Image based visual servoing using algebraic curves applied to shape alignment

Visual servoing schemes generally employ various image features (points, lines, moments etc.) in their control formulation. This paper presents a novel method for using boundary information in visual servoing. Object boundaries are modeled by algebraic equations and decomposed as a unique sum of product of lines. We propose that these lines can be used to extract useful features for visual servoing purposes. In this paper, intersection of these lines are used as point features in visual servoing. Simulations are performed with a 6 DOF Puma 560 robot using Matlab Robotics Toolbox for the alignment of a free-form object. Also, experiments are realized with a 2 DOF SCARA direct drive robot. Both simulation and experimental results are quite promising and show potential of our new method

Image based visual servoing using bitangent points applied to planar shape alignment

We present visual servoing strategies based on bitangents for aligning planar shapes. In order to acquire bitangents we use convex-hull of a curve. Bitangent points are employed in the construction of a feature vector to be used in visual control. Experimental results obtained on a 7 DOF Mitsubishi PA10 robot, verifies the proposed method

Model-based vs. model-free visual servoing: A Performance evaluation in microsystems

In this paper, model-based and model-free image based visual servoing (VS) approaches are implemented on a microassembly workstation, and their regulation and tracking performances are evaluated. A precise image based VS relies on computation of the image jacobian. In the model-based visual servoing, the image Jacobian is computed via calibrating the optical system. Precisely calibrated model based VS promises better positioning and tracking performance than the model-free approach. However, in the model-free approach, optical system calibration is not required due to the dynamic Jacobian estimation, thus it has the advantage of adapting to the different operating modes

Positioning and trajectory following tasks in microsystems using model free visual servoing

In this paper, we explore model free visual servoing algorithms by experimentally evaluating their performances for various tasks performed on a microassembly workstation developed in our lab. Model free or so called uncalibrated visual servoing does not need the system calibration (microscope-camera-micromanipulator) and the model of the observed scene. It is robust to parameter changes and disturbances. We tested its performance in point-to-point positioning and various trajectory following tasks. Experimental results validate the utility of model free visual servoing in microassembly tasks

Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach

We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL) by inferring differential rewards between state changes. The learned task function is then used as continuous feedbacks in an uncalibrated visual servoing(UVS) controller designed for the execution phase. Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification. It can also provide task interpretability by directly approximating the task function. Besides, benefiting from the use of a traditional UVS controller, our training process is efficient and the learned policy is independent from a particular robot platform. Various experiments were designed to show that, for a certain DOF task, our method can adapt to task/environment variances in target positions, backgrounds, illuminations, and occlusions without prior retraining.Comment: Accepted in ICRA 201

Visual Servoing from Deep Neural Networks

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.Comment: fixed authors lis