Automated aerial inspection guidance with improved turn planning

Aerial inspection of pipelines, powerlines, and other large linear infrastructure networks has emerged in a number of civilian remote sensing applications. Challenges relate to automating inspection flight for under-actuated aircraft with LiDAR/camera sensor constraints whilst subjected to wind disturbances. This paper presents new improved turn planning strategies with guidance suitable for automation of linear infrastructure inspection able to reduce inspection flight distance by including wind information. Simulation and experimental flight tests confirmed the flight distance saving, and the proposed guidance strategies exhibited good tracking performance in a range of wind conditions.

Guidance of aircraft in periodic inspection tasks

This paper presents a guidance approach for aircraft in periodic inspection tasks. The periodic inspection task involves flying to a series of desired fixed points of inspection with specified attitude requirements so that requirements for downward looking sensors, such as cameras, are achieved. We present a solution using a precision guidance law and a bank turn dynamics model. High fidelity simulation studies illustrate the effectiveness of this approach under both ideal (nil-wind) and non-ideal (wind) conditions.

Hybrid game evolutionary algorithm for mission path planning of aerial survey tasks

The aim of this paper is to implement a Game-Theory based offline mission path planner for aerial inspection tasks of large linear infrastructures. Like most real-world optimisation problems, mission path planning involves a number of objectives which ideally should be minimised simultaneously. The goal of this work is then to develop a Multi-Objective (MO) optimisation tool able to provide a set of optimal solutions for the inspection task, given the environment data, the mission requirements and the definition of the objectives to minimise. Results indicate the robustness and capability of the method to find the trade-off between the Pareto-optimal solutions.

Vertical infrastructure inspection using a quadcopter and shared autonomy control

This paper presents a shared autonomy control scheme for a quadcopter that is suited for inspection of vertical infrastructure — tall man-made structures such as streetlights, electricity poles or the exterior surfaces of buildings. Current approaches to inspection of such structures is slow, expensive, and potentially hazardous. Low-cost aerial platforms with an ability to hover now have sufficient payload and endurance for this kind of task, but require significant human skill to fly. We develop a control architecture that enables synergy between the ground-based operator and the aerial inspection robot. An unskilled operator is assisted by onboard sensing and partial autonomy to safely fly the robot in close proximity to the structure. The operator uses their domain knowledge and problem solving skills to guide the robot in difficult to reach locations to inspect and assess the condition of the infrastructure. The operator commands the robot in a local task coordinate frame with limited degrees of freedom (DOF). For instance: up/down, left/right, toward/away with respect to the infrastructure. We therefore avoid problems of global mapping and navigation while providing an intuitive interface to the operator. We describe algorithms for pole detection, robot velocity estimation with respect to the pole, and position estimation in 3D space as well as the control algorithms and overall system architecture. We present initial results of shared autonomy of a quadrotor with respect to a vertical pole and robot performance is evaluated by comparing with motion capture data.

Control of infrastructure inspection aircraft vertical dynamics in the presence of thermal disturbances

The low-altitude aircraft inspection of powerlines, or other linear infrastructure networks, is emerging as an important application requiring specialised control technologies. Despite some recent advances in automated control related to this application, control of the underactuated aircraft vertical dynamics has not been completely achieved, especially in the presence of thermal disturbances. Rejection of thermal disturbances represents a key challenge to the control of inspection aircraft due to the underactuated nature of the dynamics and specified speed, altitude, and pitch constraints. This paper proposes a new vertical controller consisting of a backstepping elevator controller with feedforward-feedback throttle controller. The performance of our proposed approach is evaluated against two existing candidate controllers.

Mission path planning of aerial survey tasks using hybrid game evolutionary algorithm

The work presented in this report is aimed to implement a cost-effective offline mission path planner for aerial inspection tasks of large linear infrastructures. Like most real-world optimisation problems, mission path planning involves a number of objectives which ideally should be minimised simultaneously. Understandably, the objectives of a practical optimisation problem are conflicting each other and the minimisation of one of them necessarily implies the impossibility to minimise the other ones. This leads to the need to find a set of optimal solutions for the problem; once such a set of available options is produced, the mission planning problem is reduced to a decision making problem for the mission specialists, who will choose the solution which best fit the requirements of the mission. The goal of this work is then to develop a Multi-Objective optimisation tool able to provide the mission specialists a set of optimal solutions for the inspection task amongst which the final trajectory will be chosen, given the environment data, the mission requirements and the definition of the objectives to minimise. All the possible optimal solutions of a Multi-Objective optimisation problem are said to form the Pareto-optimal front of the problem. For any of the Pareto-optimal solutions, it is impossible to improve one objective without worsening at least another one. Amongst a set of Pareto-optimal solutions, no solution is absolutely better than another and the final choice must be a trade-off of the objectives of the problem. Multi-Objective Evolutionary Algorithms (MOEAs) are recognised to be a convenient method for exploring the Pareto-optimal front of Multi-Objective optimization problems. Their efficiency is due to their parallelism architecture which allows to find several optimal solutions at each time

Visual guidance for fixed-wing unmanned aerial vehicles using feature tracking : application to power line inspection

This thesis presents novel vision based control solutions that enable fixed-wing Unmanned Aerial Vehicles to perform tasks of inspection over infrastructure including power lines, pipe lines and roads. This is achieved through the development of techniques that combine visual servoing with alternate manoeuvres that assist the UAV in both following and observing the feature from a downward facing camera. Control designs are developed through techniques of Image Based Visual Servoing to utilise sideslip through Skid-to-Turn and Forward-Slip manoeuvres. This allows the UAV to simultaneously track and collect data over the length of infrastructure, including straight segments and the transition where these meet.

