Obstacle avoidance-driven controller for safety-critical aerial robots

TL;DR

This thesis introduces MPCBF, a novel control method combining Model-Predictive-Control and Control-Barrier-Functions, enhancing obstacle avoidance for quadrotors in safety-critical scenarios through experimental validation.

Contribution

It proposes the MPCBF, integrating MPC with CBF for improved obstacle avoidance, especially in fast-moving environments, validated on quadrotor experiments.

Findings

MPCBF outperforms CBF in obstacle avoidance.

MPCBF successfully applied to quadrotors in real experiments.

Enhanced safety and predictive capabilities demonstrated.

Abstract

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.