平均更新率を調整する適応的イベントトリガ型モデル予測カバレッジ制御

原文アブストラクト

Model predictive coverage control for multi-agent systems operates under tight communication and computation budgets, which requires it to balance the load of optimization against responsiveness to varying spatial priorities. With a fixed event threshold, the update rate varies with operating conditions, making a prescribed average update budget difficult to maintain. We propose threshold-adaptive event-triggered model predictive control (MPC), which adjusts the threshold online in proportion to the difference between the target average period and the latest inter-event interval. We prove convergence of the empirical average update period to any feasible prescribed target and establish initialization-dependent one-sided bounds on the cumulative average. A nonlinear benchmark illustrates these budget guarantees. We then apply the mechanism to coverage MPC using a task-aware statistic that captures robot motion and changes in spatial importance. In a five-agent moving-density study, the adaptive policy uses 204.7 global MPC updates on average against a target of 200, compared with 266.7 for a fixed-threshold policy using the same statistic and initial threshold. It also reduces coverage cost relative to periodic schedules with exactly matched update counts. These results demonstrate direct average-budget regulation and effective task-dependent scheduling of MPC updates.