Using the Mavic 2 Pro drone for basic water quality assessment

This paper assessed the capability of the Dà-Jiāng Innovations (DJI) Mavic 2 Pro Drone (unmanned aerial vehicle – UAV) for the collection and delivery of river water samples for basic water quality assessments. The primary objective of this paper was to evaluate how this UAV model could help in generating large water quality data sets in the developing world to assist in the design and implementation of water quality monitoring and assessment programs, which are often a challenge due to data paucity and resources. We hypothesized that the traditional approach (portable hand meters) to measuring in-situ water parameters, including pH, dissolved oxygen, electrical conductivity, and turbidity could not yield significant water quality data variations from those collected by the Mavic 2 Pro. The UAV was equipped with a plastic bottle attached to a three-meter rigid thin line for sample collection. Samples were collected at stations 50 m apart over a 300 m river length. The drone captured samples in wind conditions of about 10.1 km/h with ease. About 350 mL of samples were collected per mission. A paired t-test was performed to determine the parameter differences between the two approaches. We conclude that, given similar environmental, physical conditions and pilot experience, Mavic 2 Pro can generate large and much more reliable datasets at faster rates than the traditional approach. The drone also avoided obstacles with ease, a perfect technology for use in rural rivers. However, pilot efficiency and precision, including agitation during flight require further investigations considering their potential parameter influences. Similar future tests should investigate the performance of this drone model and data reliability over a long river course to ascertain its capability and suitability in various conditions in ecological applications.The Carnegie Foundation of New York through Future Africa of the University of Pretoria (UP) under the Early Career Research Leader Fellowship (ECRLF) program.http://www.elsevier.com/locate/sciafhj2022Future Afric

Flying Animal Inspired Behavior-Based Gap-Aiming Autonomous Flight with a Small Unmanned Rotorcraft in a Restricted Maneuverability Environment

This dissertation research shows a small unmanned rotorcraft system with onboard processing and a vision sensor can produce autonomous, collision-free flight in a restricted maneuverability environment with no a priori knowledge by using a gap-aiming behavior inspired by flying animals. Current approaches to autonomous flight with small unmanned aerial systems (SUAS) concentrate on detecting and explicitly avoiding obstacles. In contrast, biology indicates that birds, bats, and insects do the opposite; they react to open spaces, or gaps in the environment, with a gap_aiming behavior. Using flying animals as inspiration a behavior-based robotics approach is taken to implement and test their observed gap-aiming behavior in three dimensions. Because biological studies were unclear whether the flying animals were reacting to the largest gap perceived, the closest gap perceived, or all of the gaps three approaches for the perceptual schema were explored in simulation: detect_closest_gap, detect_largest_gap, and detect_all_gaps. The result of these simulations was used in a proof-of-concept implementation on a 3DRobotics Solo quadrotor platform in an environment designed to represent the navigational diffi- culties found inside a restricted maneuverability environment. The motor schema is implemented with an artificial potential field to produce the action of aiming to the center of the gap. Through two sets of field trials totaling fifteen flights conducted with a small unmanned quadrotor, the gap-aiming behavior observed in flying animals is shown to produce repeatable autonomous, collision-free flight in a restricted maneuverability environment. Additionally, using the distance from the starting location to perceived gaps, the horizontal and vertical distance traveled, and the distance from the center of the gap during traversal the implementation of the gap selection approach performs as intended, the three-dimensional movement produced by the motor schema and the accuracy of the motor schema are shown, respectively. This gap-aiming behavior provides the robotics community with the first known implementation of autonomous, collision-free flight on a small unmanned quadrotor without explicit obstacle detection and avoidance as seen with current implementations. Additionally, the testing environment described by quantitative metrics provides a benchmark for autonomous SUAS flight testing in confined environments. Finally, the success of the autonomous collision-free flight implementation on a small unmanned rotorcraft and field tested in a restricted maneuverability environment could have important societal impact in both the public and private sectors

Autonomous Aerial Water Sampling

Obtaining spatially separated, high frequency water samples from rivers and lakes is critical to enhance our understanding and effective management of fresh water resources. In this thesis we present an aerial water sampler and verify the system in field experiments. The aerial water sampler has the potential to vastly increase the speed and range at which scientists obtain water samples while reducing cost and effort. The water sampling system includes: 1) a mechanism to capture three 20 ml samples per mission; 2) sensors and algorithms for safe navigation and altitude approximation over water; and 3) software components that integrate and analyze sensor data, control the vehicle, and drive the sampling mechanism. In this thesis we validate the system in the lab, characterize key sensors, and present results of outdoor experiments. We compare water samples from local lakes obtained by our system to samples obtained by traditional sampling techniques. We find that nearly all water properties are consistent between the two techniques. These experiments show that despite the challenges associated with flying precisely over water, it is possible to quickly obtain water samples with an Unmanned Aerial Vehicle (UAV). Advisers: Carrick Detweiler and Matthew B. Dwye

Autonomous Aerial Water Sampling

Obtaining spatially separated, high frequency water samples from rivers and lakes is critical to enhance our understanding and effective management of fresh water resources. In this thesis we present an aerial water sampler and verify the system in field experiments. The aerial water sampler has the potential to vastly increase the speed and range at which scientists obtain water samples while reducing cost and effort. The water sampling system includes: 1) a mechanism to capture three 20 ml samples per mission; 2) sensors and algorithms for safe navigation and altitude approximation over water; and 3) software components that integrate and analyze sensor data, control the vehicle, and drive the sampling mechanism. In this thesis we validate the system in the lab, characterize key sensors, and present results of outdoor experiments. We compare water samples from local lakes obtained by our system to samples obtained by traditional sampling techniques. We find that nearly all water properties are consistent between the two techniques. These experiments show that despite the challenges associated with flying precisely over water, it is possible to quickly obtain water samples with an Unmanned Aerial Vehicle (UAV). Advisers: Carrick Detweiler and Matthew B. Dwye

