Topology-Safe Air-to-Air Interception via “Go”-Inspired Metrics and Multi-Agent Reinforcement Learning
Year: 2026
Type: article
Abstract: Airspace defense against agile UAVs requires multiple defenders to cooperatively encircle evasive intruders, but existing formation controllers cannot certify when an intruder is truly trapped. This paper introduces a “Go”-inspired framework that maps the board-game concept of liberties to continuous-space escape corridors, yielding a Benson-inspired dual capture criterion: total liberty below a threshold AND sufficient directions blocked. A directional relevance gating mechanism enforces that interceptors behind the evader cannot contribute to blocking, naturally requiring multi-agent cooperation. The metrics feed a MAPPO-based learning layer with curriculum-scheduled capture thresholds under centralized training/decentralized execution. In 3-vs.-1 simulations with five independent seeds, trained interceptors achieve a 52.9±3.7% capture rate under deterministic evaluation against a heuristic evader under the strict multi-directional criterion, with evader liberty reduced to 0.36 and 5.1/8 directions blocked.
Source: Aerospace
Institution: National University of Singapore
Language: English
Topic: Guidance and Control Systems
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7171554635