Unmanned and intelligent technologies are the future development trend in the business field. It is of great significance for the connotation analysis and application characterization of massive interactive data. Particularly, during major epidemics or disasters, how to provide business services safely and securely is crucial. Specifically, providing users with resilient and guaranteed communication services is a challenging business task when the communication facilities are damaged. Unmanned aerial vehicles (UAVs), with flexible deployment and high maneuverability, can be used to serve as aerial base stations (BSs) to establish emergency networks. However, it is challenging to control multiple UAVs to provide efficient and fair communication quality of service (QoS) to users due to their limited communication service capabilities. In this paper, we propose a learning-based resilience guarantee framework for multi-UAV collaborative QoS management. We formulate this problem as a partial observable Markov decision process and solve it with proximal policy optimization (PPO), which is a policy-based deep reinforcement learning method. A centralized training and decentralized execution paradigm is used, where the experience collected by all UAVs is used to train the shared control policy. Each UAV takes actions based on the partial environment information it observes. In addition, the design of the reward function considers the average and variance of the communication QoS of all users. Extensive simulations are conducted for performance evaluation. The simulation results indicate that (1) the trained policies can adapt to different scenarios and provide resilient and guaranteed communication QoS to users, (2) increasing the number of UAVs can compensate for the lack of service capabilities of UAVs, (3) when UAVs have local communication service capabilities, the policies trained with PPO have better performance compared with the policies trained with other algorithms.

Multi-robot formation control has been intensively studied in recent years. In practical applications, the multi-robot system's ability to independently change the formation to avoid collision among the robots or with obstacles is critical. In this study, a multi-robot adaptive formation control framework based on deep reinforcement learning is proposed. The framework consists of two layers, namely the execution layer and the decision-making layer. The execution layer enables the robot to approach its target position and avoid collision with other robots and obstacles through a deep network trained by a reinforcement learning method. The decision-making layer organizes all robots into a formation through a new leader-follower configuration and provides target positions to the leader and followers. The leader's target position is kept unchanged, while the follower's target position is changed according to the situation it encounters. In addition, to operate more effectively in environments with different levels of complexity, a hybrid switching control strategy is proposed. The simulation results demonstrate that our proposed formation control framework enables the robots to adjust formation independently to pass through obstacle areas and can be generalized to different scenarios with unknown obstacles and varying number of robots.