Distributed Task Allocation for Heterogeneous Agricultural UAV Swarms: An Event-Triggered Potential Game with Inertia

Abstract

Heterogeneous unmanned aerial vehicle (UAV) coordination in precision agriculture is increasingly challenged by severe bandwidth constraints and limited computational resources. Existing distributed algorithms often suffer from prohibitive signaling overhead in dynamic environments, while event-triggered mechanisms in discrete strategy spaces are prone to instability and decision oscillations. To address these issues, this paper proposes the Event-Triggered Best Response with Inertia (ET-BRI) algorithm. We model multi-UAV interactions as a congestion game and establish its equivalence to an exact potential game (EPG), providing a rigorous theoretical foundation for coordination. Within this framework, ET-BRI utilizes a deterministic inertia threshold $\lambda$ to filter marginal utility gains, effectively pruning redundant signaling and dampening oscillations. Extensive simulations over 100 Monte Carlo trials involving dynamic tasks show that ET-BRI achieves a robust completion count of $47.3 \pm 1.1$, matching the performance of complex D-MPC and TVCBLL algorithms. Notably, it maintains a low computational latency (4.85 ms) while reducing communication frequency by over 98%, validating ET-BRI as a scalable and resource-efficient solution for bandwidthconstrained field operations.