to plan safe body and foot trajectories in advance, which leads to safe and energy-efficient locomotion. Inspired by this observation, we present a novel approach to train a Deep Neural Network (DNN) policy that integrates proprioceptive and exteroceptive states with a parameterized trajectory generator for quadruped robots to traverse rough terrains. Our key idea is to use a DNN policy that can modify the parameters of the trajectory generator, such as foot height and frequency, to adapt to different terrains. To encourage the robot to step on safe regions and save energy consumption, we propose foot terrain reward and lifting foot height reward, respectively. By incorporating these rewards, our method can learn a safer and more efficient terrain-aware locomotion policy that can move a quadruped robot flexibly in any direction. To evaluate the effectiveness of our approach, we conduct simulation experiments on challenging terrains, including stairs, stepping stones, and poles. The simulation results demonstrate that our approach can successfully direct the robot to traverse such tough terrains in any direction. Furthermore, we validate our method on a real legged robot, which learns to traverse stepping stones with gaps over 25.5cm. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Inspection of a helicopter blade using drone based active thermography 45

Inspection of a helicopter blade using drone based active th...

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Thermosense: Thermal Infrared Applications XLV 2023

作者： Pant, Shashank Genest, Marc Ibarra-Castanedo, Clemente Avdelidis, Nicolas P. Valdes, Julio Deane, Shakeb Zolotas, Argyrios Maldague, Xavier P. National Research Council Canada OttawaONK1A 0R6 Canada Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University Quebec CityQCG1V 0A6 Canada School of Aerospace Transport and Manufacturing Cranfield University CranfieldMK43 0AL United Kingdom

ISBN: (纸本)9781510661868

A drone-based inspection system that can move "freely" around an aircraft to perform the inspection of all the areas of interest in a fast and effective manner can have significant impact in reducing inspection time and cost. However, active thermography inspection using drone is challenging because the drone carrying the optical and thermal cameras is subjected to vibration and undesired motion. Since active thermography relies on the pixel temperature evolution over time, an unstable thermal video from a flying drone can cause error in the output results as any movement between the acquired images will affect the pixel position in the successive frames and thus disrupt the monitoring of the temperature evolution. This paper presents the outcome of experimental runs, where a commercially available drone equipped with both thermal and optical cameras was used to inspect a helicopter Main Rotor Blade (MRB) in a laboratory environment. © 2023 SPIE.

关键词： Inspection

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Pose focus transformer meet inter-part relation

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Expert Systems with Applications 2024年 240卷

作者： Luo, Yanmin Lin, Hongwei Huang, Wenlin Wang, Youjie Du, Jixiang Guo, Jing-Ming College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Electrical Engineering National Taiwan University of Science and Technology Taipei10607 China

Human pose estimation in crowded scenes is a challenging task. Due to overlap and occlusion, it is difficult to infer pose clues from individual keypoints. We proposed PFFormer, a new transformer-based approach that treats pose estimation as a hierarchical set prediction problem that first focuses on human windows and coarsely predicts whole-body poses globally within them. In PFFormer, we designed a Windows Clustering Transformer (WCT), which reorganizes the image windows by filtering the attentive windows and fusing the inattentive ones, allowing the transformer to focus on the important regions while reducing the interference from the complex background, followed by compensating for the loss of information with a global transformer. Then we partition the learned body pose into a set of structural parts and perform the Inter-Part Relation Module (IPRM) to capture the correlation between multiple parts. These full-body poses and component features are refined at a finer level through the Part-to-Joint Decoder (PJD). Extensive experiments show that PFFormer performs favorably against its counterpart on challenging datasets, including COCO2017, CrowdPose, and OChuman datasets. The performance of crowded scenes, in particular, demonstrates the robustness of the proposed methods to deal with occlusion. © 2023 Elsevier Ltd

关键词： Information filtering

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Extension Principle and Controller Design for Nonlinear Systems

Extension Principle and Controller Design for Nonlinear Syst...

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European Control Conference (ECC)

作者： Altuğ İftar Farshad Khorrami Department of Electrical and Electronics Engineering Eskişehir Technical University Eskişehir Turkey Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) New York University 5 Metrotech Center Brooklyn NY U.S.A.

We generalize the principle of extension and controller design using this principle for nonlinear systems. We first define principles of inclusion and extension for nonlinear time-invariant (NLTI) systems. Thereafter, we present the necessary and sufficient conditions for a system to be an extension of another and show that when the expanded system is an extension of the original system, any controller designed for the expanded system can be contracted for implementation on the original system. Subsequently, we demonstrate how an overlappingly decomposed NLTI system can be expanded so that the expanded system is an extension of the original system, how decentralized controllers can be designed for the expanded system, and how these controllers can be contracted for implementation on the original system. An example is also provided to illustrate the extension and contraction concepts and show controller design using the proposed principles.

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An End-to-End Human Simulator for Task-Oriented Multimodal Human-Robot Collaboration

An End-to-End Human Simulator for Task-Oriented Multimodal H...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Afagh Mehri Shervedani Siyu Li Natawut Monaikul Bahareh Abbasi Barbara Di Eugenio Miloš Žefran Department of Electrical and Computer Engineering Robotics Laboratory University of Illinois Chicago Chicago IL USA Department of Computer Science Natural Language Processing Laboratory University of Illinois Chicago Chicago IL USA Computer Science Department California State University Channel Islands Camarillo CA USA

This paper proposes a neural network-based user simulator that can provide a multimodal interactive environment for training Reinforcement Learning (RL) agents in collaborative tasks involving multiple modes of communication. The simulator is trained on the existing ELDERLY-AT-HOME corpus and accommodates multiple modalities such as language, pointing gestures, and haptic-ostensive actions. The paper also presents a novel multimodal data augmentation approach, which addresses the challenge of using a limited dataset due to the expensive and time-consuming nature of collecting human demonstrations. Overall, the study highlights the potential for using RL and multimodal user simulators in developing and improving domestic assistive robots.

