Control Barrier Function-based Predictive Control for Close Proximity Operation of UAVs Inside a Tunnel

This study introduces a control strategy for Un-manned Aerial Vehicles (UAVs) performing high-precision proximity tasks in restricted tunnel environments, enabling them to conduct detailed inspections and navigate through extremely narrow tunnel corridors. The primary challenge in these tasks lies in managing nonlinear aerodynamic forces and torques induced by the tunnel walls while ensuring safe and efficient UAV operation in close proximity to these boundaries. To tackle this issue, we propose a novel approach that integrates Model Predictive Control (MPC) with modified Control Barrier Function (CBF) constraints. This framework is designed to achieve dual objectives: ensuring a safe operational distance from walls to mitigate their aerodynamic effects, while simultaneously minimizing distance to the walls to effectively perform close-proximity operations. Our approach demonstrates significant improvements, reducing the safe hovering distance from walls by 37% and decreasing UAV power consumption by approximately 15% when flying near ground and ceiling surfaces. The efficacy of the proposed method is rigorously validated through comprehensive simulations, which evaluate various close-proximity UAV trajectories under realistically modeled aerodynamic disturbances induced by the tunnel boundaries.