Implementation of autonomous visual tracking and landing for a low-cost quadrotor

Bi, Yingcai; Duan, Haibin

doi:10.1016/j.ijleo.2012.10.060

Cited by 74 publications

(37 citation statements)

References 11 publications

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“…Bi and Duan [5] implement a visual tracking algorithm using a low-cost quadcopter. The target is a colored landing platform that is moved manually on a cart.…”

Section: Monocular Visual Tracking By Uavsmentioning

confidence: 99%

Joint Localization of Pursuit Quadcopters and Target Using Monocular Cues

Basit

Qureshi

Dailey

et al. 2014

J Intell Robot Syst

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“…Bi and Duan [5] implement a visual tracking algorithm using a low-cost quadcopter. The target is a colored landing platform that is moved manually on a cart.…”

Section: Monocular Visual Tracking By Uavsmentioning

confidence: 99%

Joint Localization of Pursuit Quadcopters and Target Using Monocular Cues

Basit

Qureshi

Dailey

et al. 2014

J Intell Robot Syst

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“…2 [1]. The input of the controller is the errors, which can be obtained by our proposed chaotic EDA.…”

Section: ( ) 100mentioning

confidence: 99%

“…The idea of using four rotors is realized as a full-scale helicopter as early as 1920s [1]. However, quadrotor is dynamically unstable and not widely developed in applications until the advance in computers and micro sensors.…”

Section: Introductionmentioning

confidence: 99%

Quadrotor Flight Control Parameters Optimization Based on Chaotic Estimation of Distribution Algorithm

Chu

Duan

2013

Advances in Neural Networks – ISNN 2013

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Abstract.Quadrotor is a type of rotor craft that consists of four rotors and two pairs of counter-rotating, fixed-pitch blades located at the four corners of the body. The flight control parameters optimization is one of the key issues for quadrotor. Estimation of distribution algorithm is a new kind of evolutionary algorithm developed rapidly recently. However, low convergence speed and local optimum of the EDA are the main disadvantages that limit its further application. To overcome the disadvantages of EDA, a chaotic estimation of distribution algorithm is proposed in this paper. It is a combination of chaos theory and principles of estimation of distribution algorithm. Series of experimental comparison results are presented to show the feasibility, effectiveness and robustness of our proposed method. The results show that the proposed chaotic EDA can effectively improve both the global searching ability and the speed of convergence.

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“…In [7], the dynamical characteristics of a quadrotor are analyzed to design a PID controller which aims to regulate the posture (position and orientation) of the quadrotor. In [8], a classical PID is implemented on a quadrotor for autonomous visual tracking and landing on a moving carrier. Based on the PID controller, the autonomous control problem of a quadrotor UAV in GPS-denied unknown environments is studied [9], [10].…”

Section: Introductionmentioning

confidence: 99%

AR.Drone UAV control parameters tuning based on particle swarm optimization algorithm

Mac

Copot

Duc

et al. 2016

2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

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Abstract-In this paper, a proposed particle swarm optimization called multi-objective particle swarm optimization (MOPSO) with an accelerated update methodology is employed to tune Proportional−Integral−Derivative (PID) controller for an AR.Drone quadrotor. The proposed approach is to modify the velocity formula of the general PSO systems in order for improving the searching efficiency and actual execution time. Three PID control parameters, i.e., the proportional gain K p , integral gain K i and derivative gain K d are required to form a parameter vector which is considered as a particle of PSO. To derive the optimal PID parameters for the Ar.Drone, the modified update method is employed to move the positions of all particles in the population. In the meanwhile, multi-objective functions defined for PID controller optimization problems are minimized. The results verify that the proposed MOPSO is able to perform appropriately in Ar.Drone control system.

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Implementation of autonomous visual tracking and landing for a low-cost quadrotor

Cited by 74 publications

References 11 publications

Joint Localization of Pursuit Quadcopters and Target Using Monocular Cues

Joint Localization of Pursuit Quadcopters and Target Using Monocular Cues

Quadrotor Flight Control Parameters Optimization Based on Chaotic Estimation of Distribution Algorithm

AR.Drone UAV control parameters tuning based on particle swarm optimization algorithm

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