A Novel Approach for Parameter Extraction of an NMPCbased 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 nonhomogeneous 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 abovementioned 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.

Obstacle Avoiding Path Following based on Nonlinear Model Predictive Control using Artificial Variables This work presents a model predictive formulation for obstacle avoiding path following control for constrained vehicles. The obstacles are introduced as soft constraints in the value function, in order to maintain the convexity of state and output spaces. In this formulation, the path following and obstacle avoidance tasks may introduce local minima solutions due to their competing costs known as corner conditions. In order to address this problem, a heuristic switch in the form of additional decision variables is introduced into the cost function. The proposed solution is based on an extension of Model Predictive Control (MPC) by using Artificial Variables. An additional cost term is included in order to prevent early stops in the path following task. Simulations results considering an autonomous vehicle subject to input constraints are carried out to illustrate the performance of the proposed control strategy.