???????????? ??????????????? ????????? ???????????? ?????? ??????

Department of Mehcanical EngineeringUnmanned aerial vehicles (UAVs) are widely used in various areas such as exploration, transportation and rescue activity due to light weight, low cost, high mobility and intelligence. This intelligent system consists of highly integrated and embedded systems along with a microprocessor to perform specific task by computing algorithm or processing data. In particular, image processing is one of main core technologies to handle important tasks such as target tracking, positioning, visual servoing using visual system. However, it often requires heavy amount of computation burden and an additional micro PC controller with a flight computer should be additionally used to process image data. However, performance of the controller is not so good enough due to limited power, size, and weight. Therefore, efficient image processing techniques are needed considering computing load and hardware resources for real time operation on embedded systems. The objective of the thesis research is to develop an efficient image processing framework on embedded systems utilizing neural network and various optimized computation techniques to satisfy both efficient computing speed versus resource usage and accuracy. Image processing techniques has been proposed and tested for management computing resources and operating high performance missions in embedded systems. Graphic processing units (GPUs) available in the market can be used for parallel computing to accelerate computing speed. Multiple cores within central processing units (CPUs) are used like multi-threading during data uploading and downloading between the CPU and the GPU. In order to minimize computing load, several methods have been proposed. The first method is visualization of convolutional neural network (CNN) that can perform both localization and detection simultaneously. The second is region proposal for input area of CNN through simple image processing, which helps algorithm to avoid full frame processing. Finally, surplus computing resources can be saved by control the transient performance such as the FPS limitation. These optimization methods have been experimentally applied to a ground vehicle and quadrotor UAVs and verified that the developed methods offer an optimization to process in embedded environment by saving CPU and memory resources. In addition, they can support to perform various tasks such as object detection and path planning, obstacle avoidance. Through optimization and algorithms, they reveal a number of improvements for the embedded system compared to the existing. Considering the characteristics of the system to transplant the various useful algorithms to the embedded system, the method developed in the research can be further applied to various practical applications.ope

Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection

© 2017 In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments

A Systematic Literature Survey of Unmanned Aerial Vehicle Based Structural Health Monitoring

Unmanned Aerial Vehicles (UAVs) are being employed in a multitude of civil applications owing to their ease of use, low maintenance, affordability, high-mobility, and ability to hover. UAVs are being utilized for real-time monitoring of road traffic, providing wireless coverage, remote sensing, search and rescue operations, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. They are the next big revolution in technology and civil infrastructure, and it is expected to dominate more than $45 billion market value. The thesis surveys the UAV assisted Structural Health Monitoring or SHM literature over the last decade and categorize UAVs based on their aerodynamics, payload, design of build, and its applications. Further, the thesis presents the payload product line to facilitate the SHM tasks, details the different applications of UAVs exploited in the last decade to support civil structures, and discusses the critical challenges faced in UASHM applications across various domains. Finally, the thesis presents two artificial neural network-based structural damage detection models and conducts a detailed performance evaluation on multiple platforms like edge computing and cloud computing

An Image-Based Real-Time Georeferencing Scheme for a UAV Based on a New Angular Parametrization

Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV.Peer reviewe

Development of High-Precision 3D Measurement On Agriculture Using Multiple UAVs

Imaging system for high-precision 3D map on agriculture using UAVs was developed. The system was based on safe and easy UAVs with a ground station application which designed to be the interface between a human operator and the UAVs to carry out mission planning, flight command activation, and real-time flight monitoring. Based on the navigation data, and the way-points generated by the ground station, the UAVs could be automatically navigated to the desired waypoints and hover around each waypoint to collect field image data. By taking only low-resolution image, the proposed system is able to reduce the payload and increase the flight time of the UAVs. The input images then transform into higher-resolution image using reference images, taken by field server or ground-based device, via super-resolution techniques which is able to reduce blurring, blocking, and ringing artifacts especially in edge areas. Finally, we construct high-precision 3D map which proven having error of a millimeter order of magnitude. Our experiment result show that the input low-resolution can be transform into high-resolution image and effective to construct high-precision 3D map. The result indicate that the proposed system provides a reliable method of sensing agricultural field with high-precision 3D map

Vision Based Collaborative Localization and Path Planning for Micro Aerial Vehicles

