1,313 research outputs found
Wide-Area Surveillance System using a UAV Helicopter Interceptor and Sensor Placement Planning Techniques
This project proposes and describes the implementation of a wide-area surveillance system comprised of a sensor/interceptor placement planning and an interceptor unmanned aerial vehicle (UAV) helicopter. Given the 2-D layout of an area, the planning system optimally places perimeter cameras based on maximum coverage and minimal cost. Part of this planning system includes the MATLAB implementation of Erdem and Sclaroff’s Radial Sweep algorithm for visibility polygon generation. Additionally, 2-D camera modeling is proposed for both fixed and PTZ cases. Finally, the interceptor is also placed to minimize shortest-path flight time to any point on the perimeter during a detection event.
Secondly, a basic flight control system for the UAV helicopter is designed and implemented. The flight control system’s primary goal is to hover the helicopter in place when a human operator holds an automatic-flight switch. This system represents the first step in a complete waypoint-navigation flight control system. The flight control system is based on an inertial measurement unit (IMU) and a proportional-integral-derivative (PID) controller. This system is implemented using a general-purpose personal computer (GPPC) running Windows XP and other commercial off-the-shelf (COTS) hardware. This setup differs from other helicopter control systems which typically use custom embedded solutions or micro-controllers.
Experiments demonstrate the sensor placement planning achieving \u3e90% coverage at optimized-cost for several typical areas given multiple camera types and parameters. Furthermore, the helicopter flight control system experiments achieve hovering success over short flight periods. However, the final conclusion is that the COTS IMU is insufficient for high-speed, high-frequency applications such as a helicopter control system
Landing site reachability and decision making for UAS forced landings
After a huge amount of success within the military, the benefits of the use of unmanned
aerial systems over manned aircraft is obvious. They are becoming cheaper and their functions
advancing to such a point that there is now a large drive for their use by civilian operators.
However there are a number of significant challenges that are slowing their inevitable
integration into the national airspace systems of countries. A large array of emergency
situations will need to be dealt with autonomously by contingency management systems
to prevent potentially deadly incidences. One such emergency situation that will need autonomous
intervention, is the total loss of thrust from engine failure. The complex multi
faceted task of landing the stricken aircraft at a potentially unprepared site is called a forced
landing.
This thesis presents methods to address a number of critical parts of a forced landing
system for use by an unmanned aerial system. In order for an emergency landing site to be
considered, it needs to be within glide range. In order to find a landing site s reachability
from the point of engine failure the aircraft s glide performance and a glide path must be
known. A method by which to calculate the glide performance, both from aircraft parameters
or experiments is shown. These are based on a number of steady state assumptions to
make them generic and quick to compute. Despite the assumptions, these are shown to have
reasonable accuracy.
A minimum height loss path to the landing site is defined, which takes account of a
steady uniform wind. While this path is not the path to be flown it enables a measure of how
reachable a landing site is, as any extra height the aircraft has once it gets to the site makes
a site more reachable. It is shown that this method is fast enough to be run online and is
generic enough for use on a range of aircraft.
Based on identified factors that make a landing site more suitable, a multi criteria decision
making Bayesian network is developed to decide upon which site a unmanned aircraft
should land in. It can handle uncertainty and non-complete information while guaranteeing
a fast reasonable decision, which is critical in this time sensitive situation.
A high fidelity simulation environment and flight test platform are developed in order to
test the performance of the developed algorithms. The test environments developed enable rapid prototyping of algorithms not just within the scope of this thesis, but on a range of
vehicle types. In simulation the minimum height loss paths show good accuracy, for two
completely different types of aircraft. The decision making algorithms show that they are
capable of being ran online in a flight test. They make a reasonable decision and are capable
of quickly reacting to changing conditions, enabling redirection to a more suitable landing
site
Aeronautical engineering: A continuing bibliography, supplement 122
This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980
Optimisation-based verification process of obstacle avoidance systems for unmanned vehicles
This thesis deals with safety verification analysis of collision avoidance systems for unmanned vehicles. The safety of the vehicle is dependent on collision avoidance algorithms and associated control laws, and it must be proven that the collision avoidance algorithms and controllers are functioning correctly in all nominal conditions, various failure conditions and in the presence of possible variations in the vehicle and operational environment. The current widely used exhaustive search based approaches are not suitable for safety analysis of autonomous vehicles due to the large number of possible variations and the complexity of algorithms and the systems. To address this topic, a new optimisation-based verification method is developed to verify the safety of collision avoidance systems.
