Vision Based Tracking for a Quadrotor Using Vanishing Points

Gomez-Balderas, Jose-Ernesto; Castillo, Pedro; Guerrero, José Alfredo; Lozano, Rogelio

doi:10.1007/s10846-011-9579-z

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…One of the attractive scenarios for multi-agent system is the tracking of a ground target using an unmanned aerial vehicle [2]. This allows performing important tasks like surveillance of convoys, reconnaissance, and intelligence missions [3]. Tracking ground targets is more difficult than aerial ones due to the topographic variations that can influence a target's motion patterns and obscurity to observation [4].…”

Section: Introductionmentioning

confidence: 99%

SVSF-Based Robust UGV/UAV Control/Tracking Architecture in Disturbed Environment

Oussar¹,

Ferrag²,

Guiatni³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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This paper presents the design of a robust architecture for the tracking of an unmanned ground vehicle (UGV) by an unmanned aerial vehicle (UAV). To enhance the robustness of the ground vehicle in the face of external disturbances and handle the non-linearities due to inputs saturation, an integral sliding mode controller was designed for the task of trajectory tracking. Stabilization of the aerial vehicle is achieved using an integral-backstepping solution. Estimation of the relative position between the two agents was solved using two approaches: the first solution (optimal) is based on a Kalman filter (KF) the second solution (robust) uses a smooth variable structure filter (SVSF). Simulations results, based on the full non-linear model of the two agents are presented in order to evaluate the performance and robustness of the proposed tracking architecture.

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Section: Introductionmentioning

confidence: 99%

SVSF-Based Robust UGV/UAV Control/Tracking Architecture in Disturbed Environment

Oussar¹,

Ferrag²,

Guiatni³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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“…Another group utilized a combination of LIDAR and a Microsoft Kinect sensor to explore an unknown environment [25]. Several other groups successfully used unique variations of computer vision methods as a means of localization [26], [27], and it is proving to be a very promising method of operating in GPS-denied environments.…”

Section: Introductionmentioning

confidence: 99%

A novel extended potential field controller for use on aerial robots

Woods

Lay

2016

2016 IEEE International Conference on Automation Science and Engineering (CASE)

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Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential uses in real world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller (PFC) with smoother tracking paths and shorter settling times. The proposed ePFC's stability is evaluated by Lyapunov approach, and its performance is simulated in a Matlab environment. Finally, the controller is implemented on an experimental platform in a laboratory environment which demonstrates the effectiveness of the controller.

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“…In [12] a vision-based control algorithm for stabilizing a UAV equipped with two cameras is presented. The same system has been used in [11] to track a line painted in a wall using a vanishing points technique. Some methods for designing UAV trajectories that increase the amount of information available are presented in [23].…”

Section: Introductionmentioning

confidence: 99%