Mobility-as-a-Resilience Service in Internet of Robotic Things Through Robust Multiagent Deep Reinforcement Learning

Abstract

The Internet of Robotic Things (IoRT) merges the capabilities of robotics with the connectivity and computing power of Internet of Things (IoT) technologies, enabling seamless data collection, processing, and exchange. This integration enhances robotic systems with greater intelligence, mobility, and autonomy, unlocking significant potential across various applications, including sustainable agriculture. However, deploying IoRT systems in unpredictable environments poses challenges, such as network instability and hardware failures, which have not been thoroughly explored in the literature. To address these issues, this article introduces Mobility-as-a-Resilience Service (MaaRS), a model that leverages the mobility of active uncrewed aerial vehicles (UAVs), strategically relocating them to critical points of interest in response to potential data collection failures, optimizing resource allocation and enhancing system resilience, particularly in smart farm scenarios. Additionally, a robust multiagent deep deterministic policy gradient (RMADDPG) method is devised to enable efficient task allocation and system recovery in the presence of model uncertainty, observation noise, and reward uncertainty. Extensive simulations demonstrate that the proposed method achieved a significant boost in performance, efficiency, and stability over the state-of-the-art.