Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Research on unmanned aerial vehicles (UAV) has been increasingly popular in the past decades, and UAVs have been widely used in industrial inspection, remote sensing for mapping & surveying, rescui...

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2017.1420509

A survey on vision-based UAV navigation

During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.

/pdf/survey-on-computer-vision-for-uavs-current-developments-and-bzv1le1sa1.pdf

Survey on Computer Vision for UAVs: Current Developments and Trends

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.

pdf/survey-of-computer-vision-algorithms-and-applications-for-1tfxhw683x.pdf

Survey of computer vision algorithms and applications for unmanned aerial vehicles

The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. In particular, deep learning techniques for motion control have recently taken a major qualitative step, since the successful application of Deep Q-Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide outstanding results with continuous state and action domains, which are a requirement in most of the robotics-related tasks. In this context, the research community is lacking the integration of realistic simulation systems with the reinforcement learning paradigm, enabling the application of deep reinforcement learning algorithms to the robotics field. In this paper, a versatile Gazebo-based reinforcement learning framework has been designed and validated with a continuous UAV landing task. The UAV landing maneuver on a moving platform has been solved by means of the novel DDPG algorithm, which has been integrated in our reinforcement learning framework. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. As an indirect result, a powerful work flow for robotics has been validated, where robots can learn in simulation and perform properly in real operation environments. To the best of the authors knowledge, this is the first work that addresses the continuous UAV landing maneuver on a moving platform by means of a state-of-the-art deep reinforcement learning algorithm, trained in simulation and tested in real flights.

/pdf/a-deep-reinforcement-learning-strategy-for-uav-autonomous-1sbtcw17g7.pdf

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform

This paper investigates solutions for the fundamental yet challenging problem of autonomous landing of multirotor Unammaned Aerial Vehicles UAVs. In addition to landing on static targets, tracking and landing on a moving platform is addressed, as a solution to facilitate the deployment of the UAV. The paper presents the design of a new landing pad and its relative pose estimation. The fusion of inertial measurement with the estimated pose is considered to ensure a high sampling rate, and to increase the manoeuvrability of the vehicle. Two filters are designed to conduct the fusion, an Extended Kalman Filter (EKF) and an Extended Hź (EHź). The extensive simulation and practical tests permitted identification of the challenges of the landing task. Adequate solutions to these challenges are proposed to lessen their impact on landing precision.

Vision Based Autonomous Landing of Multirotor UAV on Moving Platform

The objective of this work is to achieve real time tracking of power conductors using a Quadrotor Unmanned Air Vehicle (UAV). Two solutions are proposed. For the first solution, the control is directly solved in the 2D image space. The link between image features and the kinematics of the vehicle is achieved using an Image Based Visual Servoing (IBVS) formulation, combined with a Linear Quadratic Servo (LQ-Servo) approach. In the second solution, a partial pose of the vehicle with respect to (wrt) the lines is estimated. Then, a position controller designed in the Cartesian space is used to drive the vehicle to the desired configuration. Experimental results are provided to validate the two approaches.

Visual servoing of a Quadrotor UAV for autonomous power lines inspection

Quadrotors low level control has attracted the attention of many research works over the last decade. In most of previous works, position stabilisation was achieved using nonlinear control techniques. In this paper, two full linear techniques based on optimal control theory are presented. The first, based on the L
 2
 norm, is a Linear Quadratique Servo (LQ-Servo) controller. The second one, optimizes the L
 ∞
 norm and is designed using H
 ∞
 control approach. Emphasis is placed on the robustness to external disturbances and particularly wind gusts. Simulation results based on the full nonlinear model of the quadrotor are presented as a basis of comparison between the two controllers.

Full linear control of a quadrotor UAV, LQ vs H ∞

Purpose




This paper aims to present a new vision-based approach for both the identification and the estimation of the relative distance between the unmanned aerial vehicle (UAV) and power pylon. Autonomous power line inspection using small UAVs, has been the focus of many research works over the past couple of decades. Automatic detection of power pylons is a primary requirement to achieve such autonomous systems. It is still a challenging task due to the complex geometry and cluttered background of these structures.




Design/methodology/approach




The identification solution proposed, avoids the complexity of classic object recognition techniques. Instead of searching the whole image for the pylon template, low-level geometric priors with robust colour attributes are combined to remove the pylon background. The depth estimation, on the other hand, is based on a new concept which exploits the ego-motion of the inspection UAV to estimate its distance from the pylon using just a monocular camera.




Findings




An algorithm is tested on a quadrotor UAV, using different kinds of metallic power pylons. Both simulation and real-world experiments, conducted in different backgrounds and illumination conditions, show very promising results.




Research limitations/implications




In the real tests carried out, the Inertial Navigation System (INS) of the vehicle was used to estimate its ego-motion. A more reliable solution should be considered for longer distances, by either fusing INS and global positioning system data or using visual navigation techniques such as visual odometry.




Originality/value




A simple yet efficient solution is proposed that allows the UAV to reliably identify the pylon, with still a low processing cost. Considering a monocular solution is a major advantage, given the limited payload and processing power of such small vehicles.

Power pylon detection and monocular depth estimation from inspection UAVs

Quadrotor helicopters have been the focus of many research works during the last decade. Thanks to their numerous appealing features, their applications are increasingly reported in the literature. Their main limitation, however, is their lightweight which makes them sensitive to external disturbances. The aim of this paper is to achieve robust trajectory following for a Quadro-UAV, in the presence of wind disturbances. Two nonlinear controllers are designed and discussed, the first controller is based on Feedback Linearisation, while the second is designed using a Backstepping approach. Numerical simulations are provided to support the controllers design and assess their robustness to wind gust disturbances.

Quadrotor control for trajectory tracking in presence of wind disturbances

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