Learning Sensor Based Risk Map Augmentation for Risk Aware UAS Operation

Abstract

Unmanned Aerial Systems (UAS) have become increasingly popular and have been identified as a good platform for a range of tasks from surveillance and inspection to delivery and maintenance. In many of these applications these systems have to operate in environments that are frequented by people or that contain sensitive infrastructure and in which the operation of UAS thus poses physical risk in terms of damage in case of vehicle failure or psychological or privacy risks which would make their operation less acceptable. To increase the use of these systems it is thus important that they can take into account these risks when determining navigation strategies. While this can sometimes be done based on prior information, such as street and building plans in cities, a priori information is often not complete, making it essential that risk representations can be augmented in real time based on sensor information. This paper presents an approach to risk map augmentation that uses learned risk identification from aerial pictures to fuse additional information with prior data into a dynamically changing risk map that allows effective re-planning of navigation strategies.