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhou, Yang Zhu, Shiqiang Huang, Huang Li, Yuehua Gu, Jason The Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China The Beijing Institute of Control Engineering Beijing100094 China The Beijing Research Institute Zhejiang Laboratory Beijing100094 China The Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory. © 2022 IEEE.

关键词： Motion planning

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UAV Trajectory Evaluation in Large Industrial Environments: A Cost-Effective Solution

UAV Trajectory Evaluation in Large Industrial Environments: ...

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European Control Conference (ECC)

作者： Jakob Grimm Hansen Micha Heiß Michal Kozlowski Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (Air Lab) Aarhus University Aarhus C Denmark

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

The use of unmanned aerial vehicles (UAVs) for autonomous inspection tasks has become more prominent in recent years. To make the most of the autonomous inspection, the parameters governing control, perception and navigation of the robot should be tuned precisely to the necessary task. Currently, the use of motion capture (mocap) systems is the norm when performing the stringent evaluation of simultaneous localization and mapping (SLAM) and advanced controllers. In this paper, we address the use of a cost-effective solution to ground-truthing and evaluation of said algorithms in large industrial environments. To this end, we use fiducial markers, deployed in known locations, in order to estimate the pose of the vehicle in 6 degrees-of-freedom (6DOF) and test them against a state-of-the-art mocap system. We additionally test the method in the field, by deploying the markers to the environment of interest and applying widely used SLAM implementations to confirm its efficacy by evaluating their performance in two emulated inspection task scenarios. We find that our method is comparable in performance to the state-of-the-art mocap systems without the need for laborious calibration and is capable of providing a pose estimate for evaluating SLAM and underlying UAV control methods.

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Development of a compact mouse retinal imaging system using spectral domain optical coherence tomography (SD-OCT) with a full field stimulation channel for recording fast optoretinograms (ORGs) 34

Development of a compact mouse retinal imaging system using ...

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Ophthalmic Technologies XXXIV 2024

作者： Jo, Hang Chan Pijewska, Ewelina Meleppat, Ratheesh K. Jonnal, Ravi S. Kim, Dae Yu Zawadzki, Robert J. Department of Electrical and Computer Engineering Inha University 100 Inha-ro Michuhol-gu Incheon22212 Korea Republic of Dept. of Ophthalmology and Vision Science UC Davis Eye Center SacramentoCA95817 United States UC Davis Eye-Pod Small Animal Ocular Imaging Laboratory Department of Cell Biology and Human Anatomy University of California Davis 4320 Tupper Hall DavisCA95616 United States

ISBN: (纸本)9781510669079

While the a-wave of mouse electroretinogram (ERG) occurs within 50 milliseconds after exposure to light, the optoretinogram (ORG) slower than a 20Hz sampling rate could face limitations in observing immediate morphological changes from the retina. In this study, we develop a compact custom-built mouse ORG system based on spectral domain optical coherence tomography (SD-OCT) for 100Hz~1KHz B-scan rates comprised of 100 kHz A-scans. All the optics of the developed ORG system are designed on a 24 x 24 inches optical breadboard to move easily as well as to combine with the ERG system in a dark room. Without using a fundus camera, the OCT system provides en-face images from high-pass filtering and square of the OCT spectral signal for mouse retinal positioning in-vivo before acquiring ORG data. The 490nm LED for light stimulus is generated to make uniform illumination at the mouse retina using the Maxwellian view method. The common path of the OCT scanning light and the visible LED is built with achromatic doublet lens combinations based on optical simulation with Opticstudio® . The developed compact ORG system can not only observe light-evoked responses with 1~10 milliseconds but also be used for the studies of correlations between ORG and ERG in the mouse retina. © 2024 SPIE.

关键词： Optical tomography

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Detail-Enhancing Framework for Reference-Based Image Super-Resolution

arXiv

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arXiv 2024年

作者： Wang, Zihan Xiong, Ziliang Tang, Hongying Yuan, Xiaobing Shanghai Institution of Micro-system and Information Technology University of Chinese Academy of Science China Computer Vision Laboratory Department of Electrical Engineering LiU Sweden School of Information Science and Technology Shanghai Tech University China

Recent years have witnessed the prosperity of reference-based image super-resolution (Ref-SR). By importing the high-resolution (HR) reference images into the single image super-resolution (SISR) approach, the ill-posed nature of this long-standing field has been alleviated with the assistance of texture transferred from reference images. Although the significant improvement in quantitative and qualitative results has verified the superiority of Ref-SR methods, the presence of misalignment before texture transfer indicates room for further performance improvement. Existing methods tend to neglect the significance of details in the context of comparison, therefore not fully leveraging the information contained within low-resolution (LR) images. In this paper, we propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution, which introduces the diffusion model to generate and enhance the underlying detail in LR images. If corresponding parts are present in the reference image, our method can facilitate rigorous alignment. In cases where the reference image lacks corresponding parts, it ensures a fundamental improvement while avoiding the influence of the reference image. Extensive experiments demonstrate that our proposed method achieves superior visual results while maintaining comparable numerical outcomes. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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