Rudder Augmented Trajectory Correction for Small UAV to Minimize Lateral Image Errors

Civil applications for unmanned aerial vehicles (UAVs) have increased rapidly over the last few years. In the realm of civil applications, many aircraft carry cameras that are physically fixed to the airframe. While this yields a simple and robust remote sensing platform, the imagery quality diminishes with increasing attitude errors. A rudder augmented trajectory correction method for small unmanned aerial vehicles is discussed in this paper. The goal of this type of controller is to minimize the lateral image errors of body fixed non-gimbaled cameras. We present a comparison to current aileron only trajectory correction autopilots. Simulation and flight test results are presented that show significant reduction in the roll angle present during trajectory correction resulting in a large effect on total flight line image deviations

A survey on unmanned aerial vehicle collision avoidance systems

Collision avoidance is a key factor in enabling the integration of unmanned aerial vehicle into real life use, whether it is in military or civil application. For a long time there have been a large number of works to address this problem; therefore a comparative summary of them would be desirable. This paper presents a survey on the major collision avoidance systems developed in up to date publications. Each collision avoidance system contains two main parts: sensing and detection, and collision avoidance. Based on their characteristics each part is divided into different categories; and those categories are explained, compared and discussed about advantages and disadvantages in this paper.Comment: This is only a draf

A full controller for a fixed-wing UAV

This paper presents a nonlinear control law for the stabilization of a fixed-wing UAV. Such controller solves the path-following problem and the longitudinal control problem in a single control. Furthermore, the control design is performed considering aerodynamics and state information available in the commercial autopilots with the aim of an ease implementation. It is achieved that the closed-loop system is G.A.S. and robust to external disturbances. The difference among the available controllers in the literature is: 1) it depends on available states, hence it is not required extra sensors or observers; and 2) it is possible to achieve any desired airplane state with an ease of implementation, since its design is performed keeping in mind the capability of implementation in any commercial autopilot

Optimal Trajectory-Planning of UAVs via B-Splines and Disjunctive Programming

This paper investigates an efficient algorithm for trajectory planning problem of autonomous unmanned aerial vehicles which fly over three-dimensional terrains. The proposed algorithm combines convex optimization with disjunctive programming and receding horizon concept, which has many advantages, such as a high computational speed. Disjunctive programming is applied in order to relax the non-convex constraints of the problem. Moreover, the B-spline curves are employed to represent the trajectories which should be generated in the optimization process

Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning

Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state space is high dimensional, the fire propagates stochastically, the sensor information is imperfect, and the aircraft must coordinate with each other to accomplish their mission. This work presents two deep reinforcement learning approaches for training decentralized controllers that accommodate the high dimensionality and uncertainty inherent in the problem. The first approach controls the aircraft using immediate observations of the individual aircraft. The second approach allows aircraft to collaborate on a map of the wildfire's state and maintain a time history of locations visited, which are used as inputs to the controller. Simulation results show that both approaches allow the aircraft to accurately track wildfire expansions and outperform an online receding horizon controller. Additional simulations demonstrate that the approach scales with different numbers of aircraft and generalizes to different wildfire shapes

A Comprehensive Survey of Control Strategies for Autonomous Quadrotors

Over the past several decades there has been a constant increase in the use of Unmanned Aerial Systems (UAS). Hence, there has also been a growth in the number of control algorithms to service the many applications embodied by these vehicles. Initially UAS were made popular by the military for Reconnaissance, Intelligence, Surveillance, and Target Acquisition (RISTA) applications. Now-a-days UAS are used for everything from crop surveys to tourism. Nowhere is this more evident than with multi-rotor Unmanned Aerial Vehicle (UAV). This paper presents a survey of control methods for multi-rotor systems, namely quadrotors. In doing so, we hope to visualize a clear path to what additional capabilities might be needed in the future. In our examination, we review many of the notable research organizations and their efforts to expand the utility of multirotor aircraft. We also summarize the basic literature definitions and control strategies for autonomous quadrotors.Comment: 12 pages, 15 figures, 1 tabl

