Dynamic Task Allocation for Robotic Edge System Resilience Using Deep Reinforcement Learning
针对机器人边缘系统中因机械故障导致任务失败的问题，提出利用机器人移动性实现快速韧性恢复，并采用深度强化学习训练边缘服务器动态分配任务，数值实验验证了有效性。
Incorporating edge and cloud computing with robotics provides extended options for robots to perform real-time sensing and actuation operations in various cyber–physical systems (CPSs), including smart farms. Such systems are prone to uncertain failures triggered by mechanical disruptions. Consequently, the overall system performance degrades, primarily when location-specific tasks are already assigned to a faulty robot and require immediate recovery. Using edge and cloud computing resources is not always feasible due to communication and latency constraints. Therefore, this article exclusively focuses on harnessing the mobility of robots to support the computation tasks affected by uncertain failures of previously assigned robots and ensure faster resiliency management by relocating active robots near task sources. The proposed mobility-as-a-resilience-service (MaaRS) is formulated using a Markov decision process (MDP). Later, an edge server proximal to the robots is trained using deep reinforcement learning (DRL) to assign tasks among the robots. Specifically, a multiple deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (MDQN)-based dynamic task allocation mechanism is proposed to converge to a solution exploring reward uncertainties with the best exploitation. Numerical evaluation using Python and TensorFlow validates the effectiveness of the proposed approach compared to other benchmarks.