Work

Resilient multi-agent reinforcement learning for centralised tactical conflict resolution under uncertain perturbations and non-cooperative traffic in urban air mobility

Year: 2026

Type: article

Abstract: This research investigates tactical conflict resolution for Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) operations under degraded conditions and in the presence of non-cooperative UAS/UAM and manned Commercial Air Transportation and General Aviation (CAT/GA) intruders. The study adopts a centralised safety-net approach within UAS Traffic Management (UTM) architectures, envisioning ground-based conflict resolution services. We propose a set of Tactical Conflict Resolution Solvers (TCRS), each built upon a Multi-Agent Reinforcement Learning (MARL) core using a shared-policy transformer architecture and executed in a decentralised manner. To assess resilience of TCRS variants, we introduce domain-specific perturbations, including positioning noise, communication loss, and sensor-related defects. The TCRS operates with partial decision-making ability in non-cooperative traffic environments, while the perturbation model increases realism by simulating varying degrees of information availability. Results show that the perturbation-trained models achieve substantial safety gains compared with the baseline TCRS trained in ideal conditions. The most resilient variant; trained under multi-perturbation exposure and evaluated in non-cooperative environments, achieves a threefold reduction in critical safety violations compared with the baseline and remains robust under mixed cooperative/non-cooperative traffic with static intent. It exhibits a modest vulnerability under fully homogeneous non-cooperative scenarios with dynamic intent. Simulations involving concurrent CAT/GA and UAS operations further indicate that integrating UAS operations within the existing airspace classification remains hazardous for ground-based tactical conflict resolution when constrained by short look-ahead horizons and insufficient time to react.

Source: Transportation Research Part C Emerging Technologies

DOI: 10.1016/j.trc.2026.105542

Authors: Rodolphe Fremond, Yan Xu, Junjie Zhao, Antonios Tsourdos, Gokhan Inalhan

Institutions: École Nationale de l’Aviation Civile, Cranfield University, Beihang University

Language: English

Topic: Air Traffic Management and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: hybrid

Estimated APC paid: $4,040