A Novel Approach to Haptic Tele-operation of Aerial Robot Vehicles

We present a novel, simple and effective approach for tele-operation of aerial robotic vehicles with haptic feedback. Such feedback provides the remote pilot with an intuitive feel of the robot’s state and perceived local environment that will ensure simple and safe operation in cluttered 3D environments common in inspection and surveillance tasks. Our approach is based on energetic considerations and uses the concepts of network theory and port-Hamiltonian systems. We provide a general framework for addressing problems such as mapping the limited stroke of a ‘master’ joystick to the infinite stroke of a ‘slave’ vehicle, while preserving passivity of the closed-loop system in the face of potential time delays in communications links and limited sensor data

Development of an autonomous helicopter for aerial powerline inspections

Autonomous mini-helicopters have been seen as a viable option for aerial-based powerline inspections, however there are numerous research and engineering challenges in developing a system capable of achieving this task in a dependable manner. We have developed an autonomous helicopter as a research platform which will allow us to demonstrate proof-of-concept capabilities for powerline inspections. Through numerous development cycles and from flight test experience we have gained insights into the key challenges in this area. We discuss these insights, describe the helicopter platform and present our research progress in the area of obstacle avoidance for mini-helicopters.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Outdoor flight testing of a pole inspection UAV incorporating high-speed vision

We present a pole inspection system for outdoor environments comprising a high-speed camera on a vertical take-off and landing (VTOL) aerial platform. The pole inspection task requires a vehicle to fly close to a structure while maintaining a fixed stand-off distance from it. Typical GPS errors make GPS-based navigation unsuitable for this task however. When flying outdoors a vehicle is also affected by aerodynamics disturbances such as wind gusts, so the onboard controller must be robust to these disturbances in order to maintain the stand-off distance. Two problems must therefor be addressed: fast and accurate state estimation without GPS, and the design of a robust controller. We resolve these problems by a) performing visual + inertial relative state estimation and b) using a robust line tracker and a nested controller design. Our state estimation exploits high-speed camera images (100Hz) and 70Hz IMU data fused in an Extended Kalman Filter (EKF). We demonstrate results from outdoor experiments for pole-relative hovering, and pole circumnavigation where the operator provides only yaw commands. Lastly, we show results for image-based 3D reconstruction and texture mapping of a pole to demonstrate the usefulness for inspection tasks.

Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy

We present an approach for the inspection of vertical pole-like infrastructure using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structures, such as light and power distribution poles, is a time consuming, dangerous and expensive task with high operator workload. To address these issues, we propose a VTOL platform that can operate at close-quarters, whilst maintaining a safe stand-off distance and rejecting environmental disturbances. We adopt an Image based Visual Servoing (IBVS) technique using only two line features to stabilise the vehicle with respect to a pole. Visual, inertial and sonar data are used, making the approach suitable for indoor or GPS-denied environments. Results from simulation and outdoor flight experiments demonstrate the system is able to successfully inspect and circumnavigate a pole.

Modeling and control of a robotic power line inspection vehicle

Power line inspection is a vital function for electricity supply companies but it involves labor-intensive and expensive procedures which are tedious and error-prone for humans to perform. A possible solution is to use an unmanned aerial vehicle (UAV) equipped with video surveillance equipment to perform the inspection. This paper considers how a small, electrically driven rotorcraft conceived for this application could be controlled by visually tracking the overhead supply lines. A dynamic model for a ducted-fan rotorcraft is presented and used to control the action of an Air Vehicle Simulator (AVS), consisting of a cable-array robot. Results show how visual data can be used to determine, and hence regulate in closed loop, the simulated vehicle’s position relative to the overhead lines.

A robot application to marine vessel inspection

Seagoing vessels have to undergo regular inspections, which are currently performed manually by ship surveyors. The main cost factor in a ship inspection is to provide access to the different areas of the ship, since the surveyor has to be close to the inspected parts, usually within arm's reach, either to perform a visual analysis or to take thickness measurements. The access to the structural elements in cargo holds, e.g., bulkheads, is normally provided by staging or by 'cherry-picking' cranes. To make ship inspections safer and more cost-efficient, we have introduced new inspection methods, tools, and systems, which have been evaluated in field trials, particularly focusing on cargo holds. More precisely, two magnetic climbing robots and a micro-aerial vehicle, which are able to assist the surveyor during the inspection, are introduced. Since localization of inspection data is mandatory for the surveyor, we also introduce an external localization system that has been verified in field trials, using a climbing inspection robot. Furthermore, the inspection data collected by the robotic systems are organized and handled by a spatial content management system that enables us to compare the inspection data of one survey with those from another, as well as to document the ship inspection when the robot team is used. Image-based defect detection is addressed by proposing an integrated solution for detecting corrosion and cracks. The systems' performance is reported, as well as conclusions on their usability, all in accordance with the output of field trials performed onboard two different vessels under real inspection conditions.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.