Sense and Avoid Sensor Selection

            Sense and avoid is a critical piece of operating all types of aircraft, both manned and unmanned alike.  Unmanned aircraft however, do are not afforded the same flexibility and reliability of the human eye and brain to see obstacles and other traffic.  For this reason, unmanned aircraft must rely on complex sensors and computer algorithms to detect and avoid objects.  Small unmanned aerial systems pose a particular challenge to this requirement due to their small nature and low power requirements. Due to these limiting factors, when designing detect and avoid sensor packages for small unmanned aerial vehicles is that they must be size, weight, power and cost (SWaP-C) optimized, while still being able to provide top-notch detection and avoidance capabilities.  There are many approaches that can be taken and sensors available to create a detect and avoid system for small unmanned system such as light detection and ranging (LIDAR), ultrasonic sensors, electro-optical sensors (cameras), and even small radar systems.  Proper selection of these systems is important, as each has its pros, cons and limitations; making no one type a perfect all-around solution (Corrigan, 2018).

            One system that is currently being developed is the Iris Automation Collision Avoidance System (Figure 1).  The system, designed and built by Iris Automation utilizes computer vision (Figure 2) and advanced algorithms to detect obstacles such as aircraft, terrain and even wildlife.  Utilizing a monocular vision camera system and advanced algorithms that generate a highly detailed and robust computer vision image, the collision avoidance system can reliably detect, track and avoid both static and moving obstacles (Technology, n.d.).  Monocular vision sensors enable single images to be processed to create three-dimensional spatial reconstructions.  With the reconstructions distances can be determined by comparing real-time images captured by the camera to pictorial depth cues that are programmed into complex algorithms.  Monocular systems are commonly seen on small unmanned vehicle due to their low cost, weight and power requirements (Corrigan, 2018).

 

Figure 1: Iris Collision Avoidance System (Technology, n.d.)

 

Figure 2: Computer Vision Example (Technology, n.d.)

The system is designed to be highly modular and configurable so that users can incorporate varying numbers of cameras for up to 360-degree situational awareness.  Additionally, the system will be available as a software only package so that users can integrate 3^rd party sensors into the system via a seamless plug-and-play interface.  This allows the vision system to be fitted to nearly any type of small unmanned system including single and multi-rotor helicopters and fixed wing aircraft.  The collision avoidance system is designed to be rugged and is built to MIL Spec standards and has been tested in varying and extreme weather conditions and environments.  While the specific details of the size, weigh, power and cost are not yet available because the system is still in the design and testing phase, Iris Automation has promised that this system will be ultra-low SWAP-C optimized and the perfect addition for any small unmanned aerial vehicle (Technology, n.d.).

            To further prove the capabilities of the Iris Collision Avoidance System, several tests have been completed utilizing a life flight with a manned aircraft for the system to detect and avoid.  During one such test, the obstacle aircraft (manned Cessna 162) made three passes varying distances.  The first two passes were conducted at 150 meters and 200 meters respectively.  On each pass the detection system was able to not only detect and track the aircraft, but also determine its distance from the unmanned vehicle and provide a baseline classification of the size of the obstacle aircraft (Figure 3).  On the third pass, the obstacle aircraft can within approximately 500 meters of the unmanned vehicle.  During this pass the system was able to reliably detect and track the aircraft but was not able to classify the aircraft size.  This test proves the reliability of the system to detect and track even distant objects to ensure full awareness is realized (Technology, n.d.).

 

Figure 3: Example of what vision system sees.  Image captured of first pass at about 150 meters (Technology, n.d.)

            Overall the Iris Automation Collision Avoidance System is a perfect fit for almost all types of small unmanned aerial vehicles.  Its small size, weight and power requirements combined with its expected low cost will make it not only easy to integrate into all types of vehicles, but it will make it easily attainable to even the most entry level consumer in the drone industry and market.

References

Corrigan, F. (2018, June 17). Top Collision Avoidance Drones And Obstacle Detection Explained. Retrieved August 17, 2018, from https://www.dronezon.com/learn-about-drones-quadcopters/top-drones-with-obstacle-detection-collision-avoidance-sensors-explained/

Technology. (n.d.). Retrieved August 17, 2018, from https://www.irisonboard.com/technology/