Software in Loop Simulation based Waypoint Navigation for Fixed Wing UAV

Software in loop simulation (SILS) based waypoint navigation test platform being presented in this paper for fixed wing unmanned aerial vehicle. The proposed platform helps to test waypoint navigation algorithm before implementing into real time environment. Matlab/Simulink and X-plane flight simulator are chosen for the proposed platform. The interface between these two platforms are done by using user datagram protocol (UDP). The waypoint navigation which is to be tested is run in Matlab/Simulink environment where as fixed wing model runs in X-plane simulator. Inverted T tail fixed wing unmanned aerial vehicle configuration is chosen for this research work to verify both its inner loop (attitude control) and outer loop (navigation control). Navigation algorithm executed in Matlab/Simulink compares difference between current and desired latitude longitude position to command flight simulator to reach its desired waypoint. Navigation towards a desired waypoint will be achieved by varying inner loop attitude command of an unmanned aerial vehicle. Finally results are observed and performances are verified in X-plane simulator

An Algorithm for Autonomous Aerial Navigation using MATLAB® Mapping Tool Box

In the present era of aviation technology, autonomous navigation and control have emerged as a prime area of active research. Owing to the tremendous developments in the field, autonomous controls have led today’s engineers to claim that future of aerospace vehicle is unmanned. Development of guidance and navigation algorithms for an unmanned aerial vehicle (UAV) is an extremely challenging task, which requires efforts to meet strict, and at times, conflicting goals of guidance and control. In this paper, aircraft altitude and heading controllers and an efficient algorithm for self-governing navigation using MATLAB® mapping toolbox is presented which also enables loitering of a fixed wing UAV over a specified area. For this purpose, a nonlinear mathematical model of a UAV is used. The nonlinear model is linearized around a stable trim point and decoupled for controller design. The linear controllers are tested on the nonlinear aircraft model and navigation algorithm is subsequently developed for for autonomous flight of the UAV. The results are presented for trajectory controllers and waypoint based navigation. Our investigation reveals that MATLAB® mapping toolbox can be exploited to successfully deliver an efficient algorithm for autonomous aerial navigation for a UAV

Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data

In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing

Robust position control of a tilt-wing quadrotor

This paper presents a robust position controller for a tilt-wing quadrotor to track desired trajectories under external wind and aerodynamic disturbances. Wind effects are modeled using Dryden model and are included in the dynamic model of the vehicle. Robust position control is achieved by introducing a disturbance observer which estimates the total disturbance acting on the system. In the design of the disturbance observer, the nonlinear terms which appear in the dynamics of the aerial vehicle are also treated as disturbances and included in the total disturbance. Utilization of the disturbance observer implies a linear model with nominal parameters. Since the resulting dynamics are linear, only PID type simple controllers are designed for position and attitude control. Simulations and experimental results show that the performance of the observer based position control system is quite satisfactory

Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles

This paper investigates the development of a new guidance algorithm for a formation of unmanned aerial vehicles. Using the new approach of bifurcating potential fields, it is shown that a formation of unmanned aerial vehicles can be successfully controlled such that verifiable autonomous patterns are achieved, with a simple parameter switch allowing for transitions between patterns. The key contribution that this paper presents is in the development of a new bounded bifurcating potential field that avoids saturating the vehicle actuators, which is essential for real or safety-critical applications. To demonstrate this, a guidance and control method is developed, based on a six-degreeof-freedom linearized aircraft model, showing that, in simulation, three-dimensional formation flight for a swarm of unmanned aerial vehicles can be achieved