Large View Visual Servoing of a Mobile Robot with a Pan-Tilt Camera

Nierobisch, Thomas; Fischer, Wladimir; Hoffmann, Frank

doi:10.1109/iros.2006.282503

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…Thus, the study of visual tracking control, i.e. the vision-based robot motion control to track an interesting target, has become an active area in robotics research [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Based on motion constraints and the scenario of robotic applications, the research on visual tracking control can be categorized into visual servoing for holonomic manipulators and visual tracking for nonholonomic mobile robots.…”

Section: Introductionmentioning

confidence: 99%

“…Such a visual tracking control task encompasses several interesting issues such as vision-based motion control of mobile robots, estimation of Jacobian matrix, uncertainties caused by image noise and occlusion during visual tracking process. Most researchers focus on the design of visual tracking controllers to track a static object, such as a ground line, landmark, or reference image for the purpose of visual navigation [4][5][6][7] or visual regulation (e.g. visual homing) [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic visual tracking control of a mobile robot with image noise and occlusion robustness

Tsai

Song

2009

Image and Vision Computing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic visual tracking control of a mobile robot with image noise and occlusion robustness

Tsai

Song

2009

Image and Vision Computing

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“…Due to large number of mobile robot visual tracking control methods, we classify the reported methods into four groups based on the type of the target to be tracked. Many efforts focus on the first group which aims to track a static target, such as a ground line, landmark, or reference image, for the purpose of mobile robot navigation or regulation (so-called homing) [5][6][7][8][9][10][11][12][13][14][15][16][17]. For a mobile robot to track a ground line, Ma et al formulated the visual tracking control problem as controlling the shape of a ground curve in the image plane and proposed a closed-loop vision-guided control system for a nonholonomic mobile robot [5].…”

Section: Introductionmentioning

confidence: 99%

Robust visual tracking control system of a mobile robot based on a dual-Jacobian visual interaction model

Tsai

Song

Dutoit

et al. 2009

Robotics and Autonomous Systems

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“…So far, there have been only a few IBVS implementations for real plants. IBVS has been employed in various work [14,[28][29][30]. IBVS has been considered for an indoor blimp [28] and a robotic airship [29].…”

mentioning

confidence: 99%

“…IBVS has been considered for an indoor blimp [28] and a robotic airship [29]. A pan-tilt camera has been used for a ground robot [30]. IBVS has been used to locate the center of the UAVat the centroid of feature points [14].…”

mentioning

confidence: 99%

Adaptive Image-Based Visual Servoing for an Underactuated Quadrotor System

Lee

Lim

Kim

et al. 2012

Journal of Guidance, Control, and Dynamics

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This paper presents an adaptive image-based visual servoing (IBVS) integrated with adaptive sliding mode control for a vision-based operation of a quadrotor unmanned aerial vehicle (UAV). For a seamless integration with underactuated quadrotor dynamics, roll and pitch channels are decoupled from the other channels using virtual features. This allows a simple and accurate algorithm for estimating depth information and successful application of the proposed guidance and control algorithm. By employing an adaptive gain in the IBVS control method, the chance of image feature loss is reduced, and performance and stability of the vision-guided UAV control system are improved. The overall setup allows image features to be placed at the desired position in the image plane of a camera mounted on the quadrotor UAV. Stability of the IBVS system with the controller is proved using Lyapunov stability analysis. Performance of the overall approach is validated by numerical simulation, vision integrated hardware-inthe-loop simulation, and experiments. The results confirm that the target image is successfully placed at the desired position of the image plane and the quadrotor state variables are properly regulated, showing robustness in the presence of sensor noise, parametric uncertainty, and vibration from motors.input gain vector E r = diagonal matrix with entry of [1, 1, 1, 0, 0, 1] E 4 , E 5 = 0; 0; 0; 1; 0; 0 T and 0; 0; 0; 0; 1; 0 T e = task function for the image-based visual servo control e z = unit vector in the vertical upward direction in inertial coordinate frame F gr = ground effect force, N F i = thrust force of the ith rotor, N f = focal length of camera, pixel f ex = external disturbance vector g = gravity, m=s 2 H _ x = Gaussian radial basis kernel function vector J p = image Jacobian matrix associated to image point p J x , J y , J z = moment of inertia, kg m 2 k , k = proportional gains for velocity references in roll and pitch channels L = Lyapunov function L c , L s = length of the marker defined in camera frame, and image frame, respectively l = length of the bar from the center of the quadrotor to each rotor, m m = mass of the quadrotor, kg N = number of neurons in the neural networks system P i , p i = ith image feature defined in camera frame, and image frame, respectively P r i , p r i = ith image feature defined in imaginary camera frame in the roll-and pitchcompensated camera frame, and image frame, respectively P s i = ith image feature defined in imaginary camera frame in the rotated offset translation compensated camera frame P v i , p v i = ith image feature defined in camera frame, and image frame, respectively R I B = coordinate transformation matrix from body frame to inertial frame R1; , R2; = rotation matrices for and , respectively S = sliding surface in system control design T = translational velocity, m=s u 1 , u 2 , u 3 , u 4 = quadrotor control inputs for altitude, roll, pitch, and yaw W, W ad = diagonal weighting matrix and adaptive weighting matrix for visual servoing w i , w adi = ele...

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Large View Visual Servoing of a Mobile Robot with a Pan-Tilt Camera

Cited by 9 publications

References 6 publications

Dynamic visual tracking control of a mobile robot with image noise and occlusion robustness

Dynamic visual tracking control of a mobile robot with image noise and occlusion robustness

Robust visual tracking control system of a mobile robot based on a dual-Jacobian visual interaction model

Adaptive Image-Based Visual Servoing for an Underactuated Quadrotor System

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