Obstacles avoidance for autonomous marine vehicles based on the model predictive control

Obstacle avoidance of autonomous vehicles based on model predictive control

This paper presents an obstacle avoidance scheme for autonomous vehicles as an active safety procedure in unknown environments. Safe trajectories are generated using the non-linear model predictive framework, in which the simplified dynamics of the vehicle are used to predict the state of the vehicle over the look-ahead horizon. To compensate for the slight dissimilarity between the simplified ...

Threat-based hazard avoidance for semi-autonomous vehicles using nonlinear model predictive control

A Nonlinear Model Predictive Control Algorithm for Obstacle Avoidance in Autonomous Ground Vehicles within Unknown Environments

A nonlinear model predictive control algorithm is developed for obstacle avoidance in high-speed, large-size autonomous ground vehicles (AGVs) that perceive the environment only through information provided by on-board sensors. The mission of the AGV is to move from its initial configuration to the goal configuration safely. The resulting trajectory should be collision-free and the AGV should b...

Terrain Avoidance Model Predictive Control for Autonomous Rotorcraft

This paper presents a terrain avoidance control methodology for autonomous rotorcraft applied to low altitude flight. A model predictive control formulation is used to adequately address the terrain avoidance problem, which involves stabilizing a nonlinear highly coupled dynamic model, while avoiding collisions with the terrain and preventing input and state saturations. Computing the model pre...

Nonlinear Model Predictive Control For Autonomous Vehicles ~ jleBooks.com

Christian Kirches develops a fast numerical algorithm of wide applicability that efficiently solves mixed-integer nonlinear optimal control problems. He uses convexification and relaxation techniques to obtain computationally tractable reformulations for which feasibility and optimality certificates...