The proposed verification method formulates the worst case analysis problem arising the verification of collision avoidance systems into an optimisation problem and employs optimisation algorithms to automatically search the worst cases. Minimum distance to the obstacle during the collision avoidance manoeuvre is defined as the objective function of the optimisation problem, and realistic simulation consisting of the detailed vehicle dynamics, the operational environment, the collision avoidance algorithm and low level control laws is embedded in the optimisation process. This enables the verification process to take into account the parameters variations in the vehicle, the change of the environment, the uncertainties in sensors, and in particular the mismatching between model used for developing the collision avoidance algorithms and the real vehicle. It is shown that the resultant simulation based optimisation problem is non-convex and there might be many local optima.
To illustrate and investigate the proposed optimisation based verification process, the potential field method and decision making collision avoidance method are chosen as an obstacle avoidance candidate technique for verification study. Five benchmark case studies are investigated in this thesis: static obstacle avoidance system of a simple unicycle robot, moving obstacle avoidance system for a Pioneer 3DX robot, and a 6 Degrees of Freedom fixed wing Unmanned Aerial Vehicle with static and moving collision avoidance algorithms. It is proven that although a local optimisation method for nonlinear optimisation is quite efficient, it is not able to find the most dangerous situation. Results in this thesis show that, among all the global optimisation methods that have been investigated, the DIviding RECTangle method provides most promising performance for verification of collision avoidance functions in terms of guaranteed capability in searching worst scenarios
Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review
There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones, in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously --- without human interaction, perform specific tasks and avoid obstacles. Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles. Understanding the navigation environment and algorithmic limitations plays an essential role in choosing the appropriate RL algorithm to solve the navigation problem effectively. Consequently, this study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software. Next, RL algorithms are classified and discussed based on the environment, algorithm characteristics, abilities, and applications in different UAV navigation problems, which will help the practitioners and researchers select the appropriate RL algorithms for their UAV navigation use cases. Moreover, identified gaps and opportunities will drive UAV navigation research
3D visualization of in-flight recorded data.
Human being can easily acquire information by showing the object than reading the description of it. Our brain stores images that the eyes are seeing and by the brain mapping, people can analyze information by imagination in the brain. This is the reason why visualization is important and powerful. It helps people remember the scene later. Visualization transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations (Flurchick, 2001). As a consequence, many computer scientists and programmers take their time to build better visualization of the data for users. For the flight data from an aircraft, it is better to understand data in 3D computer graphics rather than to look at mere numbers. The flight data consists of several fields such as elapsed time, latitude, longitude, altitude, ground speed, roll angle, pitch angle, heading, wind speed, and so on. With these data variables, filtering is the first process for visualization in order to gather important information. The collection of processed data is transformed to 3D graphics form to be rendered by generating Keyhole Mark-up Language (KML) files in the system. KML is an XML grammar and file format for modeling and storing geographic features such as points, lines, images, polygons, and models for display in Google Earth or Google Maps. Like HTML, KML has a tag-based structure with names and attributes used for specific display purposes. In the present work, new approaches to visualize flight using Google Earth are developed. Because of the limitation of the Google Earth API, the Great Circle Distance calculation and trigonometric functions are implemented to handle the position, angles of roll and pitch, and a range of the camera positions to generate several points of view. Currently, visual representation of flight data depends on 2D graphics although an aircraft flies in a 3D space. The graphical interface allows flight analysts to create ground traces in 2D, and flight ribbons and flight paths with altitude in 3D. Additionally, by incorporating weather information, fog and clouds can also be generated as part of the animation effects. With 3D stereoscopic technique, a realistic visual representation of the flights is realized
Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection
Automated visual inspection of on-and offshore wind turbines using aerial
robots provides several benefits, namely, a safe working environment by
circumventing the need for workers to be suspended high above the ground,
reduced inspection time, preventive maintenance, and access to hard-to-reach
areas. A novel nonlinear model predictive control (NMPC) framework alongside a
global wind turbine path planner is proposed to achieve distance-optimal
coverage for wind turbine inspection. Unlike traditional MPC formulations,
visual tracking NMPC (VT-NMPC) is designed to track an inspection surface,
instead of a position and heading trajectory, thereby circumventing the need to
provide an accurate predefined trajectory for the drone. An additional
capability of the proposed VT-NMPC method is that by incorporating inspection
requirements as visual tracking costs to minimize, it naturally achieves the
inspection task successfully while respecting the physical constraints of the
drone. Multiple simulation runs and real-world tests demonstrate the efficiency
and efficacy of the proposed automated inspection framework, which outperforms
the traditional MPC designs, by providing full coverage of the target wind
turbine blades as well as its robustness to changing wind conditions. The
implementation codes are open-sourced.Comment: 8 pages, accepted for publication at ICAR conferenc
Development and demonstration of an on-board mission planner for helicopters
Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project
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