Decentralized Communication-Aware Motion Planning in Mobile Networks: An Information-Gain Approach

In this paper we consider decentralized motion-planning in mobile cooperative networks in the presence of realistic stochastic communication links, including path-loss, fading and shadowing effects. We propose a communication-aware motion-planning strategy, where each node considers the information gained through both its sensing and communication when deciding on its next move. More specifically, we show how each node can predict the information gained through its communications, by online learning of link quality measures such as received Signal to Noise Ratio (SNR) and correlation characteristics, and combine it with the information gained through its sensing in order to build objective functions for motion planning. We show that in the presence of path loss, our proposed strategy can improve the performance drastically. We furthermore show that while uncorrelated low-SNR fading channels can ruin the overall performance, the natural randomization of uncorrelated channels can potentially help the nodes leave deep fade spots with small movements. We finally show that highly correlated deep fades, on the other hand, can degrade the performance drastically for a long period of time. We then propose a randomizing motion-planning strategy that can help the nodes leave highly correlated deep fades.