Sense and Avoid: LOAWS

Unmanned Aerial Vehicles (UAV) require for low-level flights activities, sensors that can effectively operate in areas where natural and man-made obstacles are present. The sensors must be reliable even when meteorological conditions do not make possible for the human eye to differentiate between obstacles or to even identify the obstacle, being this the ultimate purpose of the sensor. The sensor(s) to be used under such circumstances have to be able to detect all types of hazardous obstacles, including topographic features, vegetation, buildings, poles/masts, towers, cables and transmission lines. It must count with satisfactory technological readiness levels. A high range and bearing resolution will be indispensable and high minimum detection range will be adequate for the platform velocity and dynamic performances.

A sensor that can meet such requirement could be the LIDAR Obstacle Warning and Avoidance System (LOWAS), which has become a mature technology that small-to- medium size UAV’s resource to when their operations require close proximity to the ground.  LOWAS is of preference when warning and avoidance of obstacles is necessary. It is important to mention that LIDAR is an exteroceptive sensor. When we talk about sensors, it is essential to differentiate between proprioceptive and exteroceptive sensors. Proprioceptive sensors are those that measure internal state of a system, it could be battery level or even the performance of gyroscopes. On the other hand, exteroceptive sensors measure the external state of systems, this could be temperature, meteorological conditions, or detection of objects between others.

LOAWS is suitable for this mission because it able to detect obstacles placed in the UAV route of flight or nearby the operation area. LOWAS has the capability to classify and the same time prioritize the obstacles that were detected. It can also provide visual and aural warnings and information to the crew (Ramasamy, 2016). Therefore, LOWAS counts with three key algorithms: prediction of the future platform trajectory; calculation of the potential collisions with the detected obstacles; and generation of a set of optimal avoidance trajectories (in case a risk of collision is determined) (Ramasamy,2016). These algorithms are the key to how LOWAS will perform and also help to kind of predict how the UAV will behave in circumstances not favorable for the flight.

LOWAS goes beyond meeting the requirements.  It counts now with a cognitive remote pilot–aircraft interface that is being developed to dynamically assist remote pilots based on their physiological and cognitive states detected in real-time (Ramasamy, 2016). The purpose of this is to create a cooperation between pilots and the systems. I definitely recommend this system for warning and avoidance of obstacles, specially after working on the human and machine cooperation that will benefit the result of the mission to be carried by the UAV.

LOWAS also offers three levels of alerts.  They are: warning, caution, and advisory. These alerts help the remote pilot to prepare or be aware of the upcoming obstacles that will have to be evaded. These alerts come in the form of digital voice outputs and tone or they can also be displayed like is done in the chart presented below.

The article I have based my post about can be found in this website
https://www-sciencedirect-com.ezproxy.libproxy.db.erau.edu/science/article/pii/S1270963816301900

References

Kohanbash, D. (2014). Sensor types (modalities) for robots to experience the world. Robots for Roboticits. Retrieved from
http://robotsforroboticists.com/sensor-modalities/

Ramasamy, S. (2016). LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid. Science Direct. Retrieved from
 https://www-sciencedirect-com.ezproxy.libproxy.db.erau.edu/science/article/pii/S1270963816301900

Newcomb, D. (2017). Velodyne LIDAR for Safety's Sake. Cthreegroup. Retrieved from
 http://cthreegroup.com/velodyne-lidar-for-safetys-sake/