# Aerial navigation in obstructed environments with embedded nonlinear model predictive control

## TL;DR

This paper introduces a real-time nonlinear model predictive control approach for autonomous aerial navigation and obstacle avoidance in complex environments, demonstrated on a micro aerial vehicle with embedded computation.

## Contribution

It presents a novel NMPC methodology using PANOC for fast, embedded obstacle avoidance in non-convex environments, with a simple battery depletion compensation method.

## Key findings

- NMPC with PANOC runs at 20Hz onboard MAV.
- The MAV successfully navigates around obstacles smoothly.
- Thrust compensation improves autonomy over time.

## Abstract

We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAV) using nonlinear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC problem at a rate of 20Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant.