ДОЦІЛЬНІСТЬ ЗАСТОСУВАННЯ БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТУ НА НАФТОГАЗОПРОВОДАХ

The article considers the possibilities of using unmanned aerial vehicles for monitoring the main pipelines of the gas transmission system.У статті розглянуті можливості застосування безпілотних літальних апаратів для моніторингу магістральних трубопроводів газотранспортної системи

UAV as an inspection tool for power lines in Troms: Assessment of UAVs as a viable alternative to already established methods for inspection of power lines.

Power line inspection is one of the most important things in a modern society, and therefore it is important that it be kept in a functional state. Because of this, many methods are used to keep it that way. There are many factors to consider before using UAVs as a viable alternative to already established methods. Factors such as robustness against harsh weather, limits to its range and battery life are all important factors which will affect the overall usefulness of UAVs compared to other methods. The main purpose of UAVs in the context of electrical transmission grids is to use for inspection of power lines and poles. Troms Kraft have over 3500 kilometres of power lines above ground, and these must be inspected on a yearly basis according to Norwegian law. Often used methods for this include the usage of helicopters, snowmobiles and by foot, and UAVs could potentially be used as an alternative to these in many situations. When UAVs are being operated by humans, there are many things that could go wrong, leading to a loss of the UAV. It is important to be aware of these factors, what consequences they could lead to and how to prevent them from happening

An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model for Enhancing UAV Landing on Moving Targets

The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which takes the image as input and provides the optimal navigation action as output. However, the delay of the multi-layer topology of the deep neural network affects the real-time aspect of such control. This paper proposes an adaptive multi-level quantization-based reinforcement learning (AMLQ) model. The AMLQ model quantizes the continuous actions and states to directly incorporate simple Q-learning to resolve the delay issue. This solution makes the training faster and enables simple knowledge representation without needing the DNN. For evaluation, the AMLQ model was compared with state-of-art approaches and was found to be superior in terms of root mean square error (RMSE), which was 8.7052 compared with the proportional-integral-derivative (PID) controller, which achieved an RMSE of 10.0592

Twin Delayed Deep Deterministic Policy Gradient-Based Target Tracking for Unmanned Aerial Vehicle with Achievement Rewarding and Multistage Training

Target tracking using an unmanned aerial vehicle (UAV) is a challenging robotic problem. It requires handling a high level of nonlinearity and dynamics. Model-free control effectively handles the uncertain nature of the problem, and reinforcement learning (RL)-based approaches are a good candidate for solving this problem. In this article, the Twin Delayed Deep Deterministic Policy Gradient Algorithm (TD3), as recent and composite architecture of RL, was explored as a tracking agent for the UAV-based target tracking problem. Several improvements on the original TD3 were also performed. First, the proportional-differential controller was used to boost the exploration of the TD3 in training. Second, a novel reward formulation for the UAV-based target tracking enabled a careful combination of the various dynamic variables in the reward functions. This was accomplished by incorporating two exponential functions to limit the effect of velocity and acceleration to prevent the deformation in the policy function approximation. In addition, the concept of multistage training based on the dynamic variables was proposed as an opposing concept to one-stage combinatory training. Third, an enhancement of the rewarding function by including piecewise decomposition was used to enable more stable learning behaviour of the policy and move out from the linear reward to the achievement formula. The training was conducted based on fixed target tracking followed by moving target tracking. The flight testing was conducted based on three types of target trajectories: fixed, square, and blinking. The multistage training achieved the best performance with both exponential and achievement rewarding for the fixed trained agent with the fixed and square moving target and for the combined agent with both exponential and achievement rewarding for a fixed trained agent in the case of a blinking target. With respect to the traditional proportional differential controller, the maximum error reduction rate is 86%. The developed achievement rewarding and the multistage training opens the door to various applications of RL in target tracking

Fast and stable direct relative orientation of UAV-based stereo pair

Coplanarity-based relative orientation (RO) is one of the most crucial processes to obtain reliable 3D model and point clouds in Computer Vision and Photogrammetry community. Whilst a classical and rigorous procedure requires very close approximate values of five independent parameters, a direct method needs additional constraints to solve the parameters. This paper proposes a new approach that facilitates a very fast but stable and accurate solution from five point correspondences between two overlapping aerial images taken form unmanned aerial vehicle (UAV) flight. Furthermore, if 3D coordinates of perspective centers are available form geotagged images, rotational elements of the RO parameters can be quickly solved using three correspondences only. So it is very reliable for a provision of closed-form solutions for the rigorous methods. Our formulation regards Thompson’s parameterizations of Euler angles in composing a coplanarity condition. Nonlinear terms are subsequently added into a stereo parallax within a constant term under a linear least squares criteria. This strategy is considered new as compared with the known literatures since the proposed approach can find optimal solution. Results from real datasets confirm that our method produces a fast, stable and reliable linear solution by using at least five correspondences or even only three conjugate points of geotagged image pairs

Routing algorithm for the ground team in transmission line inspection using unmanned aerial vehicle

