Quality assurance of aerial applications of larvicides for mosquito control : effects of granule and catch tray size on field monitoring programs

Aerial applications of granular insecticides are preferable because they can effectively penetrate vegetation, there is less drift, and no loss of product due to evaporation. We aimed to 1) assess the field efficacy ofVectoBac G to control Aedes vigilax (Skuse) in saltmarsh pools, 2) develop a stochastic-modeling procedure to monitor application quality, and 3) assess the distribution of VectoBac G after an aerial application. Because ground-based studies with Ae. vigilax immatures found that VectoBac G provided effective control below the recommended label rate of 7 kg/ha, we trialed a nominated aerial rate of 5 kg/ha as a case study. Our distribution pattern modeling method indicated that the variability in the number of VectoBac G particles captured in catch-trays was greater than expected for 5 kg/ha and that the widely accepted contour mapping approach to visualize the deposition pattern provided spurious results and therefore was not statistically appropriate. Based on the results of distribution pattern modeling, we calculated the catch tray size required to analyze the distribution of aerially applied granular formulations. The minimum catch tray size for products with large granules was 4 m2 for Altosid pellets and 2 m2 for VectoBac G. In contrast, the minimum catch-tray size for Altosid XRG, Aquabac G, and Altosand, with smaller granule sizes, was 1 m2. Little gain in precision would be made by increasing the catch-tray size further, when the increased workload and infrastructure is considered. Our improved methods for monitoring the distribution pattern of aerially applied granular insecticides can be adapted for use by both public health and agricultural contractors.

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

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Echolocation call production during aerial and terrestrial locomotion by New Zealand's enigmatic lesser short-tailed bat, Mystacina tuberculata

Linkage of echolocation call production with contraction of flight muscles has been suggested to reduce the energetic cost of flight with echolocation, such that the overall cost is approximately equal to that of flight alone. However, the pattern of call production with limb movement in terrestrially agile bats has never been investigated. We used synchronised high-speed video and audio recordings to determine patterns of association between echolocation call production and limb motion by Mystacina tuberculata Gray 1843 as individuals walked and flew, respectively. Results showed that there was no apparent linkage between call production and limb motion when bats walked. When in flight, two calls were produced per wingbeat, late in the downstroke and early in the upstroke. When bats walked, calls were produced at a higher rate, but at a slightly lower intensity, compared with bats in flight. These results suggest that M. tuberculata do not attempt to reduce the cost of terrestrial locomotion and call production through biomechanical linkage. They also suggest that the pattern of linkage seen when bats are in flight is not universal and that energetic savings cannot necessarily be explained by contraction of muscles associated with the downstroke alone.

Echolocation call intensity in the aerial hawking bat Eptesicus bottae (Vespertilionidae) studied using stereo videogrammetry

Aerial hawking bats use intense echolocation calls to search for insect prey. Their calls have evolved into the most intense airborne animal vocalisations. Yet our knowledge about call intensities in the field is restricted to a small number of species. We describe a novel stereo videogrammetry method used to study flight and echolocation behaviour, and to measure call source levels of the aerial hawking bat Eptesicus bottae (Vespertilionidae). Bats flew close to their predicted minimum power speed. Source level increased with call duration; the loudest call of E. bottae was at 133 dB peSPL. The calculated maximum detection distance for large flying objects (e.g. large prey, conspecifics) was up to 21 m. The corresponding maximum echo delay is almost exactly the duration of one wing beat in E. bottae and this also is its preferred pulse interval. These results, obtained by using videogrammetry to track bats in the field, corroborate earlier findings from other species from acoustic tracking methods.

Long-range stereo visual odometry for unmanned aerial vehicles

This thesis explored the utility of long-range stereo visual odometry for application on Unmanned Aerial Vehicles. Novel parameterisations and initialisation routines were developed for the long-range case of stereo visual odometry and new optimisation techniques were implemented to improve the robustness of visual odometry in this difficult scenario. In doing so, the applications of stereo visual odometry were expanded and shown to perform adequately in situations that were previously unworkable.

