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

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

Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized to demonstrate the NIFW MAV model, which has points of interest over first principle based modelling since it does not depend on the system dynamics, rather based on data and can incorporate various uncertainties like sensor error. The same clustering strategy is used to develop an adaptive fuzzy controller. The controller is then utilized to control the altitude of the NIFW MAV, that can adapt with environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.Comment: this paper is currently under review in Journal of Artificial Intelligence and Soft Computing Researc

The Phoenix Drone: An Open-Source Dual-Rotor Tail-Sitter Platform for Research and Education

In this paper, we introduce the Phoenix drone: the first completely open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a highly versatile, dual-rotor design and is engineered to be low-cost and easily extensible/modifiable. Our open-source release includes all of the design documents, software resources, and simulation tools needed to build and fly a high-performance tail-sitter for research and educational purposes. The drone has been developed for precision flight with a high degree of control authority. Our design methodology included extensive testing and characterization of the aerodynamic properties of the vehicle. The platform incorporates many off-the-shelf components and 3D-printed parts, in order to keep the cost down. Nonetheless, the paper includes results from flight trials which demonstrate that the vehicle is capable of very stable hovering and accurate trajectory tracking. Our hope is that the open-source Phoenix reference design will be useful to both researchers and educators. In particular, the details in this paper and the available open-source materials should enable learners to gain an understanding of aerodynamics, flight control, state estimation, software design, and simulation, while experimenting with a unique aerial robot.Comment: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'19), Montreal, Canada, May 20-24, 201

Modeling and nonlinear adaptive control of an aerial manipulation system

Autonomous aerial robots have become an essential part of many civilian and military applications. The workspace and agility of these vehicles motivated great research interest resulting in various studies addressing their control architectures and mechanical configurations. Increasing autonomy enabled them to perform tasks such as surveillance, inspection and remote sensing in hazardous and challenging environments. The ongoing research promises further contributions to the society, in both theory and practice. To furthermore extend their vast applications, aerial robots are equipped with the tools to enable physical interaction with the environment. These tasks represent a great challenge due to the technological limitations as well as the lack of sophisticated methods necessary for the control of the system to perform desired operations in an efficient and stable manner. Modeling and control problem of an aerial manipulation is still an open research topic with many studies addressing these issues from different perspectives. This thesis deals with the nonlinear adaptive control of an aerial manipulation system (AMS). The system consists of a quadrotor equipped with a 2 degrees of freedom (DOF) manipulator. The complete modeling of the system is done using the Euler-Lagrange method. A hierarchical nonlinear control structure which consists of outer and inner control loops has been utilized. Model Reference Adaptive Controller (MRAC) is designed for the outer loop where the required command signals are generated to force the quadrotor to move on a reference trajectory in the presence of mass uncertainties and reaction forces coming from the manipulator. For the inner loop, the attitude dynamics of the quadrotor and the joint dynamics of the 2-DOF robotic arm are considered as a fully actuated 5-DOF unified part of the AMS. Nonlinear adaptive control has been utilized for the low-level controller where the changes in inertias have been considered. The proposed controller is tested on a high fidelity AMS model in the presence of uncertainties, wind disturbances and measurement noise, and satisfactory trajectory tracking performance with improved robustness is achieved

Mini Unmanned Aerial Systems (UAV) - A Review of the Parameters for Classification of a Mini UAV.

In the recent years, Mini Unmanned Aerial Vehicle (UAV) has generated a lot of interest both in military and civilian applications. Contemporary innovations have seen the entry of Mini UAV into a wide range of hitherto fore unexplored domains. Advancement in computer systems, miniaturisation of electronics, artificial intelligence and composite materials is propelling the development of Mini UAV. Mini UAV is a class of UAV within the large family of unmanned systems categorised by a set of parameters. However, there are glaring inconsistencies and lack of uniformity in specifying the parameters which define a Mini UAV. The paper explores the factors which define a Mini UAV to establish itself as a distinct class. Based on the review of the recent literature and various manufacturer’s data of Mini UAVs, both fixed-wing and rotary-wing, categorisation of Mini UAV have been analysed considering functional requirements of operating altitude, endurance, operating range, maximum take-off weight and size

Simulation of Flapping-wing Unmanned Aerial Vehicle using X-plane and Matlab/Simulink

This paper presents the simulation of flapping-wing unmanned aerial vehicle model using X-plane and Matlab/ Simulink. The flapping-wing ornithopter model (i.e. an aircraft that flies by flapping its wings) has been developed in plane maker software and executed in the X-plane environment. The key idea of flapping-wing mechanism in X-plane software is by varying its dihedral angle sinusoidally. This sinusoidally varying dihedral angle of wing creates upward and downward stroke moments inturn this creates a lift and a forward thrust for flying the flapping-wing model. Here pitch, roll, yaw and throttle (flapping rate) is fed as reference input through the user datagram protocol (UDP) port. The difference between the reference inputs, the simulated outputs are again fed back to simulator through UDP port and the gains are observed for the responses of flapping-wing unmanned aerial vehicle in Matlab/Simulink environment. Here various gains are used to monitor the optimized flying of flapping-wing model.Defence Science Journal, Vol. 64, No. 4, July 2014, pp.327-331, DOI:http://dx.doi.org/10.14429/dsj.64.493

Platform Development for the Implementation and Testing of New Swarming Strategies

Gemstone Team SWARM-AISwarm robotics--the use of multiple autonomous robots in coordination to accomplish a task--is useful for mapping, light package transport, and search and rescue operations, among other applications. Researchers and industry professionals have developed robotic swarm mechanisms to accomplish these tasks. Some of those mechanisms or “strategies” have been tested on hardware; however, the technical requirements involved in fielding a drone swarm can be prohibitive to physical testing. Team SWARM-AI has developed a platform that provides a starting point for testing new swarming strategies. This platform allows the user to select vehicles of their choosing- either air, land, or water based, or some combination thereof- as well as define their own swarming method. Using a novel decentralized approach to ground control software, this platform provides a user interface and a system of computational “units” to coordinate drone swarms with a centralized, decentralized, or combination architecture. Additionally, the platform propagates user input from the master unit to the rest of the swarm and allows each unit to request sensor data from other units. The user is free to edit the processes by which each drone interacts with the environment and the rest of the swarm, giving them freedom to test their swarming strategy. The software system is then tested with a swarm of quadcopters using Software in the Loop (SITL) testing