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In-field early disease recognition of potato late blight based on deep learning and proximal hyperspectral imaging

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arXiv 2021年

作者： Qi, Chao Sandroni, Murilo Westergaard, Jesper Cairo Sundmark, Ea Høegh Riis Bagge, Merethe Alexandersson, Erik Gao, Junfeng Lincoln Agri-Robotics Lincoln Institute for Agri-Food Technology University of Lincoln Lincoln United Kingdom College of Engineering Nanjing Agricultural University Nanjing210031 China Department of Plant Protection Biology Swedish University of Agricultural Sciences Alnarp Sweden Department of Plant and Environmental Sciences University of Copenhagen Taastrup Denmark Danespo Breeding Company Give Denmark Lincoln Centre for Autonomous System University of Lincoln Lincoln United Kingdom

Effective early detection of potato late blight (PLB) is an essential aspect of potato cultivation. However, it is a challenge to detect late blight at an early stage in fields with conventional imaging approaches because of the lack of visual cues displayed at the canopy level. Hyperspectral imaging can, capture spectral signals from a wide range of wavelengths also outside the visual wavelengths. In this context, we propose a deep learning classification architecture for hyperspectral images by combining 2D convolutional neural network (2D-CNN) and 3D-CNN with deep cooperative attention networks (PLB-2D-3D-A). First, 2D-CNN and 3D-CNN are used to extract rich spectral space features, and then the attention mechanism AttentionBlock and SE-ResNet are used to emphasize the salient features in the feature maps and increase the generalization ability of the model. The dataset is built with 15,360 images (64x64x204), cropped from 240 raw images captured in an experimental field with over 20 potato genotypes. The accuracy in the test dataset of 2000 images reached 0.739 in the full band and 0.790 in the specific bands (492nm, 519nm, 560nm, 592nm, 717nm and 765nm). This study shows an encouraging result for early detection of PLB with deep learning and proximal hyperspectral imaging. Copyright © 2021, The Authors. All rights reserved.

关键词： Hyperspectral imaging

6D Pose estimation with correlation fusion

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arXiv 2019年

作者： Cheng, Yi Zhu, Hongyuan Sun, Ying Acar, Cihan Jing, Wei Wu, Yan Li, Liyuan Tan, Cheston Lim, Joo-Hwee Visual Intelligence System Insitute for Infocomm Research Agency for science Technoogy and Research Robotics & Autonomous Systems Institute for Infocomm Research Agency for Science Technology and Research

6D object pose estimation is widely applied in robotic tasks such as grasping and manipulation. Prior methods using RGB-only images are vulnerable to heavy occlusion and poor illumination, so it is important to complement them with depth information. However, existing methods using RGB-D data cannot adequately exploit consistent and complementary information between RGB and depth modalities. In this paper, we present a novel method to effectively consider the correlation within and across both modalities with attention mechanism to learn discriminative and compact multi-modal features. Then, effective fusion strategies for intra- and inter-correlation modules are explored to ensure efficient information flow between RGB and depth. To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation. The experimental results show that our method can achieve the state-of-the-art performance on LineMOD and YCB-Video dataset. We also demonstrate that the proposed method can benefit a real-world robot grasping task by providing accurate object pose estimation. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

A Map Accessibility Analysis Algorithm for Mobile Robot Navigation in Outdoor Environment

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A Map Accessibility Analysis Algorithm for Mobile Robot Navi...

IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Xiangyu Yu Jingchuan Wang Weidong Chen Hesheng Wang Ministry of Education of China Key Laboratory of System Control and Information Processing and Institute of Medical Robotics Shanghai Jiao Tong University and Autonomous Robot Lab Shanghai China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

关键词： Three-dimensional displays Mobile robots Laser radar Navigation Task analysis Real-time systems

A CNN-LSTM hybrid framework for wrist kinematics estimation using surface electromyography

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arXiv 2019年

作者： Bao, Tianzhe Zaidi, Syed Ali Raza Xie, Shengquan Yang, Pengfei Zhang, Zhiqiang Institute of Robotics Autonomous System and Sensing School of Electrical and Electronic Engineering University of Leeds LS2 9JT United Kingdom School of Computer Science and Technology Xidian University China

—Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNN-LSTM hybrid framework to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN and conventional machine learning approaches, particularly when complex wrist movements are activated. Copyright © 2019, The Authors. All rights reserved.

关键词： Kinematics

Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors

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arXiv 2019年

作者： Salt, Llewyn Howard, David Indiveri, Giacomo Sandamirskaya, Yulia School of Information Technology and Electrical Engineering University of Queensland QLD Australia Robotics and Autonomous Systems Group in the Cyberphysical System Program CSIRO QLD Australia Institute of Neuroinformatics University of Zurich ETH Zurich Zurich Switzerland

he Lobula Giant Movement Detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect’s escape responses. Understanding the neural principles and network structure that lead to these fast and robust responses can facilitate the design of efficient obstacle avoidance strategies for robotic applications. Here we present a neuromorphic spiking neural network model of the LGMD driven by the output of a neuromorphic Dynamic Vision Sensor (DVS), which incorporates spiking frequency adaptation and synaptic plasticity mechanisms, and which can be mapped onto existing neuromorphic processor chips. However, as the model has a wide range of parameters, and the mixed signal analogue-digital circuits used to implement the model are affected by variability and noise, it is necessary to optimise the parameters to produce robust and reliable responses. Here we propose to use Differential Evolution (DE) and Bayesian Optimisation (BO) techniques to optimise the parameter space and investigate the use of Self-Adaptive Differential Evolution (SADE) to ameliorate the difficulties of finding appropriate input parameters for the DE technique. We quantify the performance of the methods proposed with a comprehensive comparison of different optimisers applied to the model, and demonstrate the validity of the approach proposed using recordings made from a DVS sensor mounted on a UAV. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

