Avoiding Drone-Related Mid-Air Collisions
By: Jon Damush
Tags: Drones4good, Flysafe, Drone Week, Drones, Machine Learning

Avoiding Drone-Related Mid-Air Collisions

Crowded airspace via the Iris Automation in-house simulator

It has been just over 100 years since the Wright Brothers flew their infamous first flight. In those 100 years, we as a society have enjoyed the benefits of flight across multiple use cases. Air travel is the most obvious, but cargo and logistics aircraft make ‘next day’ deliveries possible as well. Agricultural yields are improved by aircraft spraying crops. Critical infrastructure is kept in top condition because of inspections that happen from the air at regular frequencies. As the world explored and perfected these various use cases, the number and variety of aircraft in the skies has proportionally increased. To give a sense of the scale, in 2019 (just prior to the COVID-19 pandemic), the average number of daily airline flights in the United States was over 45,000. Add to that the non-airline flights that include business aviation, law enforcement, and general aviation and you start to see a picture of increasingly crowded skies.

As we look ahead to the pending increase of air traffic due to unpiloted systems, ie drones, the community has been working to envision and create a system where the increased traffic does not meaningfully increase the mid-air collision risk between drones or worse, between a drone and a piloted or passenger carrying aircraft. The engineers among us (guilty as charged) immediately start thinking about how to solve the problem with technology. You can hear us now saying ‘there has to be a way to do that perfectly…’ But the most likely way forward is not dissimilar to the way aircraft remain separated in today’s piloted aircraft world.

It is useful to understand how the current system works so we can see how the future system might work. To those of us who have been trying to solve this problem for the last few years, this will not be groundbreaking at all, but to those of us who are relatively new to the aviation community, I hope this might help — if for no other reason than to give us a common lexicon for the ideation and discussion of new concepts.

The current airspace system

The ugly truth is that the current system does not prevent all mid-air collisions from happening. According to the Nall report, we still have a ‘single digit’ number of mid-air collisions in the USA each year, typically accounting for 10–20 fatalities. Considering the number of daily flights, however, this is an impressive feat. So how does it work?

There is essentially a ‘stack’ of layers of mitigation that operate in conjunction with one another to filter out the collision risk.

First of all, there is no single piece of technology that keeps aircraft separated. Instead, we employ a system of systems and multiple layers of mitigation to avoid mid-air collisions. It starts with planning. The way a flight is planned, the altitudes chosen, the route plotted, the airspace transited all contribute to keeping one’s own airplane (what we call the ‘ownship’) away from other traffic and terrestrial and environmental hazards (i.e. the ground and weather). Once we get in the airplane, we use a variety of tactical methods to avoid other aircraft, including talking to air traffic control, using ADS-B, ground-based radars, and transponder equipped aircraft. Finally, we come to the last line of defense — the human pilot. There is essentially a ‘stack’ of layers of mitigation that operate in conjunction with one another to filter out the collision risk. But no system is perfect, and that is why FAR part 91.113(b) states “The pilot….shall see and avoid other aircraft.”

So what happens when there is no ‘pilot’ on board?

We have effectively lost our last line of defense. The good news is that we have the ability through regulation, technology, and collaboration with each other to have all drones be considered ‘cooperative.’ That means that they report where they are and what their intentions are so that all other users of the airspace are aware of where the drone is operating and what it plans to do. Systems like UTM (Unmanned Traffic Management) offer great promise to help the low altitude drone community avoid mid-air collisions due to the ‘cooperative’ nature of the drones.

Our main challenge comes from the desire to start performing use cases that push the flight plan outside of the boundaries of the current regulations, namely Part 107 in the USA, which allows for commercial drone operations below 400 feet above the ground, within visual line of sight, and a host of other restrictions. If you think of applications like long-linear infrastructure inspections, large area mapping, search and rescue, logistics, and passenger transit you can start to see where this new class of aircraft offers value and needs to fly ‘beyond the visual line of sight’ (BVLOS). Quick aside: there is a part of me that doesn’t like the acronym, as it implies that the desire is always to fly farther than someone can see, but perhaps the bigger economic value is that solving the BVLOS problem actually allows you to invert a critical ratio — the operator to aircraft ratio, which for drones right now is 1 (or more) to 1. That’s right, in many cases, operating a single drone requires more than one human operator. In my humble opinion, we will see rapid adoption of drone technology not because they can fly far, but rather because one person can fly multiple drones. This is where the economic value lies with robotic aircraft.

So as we push these aircraft into regimes where they will have to mix with piloted aircraft, why can’t we just do the same thing we do with piloted aircraft?

If you remember, it is the pilot in the aircraft that provides the last line of defense for collision avoidance in piloted aircraft operations. To date, we have not had an equivalent system on drones that can provide the same ‘last line of defense.’ This is precisely where Iris Automation is focusing its energy. We have worked very hard to develop a system that uses a combination of computer vision, artificial intelligence, and machine learning to detect, classify, and compute the relative position of an aircraft in flight. We call this system Casia, and we have successfully flown it on-board many different types of drones and even tested it on piloted aircraft. We have been collaborating with partners and regulators to explore how our system can be used in conjunction with other technologies and concepts of operations to mitigate the mid-air collision risk as well, if not better than we do today in piloted aircraft. The exciting part of our approach is that by using computer vision, we are able to use relatively low-cost, readily available, and small off the shelf hardware components. This helps us keep our size, weight and power (SWAP) low enough to be placed on-board the aircraft without negatively impacting that aircraft’s ability to perform its mission and carry its revenue-generating payloads. Improvements in performance in the future will be provided through software updates, including constantly improving our machine learning database, allowing for use in more diverse environments and detecting and classifying an ever-increasing number of aircraft and other obstacles.

So, how are we doing?

Pretty good I would say. We have achieved waiver approvals in Alaska, Kansas, South Africa, and Canada for BVLOS flight trials. While these are not yet to the point where we can ‘cut and paste’ the operation to unlock everyone’s commercial BVLOS operations, they are an important stepping stone and one that leads us into the FAA’s BEYOND program, where we are excited to continue to collaborate with regulators to explore additional use cases and expand the envelope of performance. Are we as good as humans yet? Not quite, but we are getting there fast. One thing to remember about our system — it does not get distracted, tired or bored of doing its job. It can see all of the airspace around it and is not occluded by cockpit structure, passengers, or cargo. Considering this, I believe we are a critical part of the system of systems that includes UTM, radar, ADS-B, and other strategic mitigations. Analogous to the human pilot, we are in a great position to provide the ‘last line of defense’ for collision avoidance to the drone community. At the end of the day, it is about raising the level of safety for unpiloted systems, and our goal is making sure our users never have a mid-air collision if they have a Casia system on-board.

Learn more about Casia at www.irisonboard.com/casia