A Feedback Motion Plan for Vehicles with Bounded Curvature Constraints The use of a feedback motion plan instead of the decoupled scheme consisting of separate plan and control phases can facilitate the task of proving the properties of an autonomous system. The advantage of using a feedback motion plan is the possibility to validate the whole plan offline before its execution, which means that trajectories having different initial states can be tested simultaneously. In this paper, we formulate a feedback motion plan based on the extension of the wavefront expansion to the case of vehicles having bounded curvature. Additionally, the use of a transition function and a Gaussian filter limits undesired oscillations in the resultant trajectories. The method is suitable for both single goal missions and path following. The paper illustrates the algorithm for the generation of the plan and presents simulation data containing example trajectories and analysis of tuning parameters. Finally, future developments are discussed.

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.