Robotic Sensor Network: Achieving Mutual Communication Control Assistance With Fast Cross-Layer Optimization

Abstract

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 letter proposes the concept of mutual communication control assistance (MCCA), which leverages a model predictive communication and control ( ${\mathtt {MPC}}^{2}$ ) design for intertwined optimization of motion-assisted communication and communication-assisted collision avoidance. The ${\mathtt {MPC}}^{2}$ 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.