Autonomous micro aerial vehicles (MAV) have gained immense popularity in both the commercial and research worlds over the last few years. Due to their small size and agility, MAVs are considered to have great potential for civil and industrial tasks such as photography, search and rescue, exploration, inspection and surveillance. Autonomy on MAVs usually involves solving the major problems of localization and path planning. While GPS is a popular choice for localization for many MAV platforms today, it suffers from issues such as inaccurate estimation around large structures, and complete unavailability in remote areas/indoor scenarios. From the alternative sensing mechanisms, cameras arise as an attractive choice to be an onboard sensor due to the richness of information captured, along with small size and inexpensiveness. Another consideration that comes into picture for micro aerial vehicles is the fact that these small platforms suffer from inability to fly for long amounts of time or carry heavy payload, scenarios that can be solved by allocating a group, or a swarm of MAVs to perform a task than just one. Collaboration between multiple vehicles allows for better accuracy of estimation, task distribution and mission efficiency. Combining these rationales, this dissertation presents collaborative vision based localization and path planning frameworks. Although these were created as two separate steps, the ideal application would contain both of them as a loosely coupled localization and planning algorithm. A forward-facing monocular camera onboard each MAV is considered as the sole sensor for computing pose estimates. With this minimal setup, this dissertation first investigates methods to perform feature-based localization, with the possibility of fusing two types of localization data: one that is computed onboard each MAV, and the other that comes from relative measurements between the vehicles. Feature based methods were preferred over direct methods for vision because of the relative ease with which tangible data packets can be transferred between vehicles, and because feature data allows for minimal data transfer compared to large images. Inspired by techniques from multiple view geometry and structure from motion, this localization algorithm presents a decentralized full 6-degree of freedom pose estimation method complete with a consistent fusion methodology to obtain robust estimates only at discrete instants, thus not requiring constant communication between vehicles. This method was validated on image data obtained from high fidelity simulations as well as real life MAV tests. These vision based collaborative constraints were also applied to the problem of path planning with a focus on performing uncertainty-aware planning, where the algorithm is responsible for generating not only a valid, collision-free path, but also making sure that this path allows for successful localization throughout. As joint multi-robot planning can be a computationally intractable problem, planning was divided into two steps from a vision-aware perspective. As the first step for improving localization performance is having access to a better map of features, a next-best-multi-view algorithm was developed which can compute the best viewpoints for multiple vehicles that can improve an existing sparse reconstruction. This algorithm contains a cost function containing vision-based heuristics that determines the quality of expected images from any set of viewpoints; which is minimized through an efficient evolutionary strategy known as Covariance Matrix Adaption (CMA-ES) that can handle very high dimensional sample spaces. In the second step, a sampling based planner called Vision-Aware RRT* (VA-RRT*) was developed which includes similar vision heuristics in an information gain based framework in order to drive individual vehicles towards areas that can benefit feature tracking and thus localization. Both steps of the planning framework were tested and validated using results from simulation

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Real-time vehicle speed estimation using Unmanned Aerial Vehicles for traffic surveillance

Drones are an emerging tool for traffic surveillance; however, they inherently lack the capability to solely obtain vehicle speed on the road. This Bachelor's thesis presents the design, implementation and study of a system to detect the position, velocity and type of vehicles using the video stream obtained from drones. The solution is created to be used with any kind of aerial vehicle but is tailored for the drones in the European project LABYRINTH, of which the thesis has been a part. The tool utilizes the video feed from a sole camera and the telemetry data from the drone to detect, track and project the objects present on the road from the image into reality. This allows for an estimation of their position and speed. The detection and tracking algorithm implemented is the Simple Online Real Time algorithm, which is often referred to as SORT. Once the position has been acquired, another stream is generated that displays the same video, but with the bounding boxes, velocity and confidence ratings of all identified vehicles, with an overall computing time lower than the frame rate. After implementation, the tool underwent testing in a simulated environment to determine its assets and shortcomings, and was used during the LABYRINTH traffic monitoring flight tests. The Bachelor's thesis achieves the aimed objectives with minimum resource utilization, using readily available logic and open-source software to strike an optimal balance between real-time functionality and precise detection of vehicle position.Outgoin

Adaptive large neighborhood search algorithm – performance evaluation under parallel schemes & applications

Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a master-slave architecture to manage and assign loads in processors of the local computers. The overall goal is to simultaneously explore different areas of the search space in an attempt to escape the local minima, taking effective steps toward the optimal solution and, to the end, accelerating the convergence of the ALNS algorithm. The performance of the schemes is tested by solving a capacitated vehicle routing problem (CVRP) with available wellknown test instances. Our computational results indicate that all the parallel schemes are capable of providing a competitive optimality gap in solving CVRP within our investigated test instances. However, the parallel scheme (scheme 1), which runs the ALNS algorithm independently within different slave processors (e.g., without sharing any information with other slave processors) until the synchronization occurs only when one of the processors meets its predefined termination criteria and reports the solution to the master processor, provides the best running time with solving the instances approximately 10.5 times faster than the basic/sequential ALNS algorithm. These findings are applied in a real-life fulfillment process using mixed-mode delivery with trucks and drones. Complex but optimized routes are generated in a short time that is applicable to perform last-mile delivery to customers