Stabilizing predictive visual feedback control for fixed camera systems

Murao, Toshiyuki; Kawai, Hiroyuki; Fujita, Masayuki

doi:10.1002/ecj.10357

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…Our previous work has proposed stabilizing receding horizon control for visual feedback systems with manipulator dynamics . In a similar way, our proposed approach can be applied to robot systems which must be controlled with a small sampling period.…”

Section: Image Space Navigation Function‐based Path Planningmentioning

confidence: 99%

“…where

{\overset{f}{true}}_{italicco} \in {scriptR}^{8}

is the estimated value of the image features and

J ({\overset{true¯}{g}}_{co}) \in {scriptR}^{8 \times 6}

is an image Jacobian‐like matrix . The pose control error vector e c can also be calculated by using the desired relative pose g cd and the estimation error vector e e .…”

Section: Stabilizing Predictive Visual Feedback Controlmentioning

confidence: 99%

“…Theorem 1 guarantees the stability of receding horizon control by using the fact that the energy function ρV ( x ) of the visual motion error system (9) can be regarded as a control Lyapunov function in the case of ρ 2 I ≥ 4 QR . One of the contributions of this paper is that the proposed control law can be applied to the time‐varying desired relative pose g cd ( t ), similar to the case of a constant one . Since the stabilizing receding horizon control design is based on optimal control theory, the control performance should be improved under the condition of adequate gain assignment in the cost function.…”

Section: Stabilizing Predictive Visual Feedback Controlmentioning

confidence: 99%

“…The pose control error vector e c can also be calculated by using the desired relative pose g cd and the estimation error vector e e [14]. Differentiating Eqs.…”

Section: Visual Motion Error Systemmentioning

confidence: 99%

“…One of the contributions of this paper is that the proposed control law can be applied to the time-varying desired relative pose g cd (t), similar to the case of a constant one [13,14]. Since the stabilizing receding horizon control design is based on optimal control theory, the control performance should be improved under the condition of adequate gain assignment in the cost function.…”

Section: Stabilizing Predictive Visual Feedback Controlmentioning

confidence: 99%

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Stabilizing Predictive Visual Feedback Control via Image Space Navigation Function

Murao

Kawai

Fujita

2013

Elect Comm in Japan

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SUMMARY This paper investigates stabilizing receding horizon control via an image space navigation function for three‐dimensional (3D) visual feedback systems. First, we describe the representation of a relative pose and a camera model. Next, a visual motion error system which can handle time‐varying desired motion is constructed. Then, visual motion observer‐based stabilizing receding horizon control for 3D visual feedback systems is proposed. Moreover, a path planner appropriate for the visual motion error system is designed through an image space navigation function to keep all features in the camera field of view. The main contribution of this paper is to show that the path planner which always remains in the camera field of view during servoing is designed for position‐based visual feedback receding horizon control based on optimal control theory. Finally, we present simulation and nonlinear experimental results in order to verify control performance with visibility maintenance of the proposed control scheme. © 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 96(10): 12–21, 2013; Published online in Wiley Online Library (http://wileyonlinelibrary.com). DOI 10.1002/ecj.11493

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Section: Image Space Navigation Function‐based Path Planningmentioning

confidence: 99%

“…where

{\overset{f}{true}}_{italicco} \in {scriptR}^{8}

is the estimated value of the image features and

J ({\overset{true¯}{g}}_{co}) \in {scriptR}^{8 \times 6}

is an image Jacobian‐like matrix . The pose control error vector e c can also be calculated by using the desired relative pose g cd and the estimation error vector e e .…”

Section: Stabilizing Predictive Visual Feedback Controlmentioning

confidence: 99%

Section: Stabilizing Predictive Visual Feedback Controlmentioning

confidence: 99%

“…The pose control error vector e c can also be calculated by using the desired relative pose g cd and the estimation error vector e e [14]. Differentiating Eqs.…”

Section: Visual Motion Error Systemmentioning

confidence: 99%

Section: Stabilizing Predictive Visual Feedback Controlmentioning

confidence: 99%

See 3 more Smart Citations