In smart factories, Automated Guided Vehicles (AGVs) are usually used for patrol service, which can carry out such tasks as detecting abnormal device status, transporting material and simultaneously making their own path plan. Hence, these tasks require intensive computational power and real-time response and it is difficult to complete them by a single node. Therefore, in this paper, we propose a joint optimization method of task offloading and resource scheduling of multiple AGVs for smart factory patrol service. The goal is to minimize the overall energy consumption of the AGVs, by jointly using MEC and D2D offloading, while meeting the constraints of the delay, power, available computing capacity and bandwidth of AGVs. We first analyze the impact of information collection and cooperation tasks on transmission delay and formulate an energy consumption minimization problem. Then, we propose a two-step algorithm to get the solutions of the optimization problem. For resource allocation sub-problem, we design an efficient algorithm for power and bandwidth allocation by using the Karush-Kuhn-Tucker (KKT) condition based on the properties of convex bandwidth. To solve the nonlinear task offloading problem, we transform it into a linear mixed integer problem by introducing slack variables and use Gurobi for the solution. Simulation results show that compared with other methods, the proposed algorithm has good performances on convergence speed and energy conservation of AGVs in the process of patrol tasks under different scenarios.

Multiple Automated Guided Vehicles promise to be an effective solution for executing tasks such as material transportation and inspection in industrial parks. In particular, system stability is the key to maintaining connectivity among multiple agents. In this paper, we build the stability model of multi-agent system (MAS) under the background of swarm robots transporting materials in an industrial park, and propose a network topology optimization method to improve the stability of MAS. In concrete, we first analyze the effect of channel environment on network topology, and the communication delay is analyzed. Then considering the communication delay and artificial potential field, we establish the stability analysis model of MAS and obtain the stability condition of the MAS by using Lyapunov correlation theorem. Finally, we formulate the network topology optimization problem of MAS by maximizing the second smallest eigenvalue of the Laplacian and get the optimal solutions. Analysis and simulation results show the effectiveness of the proposed model and algorithm.