Nonlinear tracking and landing controller for quadrotor aerial robots

Voos, Holger; Bou-Ammar, Haitham

doi:10.1109/cca.2010.5611204

Cited by 35 publications

(10 citation statements)

References 9 publications

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“…Some examples include work presented by Lange in [ 47 ] where a visual system is used to estimate a vehicle position relative to a landing place. In [ 48 , 49 ], a decomposition of a quadrotor control system to an outer-loop velocity control and an inner-loop attitude control system is proposed. In this work, the landing controller consists of a linear altitude controller and a nonlinear 2D-tracking controller.…”

Section: Related Workmentioning

confidence: 99%

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers

Olivares-Méndez

Ludivig

et al. 2015

Sensors

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Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing.

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Section: Related Workmentioning

confidence: 99%

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers

Olivares-Méndez

Ludivig

et al. 2015

Sensors

Self Cite

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“…However, since these vehicles are highly unstable nonlinear dynamical systems, a suitable control system is required for their attitude stabilization and navigation [3]. So, our goal is to model a quad-rotor robot as realistically as possible.…”

Section: Vehicle Dynamicsmentioning

confidence: 99%

“…We consider an inertial frame and a body fixed frame whose origin is in the center of mass of the quad-rotor. Thus, the dynamic model of the quad-rotor can be derived using Newton-Euler formalism [3]. The dynamic model is derived under the following assumptions;…”

Section: Vehicle Dynamicsmentioning

confidence: 99%

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Nonlinear tracking and aggressive maneuver controllers for quad-rotor robots using Learning Automata

Santos

Givigi

Nascimento

2012

2012 IEEE International Systems Conference SysCon 2012

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In this paper will be presented a novel approach to design and optimization of attitude and path tracking controller for quad-rotor robots based on Learning Automata algorithm. The proposed method has superior features, including easy implementation, stable and fast convergence characteristic, and good computational efficiency while compared with others learning approaches. The quad-rotor robots have become increasingly important in recent years as platforms for both research and commercial applications. For this reason, we propose a set-up that can be used to the training and evaluation of the quad-rotor controllers in realistic conditions. First, the vehicle dynamics and mathematical model are presented. The attitude and path tracking control strategies for the robot are formulated. Next, the method used to adjust the parameters of the controllers through the Reinforcement Learning algorithm is discussed. The simulation environment is composed by 2 host computers where one host executes the control loops and the training algorithm implemented in Matlab/Simulink. The other host runs the quad-rotor model using the X-Plane Flight Simulator. The two hosts communicate using UDP (User Data Protocol) over a standard Ethernet wired network. Using this framework is possible tuning the parameters of the controllers for a nonlinear aircraft which interacts with the environment, taken into account, the aerodynamics effects present during the quad-rotor flight. This simulation environment showed to be a good set-up for researchers to investigate the application of learning algorithms to adjust the control laws for several flight maneuvers and conditions. Finally, the results obtained from the controllers adjusted by the Learning Automata algorithm are presented.

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“…Cameras are able to support the vehicle autonomy using visual information. In [12], simulation of the landing logic for a quadrotor UAV is provided. In [13,14], a full landing sequence of the quadrotor UAV is introduced in indoor environments.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Wireless Self-Charging for Multi-Rotor Unmanned Aerial Vehicles

et al. 2017

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Rotary-wing unmanned aerial vehicles (UAVs) have the ability to operate in confined spaces and to hover over point of interest, but they have limited flight time and endurance. Conventional contact-based charging system for UAVs has been used, but it requires high landing accuracy for proper docking. Instead of the conventional system, autonomous wireless battery charging system for UAVs in outdoor conditions is proposed in this paper. UAVs can be wirelessly charged using the proposed charging system, regardless of yaw angle between UAVs and wireless charging pad, which can further reduce their control complexity for autonomous landing. The increased overall mission time eventually relaxes the limitations on payload and flight time. In this paper, a cost effective automatic recharging solution for UAVs in outdoor environments is proposed using wireless power transfer (WPT). This research proposes a global positioning system (GPS) and vision-based closed-loop target detection and a tracking system for precise landing of quadcopters in outdoor environments. The system uses the onboard camera to detect the shape, color and position of the defined target in image frame. Based on the offset of the target from the center of the image frame, control commands are generated to track and maintain the center position. Commercially available AR.Drone. was used to demonstrate the proposed concept which is equppied with bottom camera and GPS. Experiments and analyses showed good performance, and about 75% average WPT efficiency was achieved in this research.

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Nonlinear tracking and landing controller for quadrotor aerial robots

Cited by 35 publications

References 9 publications

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers

Nonlinear tracking and aggressive maneuver controllers for quad-rotor robots using Learning Automata

Autonomous Wireless Self-Charging for Multi-Rotor Unmanned Aerial Vehicles

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