Conferring robustness to path-planning for image-based control

Path-planning has been proposed in visual servoing for reaching the desired location while fulfilling various constraints. Unfortunately, the real trajectory can be significantly different from the reference trajectory due to the presence of uncertainties on the model used, with the consequence that some constraints may not be fulfilled hence leading to a failure of the visual servoing task. This paper proposes a new strategy for addressing this problem, where the idea consists of conferring robustness to the path-planning scheme by considering families of admissible models. In order to obtain these families, uncertainty in the form of random variables is introduced on the available image points and intrinsic parameters. Two families are considered, one by generating a given number of admissible models corresponding to extreme values of the uncertainty, and one by estimating the extreme values of the components of the admissible models. Each model of these families identifies a reference trajectory, which is parametrized by design variables that are common to all the models. The design variables are hence determined by imposing that all the reference trajectories fulfill the required constraints. Discussions on the convergence and robustness of the proposed strategy are provided, in particular showing that the satisfaction of the visibility and workspace constraints for the second family ensures the satisfaction of these constraints for all models bounded by this family. The proposed strategy is illustrated through simulations and experiments. © 2011 IEEE.published_or_final_versio

Robust and Cooperative Image-Based Visual Servoing System Using a Redundant Architecture

The reliability and robustness of image-based visual servoing systems is still unsolved by the moment. In order to address this issue, a redundant and cooperative 2D visual servoing system based on the information provided by two cameras in eye-in-hand/eye-to-hand configurations is proposed. Its control law has been defined to assure that the whole system is stable if each subsystem is stable and to allow avoiding typical problems of image-based visual servoing systems like task singularities, features extraction errors, disappearance of image features, local minima, etc. Experimental results with an industrial robot manipulator based on Schunk modular motors to demonstrate the stability, performance and robustness of the proposed system are presented

Robust Kalman Filtering Cooperated Elman Neural Network Learning for Vision-Sensing-Based Robotic Manipulation with Global Stability

Fujian Provincial Natural Science Foundation of China [2010J05141]In this paper, a global-state-space visual servoing scheme is proposed for uncalibrated model-independent robotic manipulation. The scheme is based on robust Kalman filtering (KF), in conjunction with Elman neural network (ENN) learning techniques. The global map relationship between the vision space and the robotic workspace is learned using an ENN. This learned mapping is shown to be an approximate estimate of the Jacobian in global space. In the testing phase, the desired Jacobian is arrived at using a robust KF to improve the ENN learning result so as to achieve robotic precise convergence of the desired pose. Meanwhile, the ENN weights are updated (re-trained) using a new input-output data pair vector (obtained from the KF cycle) to ensure robot global stability manipulation. Thus, our method, without requiring either camera or model parameters, avoids the corrupted performances caused by camera calibration and modeling errors. To demonstrate the proposed scheme's performance, various simulation and experimental results have been presented using a six-degree-of-freedom robotic manipulator with eye-in-hand configurations