Deep learning based decomposition for visual navigation in industrial platforms

In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms.publishedVersio

Implementation and Comparison of Attitude Estimation Methods for Agricultural Robotics

Abstract: The field of precision agriculture increasingly utilize and develop robotics for various applications, many of which are dependent on high accuracy localization and attitude estimation. Special attention has been put towards full attitude estimation by low-cost sensors, in relation to the development of an autonomous field robot. Quaternions have been chosen due to its continuous nature, and with respect to applications in the pipeline with on other platforms. The performance and complexity of two approaches to attitude estimation has been investigated: One Multiplicative Extended Kalman Filter (MEKF) and one non-linear observer. Both were implemented on an ARM Cortex M3 microcontroller with sensors for a Attitude Heading Reference System (AHRS), and benchmarked towards a relative high grade commercial AHRS device. The relative computational burden of the MEKF have been underlined, by execution times more than 10 times those of the non-linear estimator. The implementation complexity is also significantly lower for the non-linear observer, which facilitate test and verification through more transparent software

Unscented Multi-Point Smoother for Fusion of Delayed Displacement Measurements: Application to Agricultural Robots

Visual Odometry (VO) is increasingly a useful tool for robotic navigation in a variety of applications, including weed removal for agricultural robotics. The methods of evaluating VO are often computationally expensive and can cause the VO measurements to be significantly delayed with respect to a compass, wheel odometry, and GPS measurements. In this paper we present a Bayesian formulation of fusing delayed displacement measurements. We implement solutions to this problem based on the unscented Kalman filter (UKF), leading to what we term an unscented multi-point smoother. The proposed methods are tested in simulations of an agricultural robot. The simulations show improvements in the localization RMS error when including the VO measurements with a variety of latencies

Deep learning based decomposition for visual navigation in industrial platforms

In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms

Design and control of precision drop-on-demand herbicide application in agricultural robotics

Drop-on-demand weed control is a field of research within Precision Agriculture, where the herbicide application is controlled down to individual droplets. This paper focuses on the fluid dynamics and electronics design of the droplet dispensing. The droplets are formed through an array of nozzles, controlled by two-way solenoid valves. A much used control circuit for opening and closing a solenoid valve is a spike and hold circuit, where the solenoid current finally is discharged over a Schottky diode on closing. This paper presents a PWM design, where the discharge is done by reversing the polarity of the voltage. This demands an accurate timing of the reverse spike not to recharge and reopen the valve. The PWM design gives flexibility in choosing the spike and hold voltage arbitrarily, and may use fewer components. Calculations combined with laboratory experiments verify this valve control strategy. In early flight the stability of the tail, or filament, is described in theory by the Ohnesorge number. In later flight, when a droplet shape has formed, the droplet stability is governed by the Weber number. These two considerations have opposite implications on the desired surface tension of the fluid. The Weber number is more important for longer distances, as the filament satelites normally catch up and join the main droplet in flight

Deep learning based decomposition for visual navigation in industrial platforms

In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms

Design and control of precision drop-on-demand herbicide application in agricultural robotics

This is the author’s final, accepted and refereed manuscript to the article.Drop-on-demand weed control is a field of research within Precision Agriculture, where the herbicide application is controlled down to individual droplets. This paper focuses on the fluid dynamics and electronics design of the droplet dispensing. The droplets are formed through an array of nozzles, controlled by two-way solenoid valves. A much used control circuit for opening and closing a solenoid valve is a spike and hold circuit, where the solenoid current finally is discharged over a Schottky diode on closing. This paper presents a PWM design, where the discharge is done by reversing the polarity of the voltage. This demands an accurate timing of the reverse spike not to recharge and reopen the valve. The PWM design gives flexibility in choosing the spike and hold voltage arbitrarily, and may use fewer components. Calculations combined with laboratory experiments verify this valve control strategy. In early flight the stability of the tail, or filament, is described in theory by the Ohnesorge number. In later flight, when a droplet shape has formed, the droplet stability is governed by the Weber number. These two considerations have opposite implications on the desired surface tension of the fluid. The Weber number is more important for longer distances, as the filament satelites normally catch up and join the main droplet in flight.Akseptert fagfellevurdert versjon/postprint. “© © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Robotic in-row weed control in vegetables

Vegetables and other row-crops represent a large share of the agricultural production. There is a large variation in crop species, and a limited availability in specialized herbicides. The robot presented here utilizes systematic growing techniques to navigate and operate in the field. By the use of machine vision it separates seeded vegetable crops from weed. Each weed within the row is treated with individual herbicide droplets, without affecting the crop. This results in a significant reduction in herbicide use, and allows for the use of herbicides that would otherwise harm the crop. The robot is tailored to this purpose with cost, maintainability, efficient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benefit of reduced weight, complexity and cost. Indoor pot trials with four weed species demonstrated that the Drop-on-Demand system (DoD) could control the weeds with as little as 7.6 μg glyphosate or 0.15 μg iodosulfuron per plant. The results also highlight the importance of liquid characteristics for droplet stability and leaf retention properties. The common herbicide glyphosate had no effect unless mixed with suitable additives. A field trial with the robot was performed in a carrot field, and all the weeds were effectively controlled with the DoD system applying 5.3 μg of glyphosate per droplet. The robot and DoD system represent a paradigm shift to the environmental impact and health risks of weed control, while providing a valuable tool to the producers