Initial Review of Goal-Directed Answer Set Programming for Orchestration of Neuro-Symbolic UAV Autonomy

Autonomous unmanned aerial vehicles (UAVs) are capable of solving a variety of problems in environments where humans cannot easily go, such as military zones or search and rescue operations. Edge computing explores the trade-off between running software at the edge onboard a UAV (limited space for processing power and memory) versus on a server (increased latency and connectivity problems). The state-of-the-art solution for UAV path planning is the use of deep learning, however these systems are large and unexplainable. Furthermore, when these models need to be adjusted they often cannot be retrained on the edge. We propose the use of a powerful symbolic reasoner that can be run and modified on the UAV for path planning, making explainable and trustworthy edge autonomous systems. We evaluate the feasibility of this approach with a proof-of-concept symbolic UAV system based on the VECSR system for commonsense reasoning, which we call VECSR-A