Subterranean MAV Navigation based on Nonlinear MPC with Collision Avoidance Constraints

TL;DR

This paper introduces a nonlinear model predictive control approach for subterranean MAV navigation that ensures collision avoidance by integrating obstacle kinematics and a heading correction method, validated through underground mine trials.

Contribution

It presents a novel NMPC-based navigation framework with collision avoidance constraints and a new heading correction technique for subterranean MAVs.

Findings

Successful underground mine navigation demonstrated in field trials.

Effective collision avoidance using 2D lidar measurements.

Improved heading alignment towards tunnel center.

Abstract

Micro Aerial Vehicles (MAVs) navigation in subterranean environments is gaining attention in the field of aerial robotics, however there are still multiple challenges for collision free navigation in such harsh environments. This article proposes a novel baseline solution for collision free navigation with Nonlinear Model Predictive Control (NMPC). In the proposed method, the MAV is considered as a floating object, where the velocities on the $x$, $y$ axes and the position on altitude are the references for the NMPC to navigate along the tunnel, while the NMPC avoids the collision by considering kinematics of the obstacles based on measurements from a 2D lidar. Moreover, a novel approach for correcting the heading of the MAV towards the center of the mine tunnel is proposed, while the efficacy of the suggested framework has been evaluated in multiple field trials in an underground mine in Sweden.