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Intelligent Flight in Indoor Drones
Abstract
Currently, drones are one of the most complex control systems. This control covers from the control of the stability of the drone, to the automatic control of the navigation in complex environments. In the case of indoor drones, technological challenges are specific. This paper presents an intelligent control architecture for indoor drones where security is the main axis of the system design. So, a definition of different navigation modes based on security is proposed. The drone must have different navigation modes: manual , reactive, deliberative and intelligent. For indoor navigation it is necessary to know the position of the drone, therefore the system must have a location mode similar to GPS, but that provides better accuracy. For deliberative and intelligent modes, the system must have a map of the environment , as well as a control system that sends the navigation orders to the drone.
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Many researchers from academia and industry are investigating closely how to control an autonomous mobile robot, especially Unmanned Aerial Vehicles (UAV). This paper shows the ability of a low-cost quadcopter, "Parrot AR-Drone 2.0", to navigate a predetermined route. The path is obtained by optimized path planning algorithm. A generic Simulated Annealing (SA) optimization algorithm is implemented to generate the obstacle-free path. This path is divided into several waypoints, which are navigated by the drone in various experiments. The position and orientation of the quadcopter are estimated with the incremental motion estimation approach using Inertial Measurement Unit (IMU), which is mounted on the drone. The quadcopter is controlled via Simulink model with PID, which manipulates the drone internal controller for the pitch, roll, yaw and vertical speed. Four different experiments were tested to evaluate the performance of the proposed algorithm and the obtained results indicate the high performance of the quadcopter and its applicability in various navigation applications.
En este trabajo se presenta una estrategia de control no lineal para la estabilización de un helicóptero de cuatro rotores. El algoritmo de control se basa en el análisis de Lyapunov y en la técnica de las saturaciones anidadas. La estrategia de control es muy fácil de implementar, siendo tal que es independiente del helicóptero de cuatro rotores que se pretenda controlar. El ajuste de las ganancias del algoritmo de control es muy sencillo, en comparación con otros algoritmos propuestos en la literatura. La estrategia de control propuesta ha sido implementada sobre un sistema de tiempo real para el control de un mini- helicóptero de cuatro rotores. Los resultados obtenidos, comparados con un algo- ritmo lineal LQR, muestran que las prestaciones del algoritmo propuesto son muy superiores a las obtenidas a partir de un algoritmo lineal LQR
In this paper a complete framework is proposed, to deal with trajectory tracking and positioning with an AR.Drone Parrot quadrotor flying in indoor environments. The system runs in a centralized way, in a computer installed in a ground station, and is based on two main structures, namely a Kalman Filter (KF) to track the 3D position of the vehicle and a nonlinear controller to guide it in the accomplishment of its flight missions. The KF is designed aiming at estimating the states of the vehicle, fusing inertial and visual data. The nonlinear controller is designed with basis on a dynamic model of the AR.Drone, with the closed-loop stability proven using the theory of Lyapunov. Finally, experimental results are presented, which demonstrate the effectiveness of the proposed framework.
Three-dimensional trajectory tracking of a quadrotor through PVA control
- S E Martínez
- M Tomas-Rodriguez
Martínez, S. E., & Tomas-Rodriguez, M. (2014). Three-dimensional trajectory tracking
of a quadrotor through PVA control. Revista Iberoamericana de Automática e Informática
Industrial RIAI, 11(1), 54-67.