Investigation of methods for increasing the energy efficiency on unmanned aerial vehicles (UAVS)

In recent years, development of Unmanned Aerial Vehicles (UAV) has become a significant growing segment of the global aviation industry. These vehicles are developed with the intention of operating in regions where the presence of onboard human pilots is either too risky or unnecessary. Their popularity with both the military and civilian sectors have seen the use of UAVs in a diverse range of applications, from reconnaissance and surveillance tasks for the military, to civilian uses such as aid relief and monitoring tasks. Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. This thesis presents an investigation of methods for increasing the energy efficiency on UAVs. One method is via the development of a Mission Waypoint Optimisation (MWO) procedure for a small fixed-wing UAV, focusing on improving the onboard fuel economy. MWO deals with a pre-specified set of waypoints by modifying the given waypoints within certain limits to achieve its optimisation objectives of minimising/maximising specific parameters. A simulation model of a UAV was developed in the MATLAB Simulink environment, utilising the AeroSim Blockset and the in-built Aerosonde UAV block and its parameters. This simulation model was separately integrated with a multi-objective Evolutionary Algorithm (MOEA) optimiser and a Sequential Quadratic Programming (SQP) solver to perform single-objective and multi-objective optimisation procedures of a set of real-world waypoints in order to minimise the onboard fuel consumption. The results of both procedures show potential in reducing fuel consumption on a UAV in a ight mission. Additionally, a parallel Hybrid-Electric Propulsion System (HEPS) on a small fixedwing UAV incorporating an Ideal Operating Line (IOL) control strategy was developed. An IOL analysis of an Aerosonde engine was performed, and the most efficient (i.e. provides greatest torque output at the least fuel consumption) points of operation for this engine was determined. Simulation models of the components in a HEPS were designed and constructed in the MATLAB Simulink environment. It was demonstrated through simulation that an UAV with the current HEPS configuration was capable of achieving a fuel saving of 6.5%, compared to the ICE-only configuration. These components form the basis for the development of a complete simulation model of a Hybrid-Electric UAV (HEUAV).

A multi-resolution image alignment technique based on direct methods for pose estimation of aerial vehicles

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi- Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles��� state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle’s state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle’s state for more than one minute, at real-time frame rates based, only on visual information.

Collision warning dissemination in vehicles strings : an empirical measurement

Inter-Vehicular Communications (IVC) are considered a promising technological approach for enhancing transportation safety and improving highway efficiency. Previous theoretical work has demonstrated the benefits of IVC in vehicles strings. Simulations of partially IVC-equipped vehicles strings showed that only a small equipment ratio is sufficient to drastically reduce the number of head on collisions. However, these results are based on the assumptions that IVC exhibit lossless and instantaneous messages transmission. This paper presents the research design of an empirical measurement of a vehicles string, with the goal of highlighting the constraints introduced by the actual characteristics of communication devices. A warning message diffusion system based on IEEE 802.11 wireless technology was developed for an emergency breaking scenario. Preliminary results are presented as well, showing the latencies introduced by using 802.11a and discussing early findings and experimental limitations

Controlled drift: an investigation into the controllability of underwater vehicles with minimal actuation

This paper is directed towards providing an answer to the question, ”Can you control the trajectory of a Lagrangian float?” Being a float that has minimal actuation (only buoyancy control), their horizontal trajectory is dictated through drifting with ocean currents. However, with the appropriate vertical actuation and utilising spatio-temporal variations in water speed and direction, we show here that broad controllabilty results can be met such as waypoint following to keep a float inside of a bay or out of a designated region. This paper extends theory experimen- tally evaluted on horizontally actuated Autonomous Underwater Vehicles (AUVs) for trajectory control utilising ocean forecast models and presents an initial investi- gation into the controllability of these minimally actuated drifting AUVs. Simulated results for offshore coastal and within highly dynamic tidal bays illustrate two tech- niques with the promise for an affirmative answer to the posed question above.

Field study of air change and flow rate in six automobiles

For many people, a relatively large proportion of daily exposure to a multitude of pollutants may occur inside an automobile. A key determinant of exposure is the amount of outdoor air entering the cabin (i.e. air change or flow rate). We have quantified this parameter in six passenger vehicles ranging in age from 18 years to <1 year, at three vehicle speeds and under four different ventilation settings. Average infiltration into the cabin with all operable air entry pathways closed was between 1 and 33.1 air changes per hour (ACH) at a vehicle speed of 60 km/h, and between 2.6 and 47.3 ACH at 110 km/h, with these results representing the most (2005 Volkswagen Golf) and least air-tight (1989 Mazda 121) vehicles, respectively. Average infiltration into stationary vehicles parked outdoors varied between ~0 and 1.4 ACH and was moderately related to wind speed. Measurements were also performed under an air recirculation setting with low fan speed, while airflow rate measurements were conducted under two non-recirculate ventilation settings with low and high fan speeds. The windows were closed in all cases, and over 200 measurements were performed. The results can be applied to estimate pollutant exposure inside vehicles.

