Towards Long-endurance Flight: Design and Implementation of a Variable-pitch Gasoline-engine Quadrotor

Majority of today's fixed-pitch, electric-power quadrotors have short flight endurance ($<$ 1 hour) which greatly limits their applications. This paper presents a design methodology for the construction of a long-endurance quadrotor using variable-pitch rotors and a gasoline-engine. The methodology consists of three aspects. Firstly, the rotor blades and gasoline engine are selected as a pair, so that sufficient lift can be comfortably provided by the engine. Secondly, drivetrain and airframe are designed. Major challenges include airframe vibration minimization and power transmission from one engine to four rotors while keeping alternate rotors contra-rotating. Lastly, a PD controller is tuned to facilitate preliminary flight tests. The methodology has been verified by the construction and successful flight of our gasoline quadrotor prototype, which is designed to have a flight time of 2 to 3 hours and a maximum take-off weight of 10 kg.Comment: 6 page

Design and Implementation of An Improved Camera Mounted Remote Controlled Quadcopter

Aeronautics and other studies on the science of aircraft have advanced to the point where Unmanned Aerial Vehicles (UAVs) or drones are now being extensively designed. However, such aircrafts are large and not maneuverable in tight spots especially fixed wing aircraft, therefore, the design of multi-rotors are now being considered. It is inherent that small unmanned aircraft with optimal maneuverability and the ability to carry small payloads such as cameras should be designed for operations in places where a full-size aircraft are either too big or too expensive to be deployed. One of the types of Small Unmanned Aerial Vehicle (SUAV) is a Quadcopter, which can be implemented in different applications. Quadcopters are rapidly gaining interest due to their stability, low cost in building, handling capabilities and agility. Uses of such craft include aerial photography or surveillance, ground surveillance and mapping, package delivery, for rescue operations and as a research tool into ways of designing more advanced aircraft. This study therefore designed and implemented an improved industry-grade Small Unmanned Aerial Vehicle (SUAV) multi-rotor Quadcopter. Quadcopter structure model, basic components, hovering stability, dimensions, and description of basic principles of quadcopter were discussed. The study showed that SUAVs are useful across a broad range of applications. Keywords: Unmanned Aerial Vehicles, Aeronautics, Aircraft, Multi-rotors, Quadcopter, Surveillance, Aerial Photography, Small Unmanned Aerial Vehicle. DOI: 10.7176/CEIS/11-2-08 Publication date: April 30th 202

Terahertz Micro-Doppler Radar for Detection and Characterization of Multicopters

abstract: The micromotions (e.g. vibration, rotation, etc.,) of a target induce time-varying frequency modulations on the reflected signal, called the micro-Doppler modulations. Micro-Doppler modulations are target specific and may contain information needed to detect and characterize the target. Thus, unlike conventional Doppler radars, Fourier transform cannot be used for the analysis of these time dependent frequency modulations. While Doppler radars can detect the presence of a target and deduce if it is approaching or receding from the radar location, they cannot identify the target. Meaning, for a Doppler radar, a small commercial aircraft and a fighter plane when gliding at the same velocity exhibit similar radar signature. However, using a micro-Doppler radar, the time dependent frequency variations caused by the vibrational and rotational micromotions of the two aircrafts can be captured and analyzed to discern between them. Similarly, micro-Doppler signature can be used to distinguish a multicopter from a bird, a quadcopter from a hexacopter or a octacopter, a bus from a car or a truck and even one person from another. In all these scenarios, joint time-frequency transforms must be employed for the analysis of micro-Doppler variations, in order to extract the targets’ features. Due to ample bandwidth, THz radiation provides richer radar signals than the microwave systems. Thus, a Terahertz (THz) micro-Doppler radar is developed in this work for the detection and characterization of the micro-Doppler signatures of quadcopters. The radar is implemented as a continuous-wave (CW) radar in monostatic configuration and operates at a low-THz frequency of 270 GHz. A linear time-frequency transform, the short-time Fourier transform (STFT) is used for the analysis the micro-Doppler signature. The designed radar has been built and measurements are carried out using a quadcopter to detect the micro-Doppler modulations caused by the rotation of its propellers. The spectrograms are obtained for a quadcopter hovering in front of the radar and analysis methods are developed for characterizing the frequency variations caused by the rotational and vibrational micromotions of the quadcopter. The proposed method can be effective for distinguishing the quadcopters from other flying targets like birds which lack the rotational micromotions.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Differential Flatness of Lifting-Wing Quadcopters Subject to Drag and Lift for Accurate Tracking

In this paper, we propose an effective unified control law for accurately tracking agile trajectories for lifting-wing quadcopters with different installation angles, which have the capability of vertical takeoff and landing (VTOL) as well as high-speed cruise flight. First, we derive a differential flatness transform for the lifting-wing dynamics with a nonlinear model under coordinated turn condition. To increase the tracking performance on agile trajectories, the proposed controller incorporates the state and input variables calculated from differential flatness as feedforward. In particular, the jerk, the 3-order derivative of the trajectory, is converted into angular velocity as a feedforward item, which significantly improves the system bandwidth. At the same time, feedback and feedforward outputs are combined to deal with external disturbances and model mismatch. The control algorithm has been thoroughly evaluated in the outdoor flight tests, which show that it can achieve accurate trajectory tracking

MRSL: AUTONOMOUS NEURAL NETWORK-BASED SELF-STABILIZING SYSTEM

Stabilizing and localizing the positioning systems autonomously in the areas without GPS accessibility is a difficult task. In this thesis we describe a methodology called Most Reliable Straight Line (MRSL) for stabilizing and positioning camera-based objects in 3-D space. The camera-captured images are used to identify easy-to-track points “interesting points� and track them on two consecutive images. The distance between each of interesting points on the two consecutive images are compared and one with the maximum length is assigned to MRSL, which is used to indicate the deviation from the original position. To correct this our trained algorithm is deployed to reduce the deviation by issuing relevant commands, this action is repeated until MRSL converges to zero. To test the accuracy and robustness, the algorithm was deployed to control positioning of a Quadcopter. It was demonstrated that the Quadcopter (a) was highly robust to any external forces, (b) can fly even if the Quadcopter experiences loss of engine, (c) can fly smoothly and positions itself on a desired location

Quadcopter Trajectory Prediction and Wind Estimation Using Machine Learning

Small unmanned aerial systems are heavily impacted by wind disturbances. Wind causes deviations from desired trajectories, potentially leading to crashes. In this thesis, we consider two inherently related problems: predicting quadcopter trajectory deviations due to wind disturbances and estimating wind velocity based on quadcopter trajectory deviations. The former is addressed using linear difference equation identification as well as neural network (NN) modeling. Simulations validate the use of linear difference equation identification as a tool to predict trajectory deviations in crosswinds and machine learning (specifically, long short-term memory (LSTM) NNs) as an approach to predict trajectory deviations in multidimensional wind. We approach the wind estimation problem from a machine learning perspective due to easier generalization of the NN to multidimensional winds. As in the trajectory prediction case, we use LSTM NNs to identify a model. The trained NN is deployed to estimate the turbulent winds as generated by the Dryden gust model as well as a realistic large eddy simulation of a near-neutral atmospheric boundary layer over flat terrain. The resulting NN predictions are compared to a wind triangle approach that uses tilt angle as an approximation of airspeed. Results from this study indicate that the LSTM NN based approach results in lower errors in both the mean and variance of the local wind field as compared to the wind triangle approach