S&AS: FND: Safe Task-Aware Autonomous Resilient Systems (STAARS)

University Of Texas At Arlington, Arlington TX

Investigators

Abstract

Realizing the full potential of unmanned aerial systems (UAS) for commercial and societal benefits will call for autonomous UAS that must operate around people, especially urban/suburban areas, and respect safety, privacy, and regulatory concerns. This project will quantify the operations of UAS executing a task in urban/suburban environment as "risk" to these considerations, then develop dynamic risk assessment and guidance algorithms to compute least risky trajectories for the UAS to follow while executing a task. The project will produce knowledge on the proper autonomy level of UAS in urban operations, and will benefit FAA (Federal Aviation Administration) in UAS regulatory issues. The technological advances in this project will also contribute to multiple fields including autonomy, mobile networking, and intelligent control. The task & risk-aware decision-making framework developed in this project can be applied not only to UAS, but also to other unmanned systems on the ground and in/under water, with broad applications in smart health, transportation, and manufacturing domains. The PIs also expect that successful demonstrations of "safe" UAS operation in various risk conditions will increase the public's acceptance of UAS technology, and benefit broad commercial UAS use and job market. The project will also produce exciting learning and training opportunities for students and the community at large to learn UAS technologies. The project will use two raster maps to quantify risk: (1) PREM (Probability Risk Exposure Map) defines the risk of exposure of people and property on the ground to the presence of a UAS in the air as a function of position and time. (2) PURM (Probabilistic UAS Reachability Map) is the probability that the UAS can reach a position on the ground from its current position, computed based on the vehicle's capability (both nominal and diminished by possible failures) and environmental conditions such as wind. Defining the PURM by joint probability of reachability domains for nominal and all failure cases results in a resilient system, since decisions are made considering all possible operational modes. Trajectory planning in the PURM will use a modified, bidirectional, probabilistic RRT (Rapidly Expanding Random Tree) to efficiently, incrementally plan a set of trajectories that minimizes the overall risk. An autonomous decision algorithm then can keep the risk below an acceptable level as it guides the UAS in the successful completion of a given task. Because the safe operation of UAS also highly relies on effective communication among UAS and between UAS and a Command and Control Center, the project will also develop a decentralized dynamic communication schemes under different risk levels and mobility constraints.