SITL Validation of Federated Reinforcement Learning for Priority‐Based Dynamic Task Allocation in UAV Swarms

ABSTRACT

The deployment of reinforcement learning (RL) controllers for unmanned aerial vehicle (UAV) swarm navigation has been extensively studied in abstract simulation environments. However, a critical validation gap persists between algorithmic development and operationally representative testing under realistic flight dynamics. This work presents a Software‐In‐The‐Loop (SITL) validation of a federated reinforcement learning (FRL) framework for multi‐UAV swarm navigation, implemented using ArduPilot flight controller stack integrated with the Gazebo Harmonic physics simulator and orchestrated through a Python‐based middleware architecture that connects asynchronous RL policy inference with real‐time MAVLink communication. Federated Proximal Policy Optimization (PPO) framework is tested across four progressively complex operational scenarios: (i) single‐agent point‐to‐point navigation (96.0% success rate, path efficiency ), (ii) five‐UAV leader‐follower formation flight (94.0% success rate, zero inter‐agent collisions, mean formation error  m), (iii) dynamic mid‐mission target reassignment requiring 143° heading correction (94.0% success rate, mean redirection time  s) and (iv) priority‐based dynamic task allocation, wherein a secondary target with assigned priority emerges mid‐mission, triggering autonomous priority‐proportional swarm partitioning (90.0% dual‐target success rate, mean reallocation time  s). Across all scenarios, the framework maintained a near‐zero inter‐agent collision rate (0.0% in Scenarios 2 and 4; a single incident in Scenario 3) and total federation communication overhead at or below 1.50 MB per episode, with sub‐group policy divergence () confirming effective knowledge preservation under split‐swarm operations. The results demonstrate that federated reinforcement learning constitutes a viable control paradigm for UAV swarms operating under realistic flight constraints, establishing quantitative SITL benchmarks for future sim‐to‐real transfer efforts.