Control Barrier Function-based Predictive Control for Close Proximity operation of UAVs inside a Tunnel

TL;DR

This paper presents a Control Barrier Function-based Model Predictive Control method to safely and effectively operate UAVs in tunnels, managing nonlinear forces and ensuring proximity safety through simulation testing.

Contribution

It introduces a novel CBF-based MPC framework specifically designed for close proximity UAV operation inside tunnels, addressing nonlinear wall effects and safety constraints.

Findings

Successfully maintains safe distances from tunnel walls.

Effectively manages nonlinear forces during close proximity flight.

Demonstrates robustness in simulation with aerodynamic disturbances.

Abstract

This paper introduces a method for effectively controlling the movement of an Unmanned Aerial Vehicle (UAV) within a tunnel. The primary challenge of this problem lies in the UAV's exposure to nonlinear distance-dependent torques and forces generated by the tunnel walls, along with the need to operate safely within a defined region while in close proximity to these walls. To address this problem, the paper proposes the implementation of a Model Predictive Control (MPC) framework with constraints based on Control Barrier Function (CBF). The paper approaches the issue in two distinct ways; first, by maintaining a safe distance from the tunnel walls to avoid the effects of both the walls and ceiling, and second, by minimizing the distance from the walls to effectively manage the nonlinear forces associated with close proximity tasks. Finally, the paper demonstrates the effectiveness of its approach through testing on simulation for various close proximity trajectories with the realistic model of aerodynamic disturbances due to the proximity of the ceiling and boundary walls.