Print Email Facebook Twitter Application of Control Barrier Functions to Collision Free Model Predictive Control Title Application of Control Barrier Functions to Collision Free Model Predictive Control: Robust UAV Trajectories with MPC-CBF and Euclidean Signed Distance Fields Author de Vries, Rinto (TU Delft Aerospace Engineering) Contributor Smeur, E.J.J. (mentor) Horstink, Thomas (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2023-10-10 Abstract Recent literature in real-time trajectory planning has proposed using Control Barrier Functions (CBFs) as collision constraints in Model Predictive Control (MPC) for efficient guidance, a concept referred to as MPC-CBF. This concept has been explored for both first and second-order CBFs. However, these approaches relied on an analytical description of the environment. Building upon this, we propose combining MPC-CBF with Euclidean Signed Distance Fields (ESDFs), eliminating the need for such an analytical model of the environment. Notably, we extend this approach to a new field by applying it to Unmanned Aerial Vehicles (UAVs). Through simulations, we compare flown trajectories and noise robustness for distance constraints, first-order CBF constraints and second-order CBF constraints. First-order CBF constraints outperform distance constraints, excelling in path planning and noise resilience. Second-order CBF constraints face challenges due to numerical approximations of the hessian of the ESDF and stricter dependency on an accurate acceleration model, limiting their practicality for UAVs. The proposed control framework was tested by safely maneuvering an enterprise inspection drone around a Boeing 787-9 aircraft inside an aircraft hangar, confirming its effectiveness in collision avoidance and real-world scenarios. Subject mavmpcmavlabmodel predictive controlcbfcontrol barrier functiontrajectory planningesdfeuclidean signed distance fielduavdronecollision avoidanceobstacle avoidance To reference this document use: http://resolver.tudelft.nl/uuid:6320374a-b84d-4bbf-be48-10cee914b9e0 Part of collection Student theses Document type master thesis Rights © 2023 Rinto de Vries Files PDF final_thesis_report_rinto.pdf 8.68 MB Close viewer /islandora/object/uuid:6320374a-b84d-4bbf-be48-10cee914b9e0/datastream/OBJ/view