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

Biologically Inspired Guidance for Autonomous Systems

Animals and humans can perform purposeful actions using only their senses. Birds can perch on branches; bats use echolocation to hunt prey and humans are able to control vehicles. It must therefore be possible for autonomous systems to replicate this autonomous behaviour if an understanding of how animals and humans perceive their environment and guide their movements is obtained. Tau theory offers a potential explanation as to how this is achieved in nature. Tau theory posits, that in combination with the so-called ‘motion guides’, animals and humans perform useful movements by closing action-gaps, i.e. gaps between the current state and a desired state. The theory suggests that the variabl

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Optic Flow for Obstacle Avoidance and Navigation: A Practical Approach

This thesis offers contributions and innovations to the development of vision-based autonomous flight control systems for small unmanned aerial vehicles operating in cluttered urban environments. Although many optic flow algorithms have been reported, almost none have addressed the critical issue of accuracy and reliability over a wide dynamic range of optic flow. My aim is to rigorously develop improved optic flow sensing to meet realistic mission requirements for autonomous navigation and collision avoidance. A review of related work enabled development of a new hybrid optic flow algorithm concept combining the best properties of image correlation and interpolation with additional innovations to enhance accuracy, computational speed and reliability. Key analytical work yielded a methodology for determining optic flow dynamic range requirements from system and sensor design parameters and a technique enabling a video sensor to operate as a passive ranging system for closed loop flight control. Detailed testing led to development of the hybrid image interpolation algorithm (HI2A) using improved correlation search strategies, sparse images to reduce processing loads, a solution tracking loop to bypass the more intensive initial estimation process, a frame look-back method to improve accuracy at low optic flow, a modified interpolation technique to improve robustness and an extensive error checking system for validating outputs. A realistic simulation system was developed incorporating independent, precision ground truthing to assess algorithm accuracy. Comparison testing of the HI2A against the commonly-used Lucas Kanade algorithm demonstrates major improvement in accuracy over greatly expanded dynamic range. A reactive flight controller using ranging data from a monocular, forward looking video sensor and rules-based logic was developed and tested in Monte Carlo simulations of a hundred flights. At higher flight speeds than reported in similar tests, collision-free results were obtained in a realistic urban canyon environment. The HI2A algorithm and flight controller software performance on a common PC was up to eight times faster than real-time for outputs of 250 measurements at 50 Hz. The feasibility of terrain mapping in real-time was demonstrated using 3D ranging data from optic flow in an overflight of the urban simulation environment indicating the potential for its use in path planning approaches to navigation and collision avoidance

An investigation of change in drone practices in broadacre farming environments

The application of drones in broadacre farming is influenced by novel and emergent factors. Drone technology is subject to legal, financial, social, and technical constraints that affect the Agri-tech sector. This research showed that emerging improvements to drone technology influence the analysis of precision data resulting in disparate and asymmetrically flawed Ag-tech outputs. The novelty of this thesis is that it examines the changes in drone technology through the lens of entropic decay. It considers the planning and controlling of an organisation’s resources to minimise harmful effects through systems change. The rapid advances in drone technology have outpaced the systematic approaches that precision agriculture insists is the backbone of reliable ongoing decision-making. Different models and brands take data from different heights, at different times of the day, and with flight of differing velocities. Drone data is in a state of decay, no longer equally comparable to past years’ harvest and crop data and are now mixed into a blended environment of brand-specific variations in height, image resolution, air speed, and optics. This thesis investigates the problem of the rapid emergence of image-capture technology in drones and the corresponding shift away from the established measurements and comparisons used in precision agriculture. New capabilities are applied in an ad hoc manner as different features are rushed to market. At the same time existing practices are subtly changed to suit individual technology capability. The result is a loose collection of technically superior drone imagery, with a corresponding mismatch of year-to-year agricultural data. The challenge is to understand and identify the difference between uniformly accepted technological advance, and market-driven changes that demonstrate entropic decay. The goal of this research is to identify best practice approaches for UAV deployment for broadacre farming. This study investigated the benefits of a range of characteristics to optimise data collection technologies. It identified widespread discrepancies demonstrating broadening decay on precision agriculture and productivity. The pace of drone development is so rapidly different from mainstream agricultural practices that the once reliable reliance upon yearly crop data no longer shares statistically comparable metrics. Whilst farmers have relied upon decades of satellite data that has used the same optics, time of day and flight paths for many years, the innovations that drive increasingly smarter drone technologies are also highly problematic since they render each successive past year’s crop metrics as outdated in terms of sophistication, detail, and accuracy. In five years, the standardised height for recording crop data has changed four times. New innovations, coupled with new rules and regulations have altered the once reliable practice of recording crop data. In addition, the cost of entry in adopting new drone technology is sufficiently varied that agriculturalists are acquiring multiple versions of different drone UAVs with variable camera and sensor settings, and vastly different approaches in terms of flight records, data management, and recorded indices. Without addressing this problem, the true benefits of optimization through machine learning are prevented from improving harvest outcomes for broadacre farming. The key findings of this research reveal a complex, constantly morphing environment that is seeking to build digital trust and reliability in an evolving global market in the face of rapidly changing technology, regulations, standards, networks, and knowledge. The once reliable discipline of precision agriculture is now a fractured melting pot of “first to market” innovations and highly competitive sellers. The future of drone technology is destined for further uncertainty as it struggles to establish a level of maturity that can return broadacre farming to consistent global outcomes