HILS based Waypoint Simulation for Fixed Wing Unmanned Aerial Vehicle (UAV)

Hardware in loop simulation HILS-based waypoint simulation for fixed wing unmanned aerial vehicles is proposed in this paper. It uses an open-source arducopter as a flight controller, mission planner, and X-plane simulator. Waypoint simulation is carried out in the flight controller and executed in an X-plane simulator through a mission planner. A fixed wing unmanned aerial vehicle with an inverted T tail configuration has been chosen to study and validate waypoint flight control algorithms. The data transmission between mission planner and flight controller is done by serial protocol, whereas data exchange between X-plane and mission planner is done by User Datagram Protocol (UDP). APM mission planner is used as a machine interface to exchange data between the flight controller and the user. User inputs and flight gain parameters, both inner loop and outer loop, can be modified with the help of a mission planner. In addition to that, the mission planner provides a visual output representation of flight data and navigation algorithm

Transfer Learning-Based Crack Detection by Autonomous UAVs

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping in building inspection. Yet, the number of studies is limited considering the post processing of the data and its integration with autonomous UAVs. These will enable huge steps onward into full automation of building inspection. In this regard, this work presents a decision making tool for revisiting tasks in visual building inspection by autonomous UAVs. The tool is an implementation of fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack detection. It offers an optional mechanism for task planning of revisiting pinpoint locations during inspection. It is integrated to a quadrotor UAV system that can autonomously navigate in GPS-denied environments. The UAV is equipped with onboard sensors and computers for autonomous localization, mapping and motion planning. The integrated system is tested through simulations and real-world experiments. The results show that the system achieves crack detection and autonomous navigation in GPS-denied environments for building inspection

Close Formation Flight Missions Using Vision-Based Position Detection System

In this thesis, a formation flight architecture is described along with the implementation and evaluation of a state-of-the-art vision-based algorithm for solving the problem of estimating and tracking a leader vehicle within a close-formation configuration. A vision-based algorithm that uses Darknet architecture and a formation flight control law to track and follow a leader with desired clearance in forward, lateral directions are developed and implemented. The architecture is run on a flight computer that handles the process in real-time while integrating navigation sensors and a stereo camera. Numerical simulations along with indoor and outdoor actual flight tests demonstrate the capabilities of detection and tracking by providing a low cost, compact size and low weight solution for the problem of estimating the location of other cooperative or non-cooperative flying vehicles within a formation architecture

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

A modular software architecture for UAVs

There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module

Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system is that all the algorithms relied on the multi-resolution of the octomap for the world representation. We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements of the capability of the open-source system to run online and on-board the UAV in real-time. Our approach is compared to different reference heuristics under this simulation environment showing better performance in regards to the amount of explored space. With the proposed approach, the UAV is able to explore 93% of the search space under 30 min, generating a path without repetition that adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411