Mission Continuity with Resilient Autonomy Architectures in Disaster Response Robots: Network Simulation

Abstract

Disaster response missions operate in highly dynamic and structurally compromised environments where communication networks are frequently degraded or completely destroyed. For unmanned ground vehicles and aerial drones deployed in such scenarios, the loss of telemetry and control signals often results in mission failure, as traditional teleoperation heavily relies on continuous high-bandwidth connectivity. This paper proposes a comprehensive framework for predicting mission continuity by evaluating resilient autonomy architectures through advanced network simulation. By coupling robotic operating system environments with discrete-event network simulators, we model the complex interplay between autonomous decision-making algorithms and fluctuating network topologies. The core objective is to quantify how autonomous failover mechanisms, decentralized swarm intelligence, and adaptive behavioral models contribute to sustained mission performance when communication links fail. Through extensive simulated urban search and rescue scenarios, we assess various degrees of network degradation, including signal attenuation, multipath fading, and complete node isolation. The analysis reveals that resilient autonomy architectures significantly improve mission continuity probabilities compared to traditional semi-autonomous systems. Furthermore, we introduce a predictive model that utilizes real-time network state data to forecast the likelihood of successful task completion, enabling proactive deployment strategies. The findings provide critical insights for the design of robust robotic systems capable of sustained operations in extreme, communication-denied environments.