Robust Communication-Aware Jamming Detection and Avoidance for UAS Swarms

Unmanned autonomous system (UAS) swarms empower groups of robotic agents (such as drones) to collectively accomplish complex missions by navigating intricate environments, but face significant challenges when operating in contested areas with electromagnetic jamming that disrupts interagent communication. This paper introduces an innovative hierarchical control strategy that enables a drone swarm to traverse complex terrains, including regions compromised by electromagnetic jamming intended to disrupt communications. The swarm is initially unaware of the existence or location of these jamming regions, making autonomous detection and avoidance critical for mission success. The proposed strategy synergistically combines communication-aware formation control, particle swarm optimization (PSO) for reactive navigation, and a global path planner for deadlock resolution to ensure the swarm maintains optimal communication quality and mission continuity. A gradient controller maintains the desired swarm formation by maximizing communication quality among agents, while a PSO-based movement controller navigates toward a destination, avoiding newly discovered jamming areas. To address potential deadlocks, a path planning algorithm is invoked to generate a globally optimal route. The framework operates without prior knowledge of jamming locations, learning threat positions through real-time environmental interaction. A comparative analysis of various path planning algorithms, including A*, Greedy Best-First Search, Breadth-First Search, Dijkstra, Jump Point Search, and Theta*, is presented through simulation. The results demonstrate that A* provides optimal performance for our integrated framework, facilitating efficient and robust navigation for the UAS swarms, while maintaining high communication quality among individual drones.