Quad-rotor Flight Simulation in Realistic Atmospheric Conditions

In trajectory planning and control design for unmanned air vehicles, highly simplified models are typically used to represent the vehicle dynamics and the operating environment. The goal of this work is to perform real-time, but realistic flight simulations and trajectory planning for quad-copters in low altitude (<500m) atmospheric conditions. The aerodynamic model for rotor performance is adapted from blade element momentum theory and validated against experimental data. Large-eddy simulations of the atmospheric boundary layer are used to accurately represent the operating environment of unmanned air vehicles. A reduced-order version of the atmospheric boundary layer data as well as the popular Dryden model are used to assess the impact of accuracy of the wind field model on the predicted vehicle performance and trajectory. The wind model, aerodynamics and control modules are integrated into a six-degree-of-freedom flight simulation environment with a fully nonlinear flight controller. Simulations are performed for two representative flight paths, namely, straight and circular paths. Results for different wind models are compared and the impact of simplifying assumptions in representing rotor aerodynamics is discussed. The simulation framework and codes are open-sourced for use by the community.Comment: Preprint submitted to AIAA Journa

MAT-Fly: an educational platform for simulating Unmanned Aerial Vehicles aimed to detect and track moving objects

The main motivation of this work is to propose a simulation approach for a specific task within the UAV (Unmanned Aerial Vehicle) field, i.e., the visual detection and tracking of arbitrary moving objects. In particular, it is described MAT-Fly, a numerical simulation platform for multi-rotors aircraft characterized by the ease of use and control development. The platform is based on Matlab and the MathWorks Virtual Reality (VR) and Computer Vision System (CVS) toolboxes that work together to simulate the behavior of a drone in a 3D environment while tracking a car that moves a long a non trivial path. The VR toolbox has been chosen due to the familiarity that students have with Matlab and because it allows to move the attention to the classifier, the tracker, the reference generator and the trajectory tracking control thanks to its simple structure. The overall architecture is quite modular so that each block can be easily replaced with others by simplifying the development phase and by allowing to add even more functionalities. The simulation platform makes easy and quick to insert and to remove flight control system components, testing and comparing different plans when computer vision algorithms are in the loop. In an automatic way, the proposed simulator is able to acquire frames from the virtual scenario, to search for one or more objects on which it has been trained during the learning phase, and to track the target position applying a trajectory control addressing what is well-known in the literature as an image-based visual servoing problem. Some simple testbeds have been presented in order to show the effectiveness and robustness of the proposed approach as well as the platform works. We released the software as open-source, making it available for educational purposes

Sample-based SMPC for tracking control of fixed-wing UAV: multi-scenario mapping

In this paper, a guidance and tracking control strategy for fixed-wing Unmanned Aerial Vehicle (UAV) autopilots is presented. The proposed control exploits recent results on sample-based stochastic Model Predictive Control, which allow coping in a computationally efficient way with both parametric uncertainty and additive random noise. Different application scenarios are discussed, and the implementability of the proposed approach are demonstrated through software-in-the-loop simulations. The capability of guaranteeing probabilistic robust satisfaction of the constraint specifications represents a key-feature of the proposed scheme, allowing real-time tracking of the designed trajectory with guarantees in terms of maximal deviation with respect to the planned one. The presented simulations show the effectiveness of the proposed control scheme.Comment: 13 pages; 9 figures; 3 table

A Harmonic Potential Approach For Simultaneous Planning And Control Of A Generic UAV Platform

Simultaneous planning and control of a large variety of unmanned aerial vehicles (UAVs) is tackled using the harmonic potential field (HPF) approach. A dense reference velocity field generated from the gradient of an HPF is used to regulate the velocity of the UAV concerned in a manner that would propel the UAV to a target point while enforcing the constraints on behavior that were a priori encoded in the reference field. The regulation process is carried-out using a novel and simple concept called the: virtual velocity attractor (VVA). The combined effect of the HPF gradient and the VVA is found able to yield an efficient, easy to implement, well-behaved and provably-correct context-sensitive control action that suits a wide variety of UAVs. The approach is developed and basic proofs of correctness are provided along with simulation results