A Unified NMPC Scheme for MAVs Navigation with 3D Collision Avoidance under Position Uncertainty

TL;DR

This paper introduces a real-time, robust NMPC framework for MAV navigation that integrates 3D collision avoidance using geometry-based constraints and accounts for localization uncertainty, validated through Gazebo simulations.

Contribution

It presents a novel, efficient subspace clustering method for 3D point cloud analysis and incorporates this into NMPC to enhance collision avoidance under localization uncertainties.

Findings

Effective collision avoidance in simulated environments.

Robustness to localization uncertainties demonstrated.

Maintains real-time performance during navigation.

Abstract

This article proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in constrained environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs and guarantees real-time performance. Our first contribution is to design a computationally efficient subspace clustering method to reveal from geometrical constraints to underlying constraint planes within a 3D point cloud, obtained from a 3D lidar scanner. The second contribution of our work is to incorporate the extracted information into the nonlinear constraints of NMPC for avoiding collisions. Our third contribution focuses on making the controller robust by considering the uncertainty of localization and NMPC using the Shannon entropy. This step enables us to track either the position or velocity references, or none of them if necessary. As a result, the collision avoidance constraints are defined in the local coordinates of MAVs and it remains active and guarantees collision avoidance, despite localization uncertainties, e.g., position estimation drifts. Additionally, as the platform continues the mission, this will result in less uncertain position estimations, due to the feature extraction and loop closure. The efficacy of the suggested framework has been evaluated using various simulations in the Gazebo environment.