Indoor Hovering Flight of a Micro Helicopter by Markerless Visual Servo Control Based on Phase Correlation

Iwatani, Yasushi; Numata, Takuya

doi:10.9746/jcmsi.7.291

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…The first simulation showcases the effectiveness of using pose information ǧwo rather than only the position p wo from the author's previous work [15]. In the second simulation we show the advantage of using variance-dependent varying gains when the visual measurements (6) are corrupted by noise. Feature extractions from visual measurements always cause noise.…”

Section: Simulation Experimentsmentioning

confidence: 83%

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Visual Pursuit Control with Target Motion Learning via Gaussian Process

Yamauchi

Beckers

Omainska

et al. 2020

2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

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In this paper, we propose an observer-based visual pursuit control integrating 3-dimensional target motion learning by Gaussian Process Regression (GPR). We consider a situation where a visual sensor equipped rigid body pursuits a target rigid body whose velocity is unknown, but dependent on the target's pose. We estimate the pose from visual information and propose a Gaussian Process (GP) model to predict the target velocity from the pose estimate. We analyze stability of the proposed control by showing that estimation and control errors are ultimately bounded with high probability. Finally, simulations illustrate the performance of the proposed control schemes even if the visual measurement is corrupted by noise.

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Section: Simulation Experimentsmentioning

confidence: 83%

“…Control methods that use a vision sensor to estimate the state of a robot in the environment and maintain it in a desired position have been proposed, for example in [5], [6]. The authors of [7], [8] consider tracking control of a target with simultaneous vision-based estimation of the unknown target pose in two dimensions.…”

Section: Introductionmentioning

confidence: 99%

Visual Pursuit Control with Target Motion Learning via Gaussian Process

Yamauchi

Beckers

Omainska

et al. 2020

2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

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show abstract

“…The goal of this paper is to design the camera vehicle body velocity w V b vc to attain the following condition based only on visual information f under the assumption (4).…”

Section: Control Objectivementioning

confidence: 99%

“…In this work, we mainly focus on tracking control based on visual information. This theory is expected to be applied to some kinds of robots such as helicopters [4], quadrotors [5], underwater vehicles [6] and two-wheeled vehicles [7]- [9] for practical situations.…”

Section: Introductionmentioning

confidence: 99%

Visual Feedback Position Tracking and Attitude Analysis of Two-Wheeled Vehicles Integrating a Target Vehicle Motion Model

Nakano

Ibuki²,

Sampei³

2017

SICE Journal of Control, Measurement, and System Integration

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This paper studies visual feedback position tracking control of two-wheeled vehicles in the situation that a camera and a target object are attached to the vehicles, respectively. Here, the body velocity of the target object vehicle is modeled as a Fourier series expansion. The relative position between the camera and the target object is controlled to the desired relative position. The present control law is based only on visual measurements, and the necessary information to implement the law is estimated from them. The asymptotic stability of the equilibrium of the total system including the internal attitude behavior is analyzed under some conditions of the target object vehicle velocity via stability theory of perturbed systems. Finally, a numerical simulation is conducted to show the effectiveness of the proposed method.

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Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions

Omainska

Yamauchi

Beckers

et al. 2021

SICE Journal of Control, Measurement, and System Integration

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In this paper, we propose an observer-based visual pursuit control integrating three-dimensional target motion learning by Gaussian Process Regression (GPR). We consider a situation where a visual sensor equipped rigid body pursuits a target rigid body whose velocity is unknown but dependent on the target's pose. We estimate the pose from visual information and propose a Gaussian Process (GP) model to predict the target velocity from the pose estimate. We analyse stability of the proposed control by showing that estimation and control errors are ultimately bounded with high probability. Finally, simulations illustrate the performance of the proposed control schemes even if the visual measurement is corrupted by noise.

show abstract

Indoor Hovering Flight of a Micro Helicopter by Markerless Visual Servo Control Based on Phase Correlation

Cited by 5 publications

References 9 publications

Visual Pursuit Control with Target Motion Learning via Gaussian Process

Visual Pursuit Control with Target Motion Learning via Gaussian Process

Visual Feedback Position Tracking and Attitude Analysis of Two-Wheeled Vehicles Integrating a Target Vehicle Motion Model

Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions

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