With the rapid development of robotics technology, robots are increasingly used to conduct various tasks by utility companies. An unmanned aerial vehicle (UAV) is an efficient robot that can be used to inspect high-voltage transmission lines. UAVs need to stay within a data transmission range from the ground station and periodically land to replace the battery in order to ensure that the power system can support its operation. A routing algorithm must be used in order to guide the motion and deployment of the ground station while using UAV in transmission line inspection. Most existing routing algorithms are dedicated to pathfinding for a single object that needs to travel from a given start point to end point and cannot be directly used for guiding the ground station deployment and motion since multiple objects (i.e., the UAV and the ground team) whose motions and locations need to be coordinated are involved. In this thesis, we intend to explore the routing algorithm that can be used by utility companies to effectively utilize UAVs in transmission line inspection. Both heuristic and analytical algorithms are proposed to guide the deployment of the ground station and the landing point for UAV power system change. A case study was conducted to validate the effectiveness of the proposed routing algorithm and examine the performance and cost-effectiveness --Abstract, page iii

Reproducibility and Replicability in Unmanned Aircraft Systems and Geographic Information Science

Multiple scientific disciplines face a so-called crisis of reproducibility and replicability (R&R) in which the validity of methodologies is questioned due to an inability to confirm experimental results. Trust in information technology (IT)-intensive workflows within geographic information science (GIScience), remote sensing, and photogrammetry depends on solutions to R&R challenges affecting multiple computationally driven disciplines. To date, there have only been very limited efforts to overcome R&R-related issues in remote sensing workflows in general, let alone those tied to disruptive technologies such as unmanned aircraft systems (UAS) and machine learning (ML). To accelerate an understanding of this crisis, a review was conducted to identify the issues preventing R&R in GIScience. Key barriers included: (1) awareness of time and resource requirements, (2) accessibility of provenance, metadata, and version control, (3) conceptualization of geographic problems, and (4) geographic variability between study areas. As a case study, a replication of a GIScience workflow utilizing Yolov3 algorithms to identify objects in UAS imagery was attempted. Despite the ability to access source data and workflow steps, it was discovered that the lack of accessibility to provenance and metadata of each small step of the work prohibited the ability to successfully replicate the work. Finally, a novel method for provenance generation was proposed to address these issues. It was found that artificial intelligence (AI) could be used to quickly create robust provenance records for workflows that do not exceed time and resource constraints and provide the information needed to replicate work. Such information can bolster trust in scientific results and provide access to cutting edge technology that can improve everyday life

Reproducibility and Replicability in Unmanned Aircraft Systems and Geographic Information Science

Multiple scientific disciplines face a so-called crisis of reproducibility and replicability (R&R) in which the validity of methodologies is questioned due to an inability to confirm experimental results. Trust in information technology (IT)-intensive workflows within geographic information science (GIScience), remote sensing, and photogrammetry depends on solutions to R&R challenges affecting multiple computationally driven disciplines. To date, there have only been very limited efforts to overcome R&R-related issues in remote sensing workflows in general, let alone those tied to disruptive technologies such as unmanned aircraft systems (UAS) and machine learning (ML). To accelerate an understanding of this crisis, a review was conducted to identify the issues preventing R&R in GIScience. Key barriers included: (1) awareness of time and resource requirements, (2) accessibility of provenance, metadata, and version control, (3) conceptualization of geographic problems, and (4) geographic variability between study areas. As a case study, a replication of a GIScience workflow utilizing Yolov3 algorithms to identify objects in UAS imagery was attempted. Despite the ability to access source data and workflow steps, it was discovered that the lack of accessibility to provenance and metadata of each small step of the work prohibited the ability to successfully replicate the work. Finally, a novel method for provenance generation was proposed to address these issues. It was found that artificial intelligence (AI) could be used to quickly create robust provenance records for workflows that do not exceed time and resource constraints and provide the information needed to replicate work. Such information can bolster trust in scientific results and provide access to cutting edge technology that can improve everyday life

Hacia una monitorización en continua basada en teledetección multi-temporal de infraestructuras para el transporte de energía

Este proyecto propone un acercamiento a la monitorización de zonas de interés mediante el análisis de series temporales. En concreto, se ha seleccionado un conjunto de cables de alta tensión del área de Donostia con el fin de mostrar y advertir de las lı́neas eléctricas que se encuentran en áreas boscosas o de alta vegetación. Para ello, se utilizan tecnologı́as como Google Earth Engine y QuantumGIS que son combinadas para gestionar los datos que se adquieren de ambas fuentes. También se ha realizado un estudio previo de las imágenes Sentinel-2, los diferentes tipos de nivel y el preproceso de esta clase de imágenes ası́ como otros conceptos básicos utilizados en teledetección. Finalmente, se han analizado algunos de los ı́ndices de vegetación más populares y se han utilizado dos de ellos para realizar la experimentación ası́ como el análisis comparativo de los resultados obtenidos con cada uno de los ı́ndices