Using Visual Sensors and Machine Learning For Collision Sensing and Avoidance

In manned aviation, humans act in the command, communication, control and intelligence loop to enable safe flight operations. Aircrafts employ several onboard sensors that work to keep the aircraft from colliding with other aircrafts in the air. Automatic Dependent Surveillance-Broadcast (ADS-B) is an example a sensor system that allows both manned and unmanned aerial vehicles to broadcasts their location to other aircrafts and ground control crews. There is a push into integrating ADS-B into as part of the suite if sensors on unmanned aerial vehicles especially as the commercial industry prepares for regulatory approvals (AUVSI, 2008) for beyond visual Iine of site (BVLOS) operation of UAVs.

Towards safe automatic or autonomous flight operation, including remotely controlled operation, more onboard decision making will be needed to keep UAVs safe in the air. Specifically, as UAVs are operated BVLOS and especially beyond the reach of terrestrial cellular networks, it becomes more important for UAVs to be able to sense and make decision about traffic and objects arounds it without the constant prompting from a ground station. To this end, Iris Automation is developing a visual-based sensor that allows a UAV to sense and identify other aircrafts and objects (including birds) in its flight path and ultimately avoid collisions. The sensor uses RGB cameras to gather images of the environment and with the help of onboard computing included in the sensors pack, it employs machine learning algorithms to make sense of its environment.

Sawyers, P. (2018, January). Iris Automation raises $8 million to help autonomous flying machines avoid collisions. Retrieved from

AUVSI. (2018, January). uAvionix ping200S Mode S transponder receives fcc approval to install on uas, balloons and gliders. Retrieved from

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