Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166282/1/itr2bf00873.pd

Virtual environment UAV swarm management using GPU calculated digital pheromones

Our future military force will be complex: a highly integrated mix of manned and unmanned units. These unmanned units could function individually or within a swarm. The readiness of future warfighters to work alongside and utilize these new forces depends on the creation of usable interfaces and training simulators. The difficulty is that current unmanned aerial vehicle (UAV) control interfaces require too much operator attention and common swarm control methods require expensive computational power. This dissertation discusses how to improve upon current user interfaces and how to improve the performance of a common swarm control method, the digital pheromone field. This method uses digital pheromones to bias the movements of individual units within a swarm toward areas that are attractive and away from areas that are dangerous or unattractive. A more efficient method for performing pheromone field calculations is introduced, one that harnesses the power of the GPU (graphics processing unit) in today\u27s graphics cards by reshaping the ADAPTIV swarm control algorithm into a form acceptable to the GPU\u27s pipeline. The GPU ADAPTIV implementation is tested in scenarios that involve up to 50,000 virtual UAVs. When compared to its counterpart CPU implementation, the GPU version performed over 30 times faster than the CPU version. This gain translates directly into lower costs for training the future warfighter today and fielding the swarms of tomorrow. Finally, this dissertation presents a vision for combining these new interface ideas and performance enhancements into an effective swarm control interface and training simulator

Surveillance Planning against Smart Insurgents in Complex Terrain

This study is concerned with finding a way to solve a surveillance system allocation problem based on the need to consider intelligent insurgency that takes place in a complex geographical environment. Although this effort can be generalized to other situations, it is particularly geared towards protecting military outposts in foreign lands. The technological assets that are assumed available include stare-devices, such as tower-cameras and aerostats, as well as manned and unmanned aerial systems. Since acquiring these assets depends on the ability to control and monitor them on the target terrain, their operations on the geo-location of interest ought to be evaluated. Such an assessment has to also consider the risks associated with the environmental advantages that are accessible to a smart adversary. Failure to consider these aspects might render the forces vulnerable to surprise attacks. The problem of this study is formulated as follows: given a complex terrain and a smart adversary, what types of surveillance systems, and how many entities of each kind, does a military outpost need to adequately monitor its surrounding environment? To answer this question, an analytical framework is developed and structured as a series of problems that are solved in a comprehensive and realistic fashion. This includes digitizing the terrain into a grid of cell objects, identifying high-risk spots, generating flight tours, and assigning the appropriate surveillance system to the right route or area. Optimization tools are employed to empower the framework in enforcing constraints--such as fuel/battery endurance, flying assets at adequate altitudes, and respecting the climbing/diving rate limits of the aerial vehicles--and optimizing certain mission objectives--e.g. revisiting critical regions in a timely manner, minimizing manning requirements, and maximizing sensor-captured image quality. The framework is embedded in a software application that supports a friendly user interface, which includes the visualization of maps, tours, and related statistics. The final product is expected to support designing surveillance plans for remote military outposts and making critical decisions in a more reliable manner

Persistent vision-based search and track using multiple UAVs

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (p. 93-98).Unmanned aerial vehicles (UAVs) have attracted interest for their ability to carry out missions such as border patrol, urban traffic monitoring, persistent surveillance, and search and rescue operations. Most of these missions require the ability to detect and track objects of interest on or near the ground. In addition, most of the missions are inherently long-duration, requiring multiple UAVs to cooperate over time periods longer than the endurance of a single vehicle. This thesis presents a framework to enable such missions to be carried out autonomously and robustly. First, a technique for vision-based target detection and bearing determination that utilizes a video camera onboard each UAV is presented. The technique is designed to detect the presence of targets of interest in the camera video stream and determine the bearing from the UAV to the target even when the video data is noisy. Next, a cooperative, bearings-only target estimation algorithm is presented. The algorithm is shown to provide better estimates of a target's position and velocity in three dimensions than could be achieved by a single vehicle, while being computationally efficient and naturally distributable among multiple UAVs.(cont. )Next, a task assignment algorithm that incorporates closed-loop feedback on the performance of individual UAVs and sensor suites is developed, enabling underperforming UAVs to be dynamically swapped out by the tasking system. Finally, flight results from several persistent, multiple-target search and track experiments conducted on MIT's Real-time indoor Autonomous Vehicle test ENvironment (RAVEN) are presented.by Brett Bethke.S.M

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

Design of autonomous sustainable unmanned aerial vehicle - A novel approach to its dynamic wireless power transfer

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Electric UAVs are presently being used widely in civilian duties such as security, surveillance, and disaster relief. The use of Unmanned Aerial Vehicle (UAV) has increased dramatically over the past years in different areas/fields such as marines, mountains, wild environments. Nowadays, there are many electric UAVs development with fast computational speed and autonomous flying has been a reality by fusing many sensors such as camera tracking sensor, obstacle avoiding sensor, radar sensor, etc. But there is one main problem still not able to overcome which is power requirement for continuous autonomous operation. When the operation needs more power, but batteries can only give for 20 to 30 mins of flight time. These types of system are not reliable for long term civilian operation because we need to recharge or replace batteries by landing the craft every time when we want to continue the operation. The large batteries also take more loads on the UAV which is also not a reliable system. To eliminate these obstacles, there should a recharging wireless power station in ground which can transmit power to these small UAVs wirelessly for long term operation. There will be camera attached in the drone to detect and hover above the Wireless Power Transfer device which got receiving and transmitting station can be use with deep learning and sensor fusion techniques for more reliable flight operations. This thesis explores the use of dynamic wireless power to transfer energy using novel rotating WPT charging technique to the UAV with improved range, endurance, and average speed by giving extra hours in the air. The hypothesis that was created has a broad application beyond UAVs. The drone autonomous charging was mostly done by detecting a rotating WPT receiver connected to main power outlet that served as a recharging platform using deep neural vision capabilities. It was the purpose of the thesis to provide an alternative to traditional self-charging systems that relies purely on static WPT method and requires little distance between the vehicle and receiver. When the UAV camera detect the WPT receiving station, it will try to align and hover using onboard sensors for best power transfer efficiency. Since this strategy relied on traditional automatic drone landing technique, but the target is rotating all the time which needs smart approaches like deep learning and sensor fusion. The simulation environment was created and tested using robot operating system on a Linux operating system using a model of the custom-made drone. Experiments on the charging of the drone confirmed that the intelligent dynamic wireless power transfer (DWPT) method worked successfully while flying on air

VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VEHICLES USING SEGMENTATION-INITIALISED TRACKERS

We propose an effective vehicle tracker and speed estimation method from Unmanned Aerial Vehicles (UAVs) videos that can be deployed on UAV-embedded edge devices. Our tracker uses segmentation-derived vehicle regions to initialise a MOSSE tracker. This enables road operators to make multipurpose use of segmentation outputs while still being able to track the vehicles across frames. The vehicle speed is estimated using flight parameters derived from the UAV's flight computer and the vehicle displacement across frames. We trained CABiNet on the UAVid urban segmentation benchmark dataset and finetuned it on a dataset collected at our study site. A mean Intersection over Union (mIoU) of 0.73 was obtained for the vehicle class. Our segmentation-initialised MOSSE tracker was evaluated on the VisDrone Multi-Object Tracking (MOT) benchmark dataset and compared against traditional methods that utilise object regions for tracker initialisation. Our approach yielded a Multi-Object Tracking Precision (MOTP) of 0.872 compared to 0.830 when using YOLOv4. Our vehicle speed estimations approach was evaluated using a privately collected ground truth vehicle speed dataset. Our approach yielded a Root Mean Square Error (RMSE) between 3.42 and 16.12 km/hr across different flight configurations. Finally, our approach was deployed on an NVIDIA Jetson Xavier NX edge device and could be executed at 8 Frames Per Second (FPS). The results indicate that our approach is a simple yet fast alternative to traditional tracking methods while producing multipurpose segmentation information

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Military Application of Aerial Photogrammetry Mapping Assisted by Small Unmanned Air Vehicles

This research investigated the practical military applications of the photogrammetric methods using remote sensing assisted by small unmanned aerial vehicles (SUAVs). The research explored the feasibility of UAV aerial mapping in terms of the specific military purposes, focusing on the geolocational and measurement accuracy of the digital models, and image processing time. The research method involved experimental flight tests using low-cost Commercial off-the-shelf (COTS) components, sensors and image processing tools to study key features of the method required in military like location accuracy, time estimation, and measurement capability. Based on the results of the data analysis, two military applications are defined to justify the feasibility and utility of the methods. The first application is to assess the damage of an attacked military airfield using photogrammetric digital models. Using a hex-rotor test platform with Sony A6000 camera, georeferenced maps with 1 meter accuracy was produced and with sufficient resolution (about 1 cm/pixel) to identify foreign objects on the runway. The other case examines the utility and quality of the targeting system using geo-spatial data from reconstructed 3-Dimensional (3-D) photogrammetry models. By analyzing 3-D model, operable targeting under 1meter accuracy with only 5 percent error on distance, area, and volume wer

Design Methodology for Unmanned Aerial Vehicle (UAV) Team Coordination

Unmanned Aerial Vehicle (UAV) systems, despite having no onboard human pilots, currently require extensive human involvement to accomplish successful mission operations. Further, successful operations also require extensive colalboration between mission stakeholders, including operators, mission commanders, and information consumers (e.g. ground troops relying on intelligence reports in their area). Existing UAV system interfaces provide little to no support for collaboration between remote operators or for operators to collaborate with information consumers. As reliance on UAVs continues to increase in military and civilian operations, this lack of support for collaboration will likely become a substantial limitation of existing UAV systems. In order to introduce effective collaboration support to UAV system interfaces, it is essential to understand, and be able to derive system design requirements that address, the necessary group interactions that occur in UAV task enviroments. However, few collaborative requirements analysis methods exist, and to our knowledge, no method exists that captures design requirements for collaborative decision making in complex, time-critical environments. This report describes the development of a new design requirements analysis method for deriving information and functional requirements that address the collaboration needs of UAV (and other complex task) operators, and the needs of stakeholders interacting with these operators. More specifically, theis method extends a recently developed requirements analysis method, called the Hybrid Cognitive Task Analysis (CTA) method, which enables the generation of information and functional requirements for futuristic UAV system interfaces. The original Hybrid CTA method focused on deriving single user system interface requirements. This work extends this method by introducing analytic steps to identify task and decision-making dependencies between different UAV operations collaborators. This collaborative extension to the Hybrid CTA utilizes the notion of boundary objects, an analytic construct commonly used in the study of group work. Boundary objects are physical or information artifacts that cross the task boundaries between members of distinct groups. Identifying boundary objects in complex task operations help the analyst to identify task and decision-making dependencies between local and remote collaborators. Understanding these dependencies helps to identify information sharing requirements that the UAV system should support. This report describes the analytic steps of the collaborative extension, and provides background information on the original Hybrid CTA method and the boundary object construct. The report also describes a project in which the new design requirements method was used to revise a proposed set of UAV operator displays.Prepared For Boeing Phantom Work