Obstacle Avoidance onboard MAVs using a FMCW RADAR

TL;DR

This paper explores the use of miniature FMCW radar sensors on Micro Air Vehicles for obstacle avoidance in cluttered, poor visibility environments, demonstrating their potential to enhance autonomous navigation.

Contribution

It investigates the application of FMCW radar sensors on MAVs using signal processing techniques, showing their effectiveness in obstacle detection and avoidance in indoor environments.

Findings

Radar sensors outperform cameras in dust, fog, or smoke.

FMCW radar enables robust obstacle detection on MAVs.

Successful onboard implementation with improved navigation.

Abstract

Micro Air Vehicles (MAVs) are increasingly being used for complex or hazardous tasks in enclosed and cluttered environments such as surveillance or search and rescue. With this comes the necessity for sensors that can operate in poor visibility conditions to facilitate with navigation and avoidance of objects or people. Radar sensors in particular can provide more robust sensing of the environment when traditional sensors such as cameras fail in the presence of dust, fog or smoke. While extensively used in autonomous driving, miniature FMCW radars on MAVs have been relatively unexplored. This study aims to investigate to what extent this sensor is of use in these environments by employing traditional signal processing such as multi-target tracking and velocity obstacles. The viability of the solution is evaluated with an implementation on board a MAV by running trial tests in an indoor environment containing obstacles and by comparison with a human pilot, demonstrating the potential for the sensor to provide a more robust sense and avoid function in fully autonomous MAVs.