Obstacle avoidance-driven controller for safety-critical aerial robots
This work addresses safety-critical control for aerial robots like quadrotors, offering an incremental improvement over existing methods.
The paper tackled the problem of obstacle avoidance for aerial robots by proposing a novel Model-Predictive-Control-Barrier-Function (MPCBF) that combines Control Barrier Functions (CBF) with Model Predictive Control (MPC), resulting in improved performance where the quadrotor successfully avoided obstacles that CBF failed to handle due to obstacle speed.
The goal of this thesis is to propose the combination of Control-Barrier-Functions (CBF) with Model-Predictive-Control (MPC) resulting in the novel Model-Predictive-Control-Barrier-Function (MPCBF). It can be shown, that the performance of the MPCBF surpasses the performance of the CBF due to the increased time horizon of the MPC. Moreover, the MPCBF was applied to a quadrotor, a system strongly in need of fast and predictive control. Using the MPCBF, the quadrotor was able to avoid obstacles, which the CBF failed to avoid due to the relative speed of the obstacle. The results of this work are experimentally validated.