Integrating electrical vehicles to demand side response scheme in Queensland Australia

Depleting fossil fuel resources and increased accumulation of greenhouse gas emissions are increasingly making electrical vehicles (EV) attractive option for the transportation sector. However uncontrolled random charging and discharging of EVs may aggravate the problems of an already stressed system during the peak demand and cause voltage problems during low demand. This paper develops a demand side response scheme for properly integrating EVs in the Electrical Network. The scheme enacted upon information on electricity market conditions regularly released by the Australian Energy Market Operator (AEMO) on the internet. The scheme adopts Internet relays and solid state switches to cycle charging and discharging of EVs. Due to the pending time-of-use and real-price programs, financial benefits will represent driving incentives to consumers to implement the scheme. A wide-scale dissemination of the scheme is expected to mitigate excessive peaks on the electrical network with all associated technical, economic and social benefits.

Optimal sitting and sizing of distributed generators considering plug-in electric vehicles

Some uncertainties such as the stochastic input/output power of a plug-in electric vehicle due to its stochastic charging and discharging schedule, that of a wind unit and that of a photovoltaic generation source, volatile fuel prices and future uncertain load growth, all together could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distributed systems. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal sitting and sizing problem of DGs. First, a mathematical model of CCP is developed with the minimization of DGs investment cost, operational cost and maintenance cost as well as the network loss cost as the objective, security limitations as constraints, the sitting and sizing of DGs as optimization variables. Then, a Monte Carolo simulation embedded genetic algorithm approach is developed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is employed to verify the feasibility and effectiveness of the developed model and method. This work is supported by an Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) Project on Intelligent Grids Under the Energy Transformed Flagship, and Project from Jiangxi Power Company.

Wind-energy based path planning for electric unmanned aerial vehicles using Markov Decision Processes

Exploiting wind-energy is one possible way to ex- tend flight duration for Unmanned Arial Vehicles. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain time-varying wind fields and plan a path through them. A Gaussian distribution is used to determine uncertainty in the Time-varying wind fields. We use Markov Decision Process to plan a path based upon the uncertainty of Gaussian distribution. Simulation results that compare the direct line of flight between start and target point and our planned path for energy consumption and time of travel are presented. The result is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.

Reducing Actuator Switchings for Motion Control of Autonomous Underwater Vehicles

A priority when designing control strategies for autonomous underwater vehicles is to emphasize their cost of implementation on a real vehicle. Indeed, due to the vehicles' design and the actuation modes usually under consideration for underwater plateforms the number of actuator switchings must be kept to a small value to insure feasibility and precision. This is the main objective of the algorithm presented in this paper. The theory is illustrated on two examples, one is a fully actuated underwater vehicle capable of motion in six-degrees-of freedom and one is minimally actuated with control motions in the vertical plane only.

Controlling Minimally-Actuated Vehicles for Applications in Ocean Observation

Establishing a persistent presence in the ocean with an AUV to observe temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we propose a strategy that utilizes ocean model predictions to increase the autonomy and control of Lagrangian or profiling floats for precisely this purpose. An A* planner is applied to a local controllability map generated from predictions of ocean currents to compute a path between prescribed waypoints that has the highest likelihood of successful execution. The control to follow the planned path is computed by use of a model predictive controller. This controller is designed to select the best depth for the vehicle to exploit ambient currents to reach the goal waypoint. Mission constraints are employed to simulate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA USA, and show surprising results in the ability of a Lagrangian float to reach a desired location.

Windows

In this video, words emerge out of an abstract, ‘digital’, animated horizon line. The words are accompanied by a female voice-over who narrates a seductive relaxation and visualization activity. This work examines the nature of consciousness and identity in a contemporary context. It mixes the languages of meditation, new age philosophy and pop-psychology. Drawing on Zygmunt Bauman’s theoretical work on “liquid modernity”, this work questions how and where we find space for contemplation and reflection in a contemporary context increasingly defined by temporary social bonds and consumer choices.