Design, modelling and measurement of a hybrid powerplant for unmanned aerial systems

Hybrid powerplants combining internal combustion engines and electric motor prime movers have been extensively developed for land- and marine-based transport systems. The use of such powerplants in airborne applications has been historically impractical due to energy and power density constraints. Improvements in battery and electric motor technology make aircraft hybrid powerplants feasible. This paper presents a technique for determining the feasibility and mechanical effectiveness of powerplant hybridisation. In this work, a prototype aircraft hybrid powerplant was designed, constructed and tested. It is shown that an additional 35% power can be supplied from the hybrid system with an overall weight penalty of 5%, for a given unmanned aerial system. A flight dynamic model was developed using the AeroSim Blockset in MATLAB Simulink. The results have shown that climb rates can be improved by 56% and endurance increased by 13% when using the hybrid powerplant concept.

Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases

Measuring gases for environmental monitoring is a demanding task that requires long periods of observation and large numbers of sensors. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) currently represent the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialized gas sensing systems. This paper presents the development and integration of a WSN and an UAV powered by solar energy in order to enhance their functionality and broader their applications. A gas sensing system implementing nanostructured metal oxide (MOX) and non-dispersive infrared sensors was developed to measure concentrations of CH4 and CO2. Laboratory, bench and field testing results demonstrate the capability of UAV to capture, analyze and geo-locate a gas sample during flight operations. The field testing integrated ground sensor nodes and the UAV to measure CO2 concentration at ground and low aerial altitudes, simultaneously. Data collected during the mission was transmitted in real time to a central node for analysis and 3D mapping of the target gas. The results highlights the accomplishment of the first flight mission of a solar powered UAV equipped with a CO2 sensing system integrated with a WSN. The system provides an effective 3D monitoring and can be used in a wide range of environmental applications such as agriculture, bushfires, mining studies, zoology and botanical studies using a ubiquitous low cost technology.

Design of a four-rotor aerial robot

The design and fabrication of a proto-type four-rotor vertical take-off and landing (VTOL) aerial robot for use as indoor experimental robotics platform is presented. The flyer is termed an X4-flyer. A development of the dynamic model of the system is presented and a pilot augmentation control design is proposed.

3D reconfigurable autopilot flight system for both general aviation and unmanned aerial systems

This research project investigated and designed a modular architecture for a 3D Reconfigurable Autopilot Flight System that could be used to control actuators in both manned and unmanned aircraft. The system is based on a CAN Bus interface and allows seamless control of different types of actuators. During the course of the research the differences and similarities of autopilots for fixed-wing general aviation aircraft and unmanned aircraft were analysed focusing on the actuator interfaces. This project suggests that software and hardware used in commercial-of-the-shelf avionics could be used in manned and unmanned aviation.

Remote Sensing and Data Collection from Solar Powered Wireless Sensor Network using an Unmanned Aerial Vehicle System

Sensor networks for environmental monitoring present enormous benefits to the community and society as a whole. Currently there is a need for low cost, compact, solar powered sensors suitable for deployment in rural areas. The purpose of this research is to develop both a ground based wireless sensor network and data collection using unmanned aerial vehicles. The ground based sensor system is capable of measuring environmental data such as temperature or air quality using cost effective low power sensors. The sensor will be configured such that its data is stored on an ATMega16 microcontroller which will have the capability of communicating with a UAV flying overhead using UAV communication protocols. The data is then either sent to the ground in real time or stored on the UAV using a microcontroller until it lands or is close enough to enable the transmission of data to the ground station.

LiDAR Augmented Optimal Path Planning for Unmanned Aerial Vehicle during Remote Sensing at Low Altitude and Network Sensor Data Acquisition

This technical report describes a Light Detection and Ranging (LiDAR) augmented optimal path planning at low level flight methodology for remote sensing and sampling Unmanned Aerial Vehicles (UAV). The UAV is used to perform remote air sampling and data acquisition from a network of sensors on the ground. The data that contains information on the terrain is in the form of a 3D point clouds maps is processed by the algorithms to find an optimal path. The results show that the method and algorithm are able to use the LiDAR data to avoid obstacles when planning a path from a start to a target point. The report compares the performance of the method as the resolution of the LIDAR map is increased and when a Digital Elevation Model (DEM) is included. From a practical point of view, the optimal path plan is loaded and works seemingly with the UAV ground station and also shows the UAV ground station software augmented with more accurate LIDAR data.

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.