Adaptive Hybrid Visual Servo Regulation of Mobile Robots Based on Fast Homography Decomposition

For the monocular camera-based mobile robot system, an adaptive hybrid visual servo regulation algorithm which is based on a fast homography decomposition method is proposed to drive the mobile robot to its desired position and orientation, even when object’s imaging depth and camera’s position extrinsic parameters are unknown. Firstly, the homography’s particular properties caused by mobile robot’s 2-DOF motion are taken into account to induce a fast homography decomposition method. Secondly, the homography matrix and the extracted orientation error, incorporated with the desired view’s single feature point, are utilized to form an error vector and its open-loop error function. Finally, Lyapunov-based techniques are exploited to construct an adaptive regulation control law, followed by the experimental verification. The experimental results show that the proposed fast homography decomposition method is not only simple and efficient, but also highly precise. Meanwhile, the designed control law can well enable mobile robot position and orientation regulation despite the lack of depth information and camera’s position extrinsic parameters

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

Vision based leader-follower formation control for mobile robots

Creating systems with multiple autonomous vehicles places severe demands on the design of control schemes. Robot formation control plays a vital role in coordinating robots. As the number of members in a system rise, the complexity of each member increases. There is a proportional increase in the quantity and complexity of onboard sensing, control and computation. This thesis investigates the control of a group of mobile robots consisting of a leader and several followers to maintain a desired geometric formation --Abstract, page iii

An Investigation of Nonlinear Estimation and System Design for Mechatronic Systems

This thesis is a collection of two projects in which the author was involved during his master\u27s degree program. The first project involves the estimation of 3D Euclidean coordinates of features from 2D images. A 3D Euclidean position estimation strategy is developed for a static object using a single moving camera whose motion is known. This Euclidean depth estimator has a very simple mathematical structure and is easy to implement. Numerical simulations and experimental results using a mobile robot in an indoor environment are presented to illustrate the performance of the algorithm. The second section describes the design of a test system for the Argon Environment Electrical Study (AEES) conducted by the Department of Energy (DOE). The initial research proposal, safety review, and literature review are presented. Additionally, the test plan and system design are highlighted