Robotic sensor network (RSN) is an emerging paradigm that harvests data from remote sensors adopting mobile robots. However, communication and control functionalities in RSNs are interdependent, for which existing approaches become inefficient, as they plan robot trajectories merely based on unidirectional impact between communication and control. This paper proposes the concept of mutual communication control assistance (MCCA), which leverages a model predictive communication and control (MPC2) design for intertwined optimization of motion-assisted communication and communication-assisted collision avoidance. The MPC2 problem jointly optimizes the cross-layer variables of sensor powers and robot actions, and is solved by alternating optimization, strong duality, and cross-horizon minorization maximization in real time. This approach contrasts with conventional communication control co-design methods that calculate an offline non-executable trajectory. Experiments in a high-fidelity RSN simulator demonstrate that the proposed MCCA outperforms various benchmarks in terms of communication efficiency and navigation time.

翻译：机器人传感器网络（RSN）是一种采用移动机器人从远程传感器采集数据的新兴范式。然而，RSN中的通信与控制功能相互依赖，现有方法因仅基于通信与控制之间的单向影响来规划机器人轨迹而变得低效。本文提出了互通信控制辅助（MCCA）的概念，该概念利用模型预测通信与控制（MPC2）设计，对运动辅助通信与通信辅助避碰进行交织优化。MPC2问题联合优化传感器功率与机器人动作的跨层变量，并通过交替优化、强对偶性和跨时域最小化最大化实时求解。该方法与计算离线不可执行轨迹的传统通信控制协同设计方法形成对比。在高保真RSN模拟器中的实验表明，所提出的MCCA在通信效率和导航时间方面均优于多种基准方法。