Obstacle Avoidance onboard MAVs using a FMCW RADAR

Abstract

Micro air vehicles (MAVs) are increasingly being considered for aerial tasks such as delivery of goods and surveillance due to their lightweight, compact design and manoeuvrability. To safely and reliably carry out these tasks and navigate to its objective, especially in complex and cluttered environments, the MAV is also required to sense and avoid (S&A) obstacles. Due to the MAVs limitations in weight, power and processing power, vision systems usually prove ideal for sensing the environment, being a cheap, lightweight, power efficient and a rich source of information. They do however require adequate computational resources and most importantly, good visibility. When the environment does not host these conditions, for instance when flying though dust, smoke or fog, other sensors need to be utilised that can provide more robust sensing to ensure safe and reliable operation. Radar sensors are mostly unaffected by atmospheric conditions and have been used extensively in the aerospace industry for this purpose. These sensors were traditionally heavy and power hungry, only applicable on ground or in large craft. However other radar sensors have since come about that are more suited for use in small MAVs. Specifically, lightweight, power efficient and compact frequency modulated continuous wave (FMCW) radars have increasingly been used in advanced driver assistance systems as auxiliary sensors, however there has been little work to integrate them on MAVs. This sensor provides the range, horizontal bearing and radial velocity (Doppler shift) of any objects in the field of view, which can then be used for multi-target tracking (MTT) [38]. The major disadvantage of the sensor is the limited field of view (approximately 80 degrees horizontal) and noisy nature of the sensor, especially in cluttered environments. The challenge is to explore filtering, tracking and avoidance algorithm pipelines to extract meaningful information from the raw data and investigate the sensor’s effectiveness with respect to obstacle avoidance on MAVs. This will include algorithms such as data association, estimation and avoidance, as well as an investigation of neural networks to aid in processing the raw data and provide some filtering. This will be accomplished by integrating the sensor on a MAV and testing and tuning the algorithms both in real life (in the cyberzoo flying arena of the aerospace faculty), and using data gathered as part of an obstacle detection and avoidance dataset that was generated during this project. This will hopefully allow MAVs to operate safer, either using a standalone radar or integrated with other sensors.