Control and communication are often tightly coupled for networked mobile robots; motions of robots impact communication quality and communication quality of service (QoS), in turn, affecting coordination performance of robots. In this article, we propose a theoretical motion planning control framework for a team of networked mobile robots to accomplish high-level spatial and temporal motion specifications while optimizing communication QoS. Desired motion specifications are formulated as signal temporal logic (STL), whereas the communication QoS to be optimized is captured by spatial temporal reach and escape logic (STREL) formulas. Both the STL and STREL specifications are encoded as mixed integer linear constraints posed on the system and environment state variables of the mobile robot network, where satisfactory control strategies can be computed by exploiting a distributed model predictive control (MPC) approach. A two-layer hierarchical MPC procedure is proposed to efficiently solve the co-optimization problem, whose recursive feasibility is formally ensured. The effectiveness of the proposed framework is validated by simulation.

中文翻译：

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正式规范下网络移动机器人的分布式感知通信的运动计划

对于联网的移动机器人，控制和通信通常紧密相连。机器人的动作会影响通信质量和通信服务质量（QoS），进而影响机器人的协调性能。在本文中，我们为一组联网的移动机器人提出了一种理论上的运动计划控制框架，以在优化通信QoS的同时完成高级的时空运动规范。所需的运动规范被表述为信号时间逻辑（STL），而要优化的通信QoS由空间时间到达和逃逸逻辑（STREL）公式捕获。STL和STREL规范都被编码为对移动机器人网络的系统和环境状态变量构成的混合整数线性约束，通过利用分布式模型预测控制（MPC）方法可以计算出令人满意的控制策略。为了有效地解决协同优化问题，提出了一种两层的层次化MPC程序，形式上保证了递归的可行性。仿真验证了所提出框架的有效性。