This paper describes a learning-based strategy for selecting conflict avoidance maneuvers for autonomous unmanned aircraft systems. The selected maneuvers are provided by a formally verified algorithm and they are guaranteed to solve any impending conflict under general assumptions about aircraft dynamics. The decision-making logic that selects the appropriate maneuvers is encoded in a stochastic policy encapsulated as a neural network. The network’s parameters are optimized to maximize a reward function. The reward function penalizes loss of separation with other aircraft while rewarding resolutions that result in minimum excursions from the nominal flight plan. This paper provides a description of the technique and presents preliminary simulation results.
A Learning-Based Guidance Selection Mechanism for a Formally Verified Sense and Avoid Algorithm
Digital Avionics Systems Conference (DASC) ; 2019 ; San Diego, CA, United States
2019-09-08
Conference paper
No indication
English
Software safety architecture that can be formally verified
Automotive engineering | 2004
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