Retrieval-based localization based on domain-invariant feature learning under changing environments

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arXiv 2019年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Yang, Chenguang Chen, Weidong Xie, Le Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China T Stone Robotics Institute Chinese University of Hong Kong Hong Kong Bristol Robotics Laboratory University of the West of England BristolBS16 1QY United Kingdom School of Material Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Visual localization is a crucial problem in mobile robotics and autonomous driving. One solution is to retrieve images with known pose from a database for the localization of query images. However, in environments with drastically varying conditions (e.g. illumination changes, seasons, occlusion, dynamic objects), retrieval-based localization is severely hampered and becomes a challenging problem. In this paper, a novel domain-invariant feature learning method (DIFL) is proposed based on ComboGAN, a multi-domain image translation network architecture. By introducing a feature consistency loss (FCL) between the encoded features of the original image and translated image in another domain, we are able to train the encoders to generate domain-invariant features in a self-supervised manner. To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector. We then preform retrieval by selecting the database image with the most similar domain-invariant feature vector. We validate the proposed approach on the CMU-Seasons dataset, where we outperform state-of-the-art learning-based descriptors in retrieval-based localization for high and medium precision scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Towards a soft robotic skin for autonomous tissue palpation

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Towards a soft robotic skin for autonomous tissue palpation

2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Campisano, Federico Ozel, Selim Ramakrishnan, Anand Dwivedi, Anany Gkotsis, Nikolaos Onal, Cagdas D. Valdastri, Pietro STORM Lab Department of Mechanical Engineering Vanderbilt University NashvilleTN United States WPI Soft Robotics Lab Department of Mechanical Engineering and Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA United States Department of Robotics and Autonomous System University of Leeds Leeds United Kingdom

ISBN: (纸本)9781509046331

Manual palpation is commonly used to localize tumors and other features buried deep inside organs during open surgery. This approach is not feasible in minimally invasive or robotic surgery, as the contact with the tissue is mediated by instruments. To address this problem, we propose a soft robotic skin (SRS) that can be deployed from a small incision and create a stiffness map in a single step. Such a skin is composed of a matrix of soft robotic tactile elements (SRTEs), each one able to expand and record the tissue response during expansion. In this paper, we firstly prove the feasibility of palpation using a single SRTE. Then, we present and test a soft-suction based anchoring mechanism able to keep the SRS in the desired position in contact with the tissue, allowing surgeons to palpate different sides of the organ. Finally, we detail a calibration method for the SRTE, and assess the feasibility of identifying lumps buried inside a soft tissue phantom, and then inside a chicken liver during an ex-vivo trial. Experimental results show that the SRTE was able to differentiate simulated lumps (up to 3.25 mm deep) from healthy tissue in both the phantom and the ex-vivo trials. These results, added to the ability of the suction gripper to compensate for the expansion forces of each SRTE, are paving the way for soft robotic autonomous tools that can be used for intraoperative mapping of tissue cancers. © 2017 IEEE.

关键词： Tissue

Why and how to avoid the flipped quaternion multiplication

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arXiv 2018年

作者： Sommer, Hannes Gilitschenski, Igor Bloesch, Michael Weiss, Stephan Siegwart, Roland Nieto, Juan Autonomous Systems Lab ETH Zurich Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Dyson Robotics Lab Imperial College London United Kingdom Institute of Smart System Technologies Alpen-Adria-Universitat Klagenfurt Austria

Over the last decades quaternions have become a crucial and very successful tool for attitude representation in robotics and aerospace. However, there is a major problem that is continuously causing trouble in practice when it comes to exchanging formulas or implementations: there are two quaternion multiplications in common use, Hamilton's multiplication and its flipped version, which is often associated with NASA's Jet Propulsion Laboratory. We believe that this particular issue is completely avoidable and only exists today due to a lack of understanding. This paper explains the underlying problem for the popular passive world to body usage of rotation quaternions, and derives an alternative solution compatible with Hamilton's multiplication. Furthermore, it argues for entirely discontinuing the flipped multiplication. Additionally, it provides recipes for efficiently detecting relevant conventions and migrating formulas or algorithms between them. Copyright © 2018, The Authors. All rights reserved.

关键词： NASA

Towards a soft robotic skin for autonomous tissue palpation

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Towards a soft robotic skin for autonomous tissue palpation

IEEE International Conference on robotics and Automation (ICRA)

作者： Federico Campisano Selim Ozel Anand Ramakrishnan Anany Dwivedi Nikolaos Gkotsis Cagdas D. Onal Pietro Valdastri Department of Mechanical Engineering Vanderbilt University Nashville TN USA Worcester Polytechnic Institute Worcester MA US Department of Mechanical Engineering and Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA Department of Robotics and Autonomous System University of Leeds Leeds UK

关键词： Surgery Rubber Soft robotics Grippers Calibration Robot sensing systems