A Novel Approach for Parameter Extraction of an NMPC-based Visual Follower Model

Images captured by visual sensors, such as cameras, with the goal of performing image based control, require processing for the extraction of useful information in the presence of imperfection of objects of the scene and restrictive environmental conditions. The problem of path following encounters these inconveniences, more precisely in the detection of the marks that represent the path to be followed. Handling faults along the path on non-homogeneous floors and extracting parameters, such as visual pose and curvature, accurately, are some of the difficulties encountered. In this article, a system of detection and extraction of parameters for the path following problem based on NMPC (Nonlinear Model Predictive Control), using computer vision techniques is proposed. To remedy the above-mentioned problems, the visual path is approximated by a quadratic function. The algorithm proposed here was embedded in Husky UGV (Unmanned Ground Vehicle) robot and compared with the original approach. Experimental results demonstrate the superiority of the proposed new algorithm.