Nonlinear Model Predictive Control of UAVs for Optimal Path Planning and Obstacle Avoidance

In this paper, Nonlinear Model Predictive Control (NMPC) of an Unmanned Aerial Vehicle (UAV) is used to avoid static obstacles. To reach this objective, a quaternion-based model of a UAV with 6 Degrees of Freedom (6-DoF) is provided. The control effort, as well as a quadratic form of states, are considered as the cost function. Obstacles are introduced as optimization constraints besides some general and stability constraints. Two different scenarios are investigated; one illustrates trajectory tracking with an obstacle on the way while the other contains a path planning of the UAV in a hostile environment containing a cube and a sphere. The performance of the controller, both in processing time and path tracking, is promising because of the OpEn framework, the engine utilized here with PANOC-based algorithm.