Fast multipole networks

Two prerequisites for robotic multiagent systems are mobility and communication. Fast multipole networks (FMNs) enable both ends within a unified framework. FMNs can be organized very efficiently in a distributed way from local information and are ideally suited for motion planning using artificial potentials. We compare FMNs to conventional communication topologies, and find that FMNs offer competitive communication performance (including higher network efficiency per edge at marginal energy cost) in addition to advantages for mobility

Interference alignment by motion

Recent years have witnessed increasing interest in interference alignment which has been demonstrated to deliver gains for wireless networks both analytically and empirically. Typically, interference alignment is achieved by having a MIMO sender precode its transmission to align it at the receiver. In this paper, we show, for the first time, that interference alignment can be achieved via motion, and works even for single-antenna transmitters. Specifically, this alignment can be achieved purely by sliding the receiver's antenna. Interestingly, the amount of antenna displacement is of the order of one inch which makes it practical to incorporate into recent sliding antennas available on the market. We implemented our design on USRPs and demonstrated that it can deliver 1.98× throughput gains over 802.11n in networks with both single-antenna and multi- antenna nodes.National Science Foundation (U.S.

Co-Optimization of Communication, Motion and Sensing in Mobile Robotic Operations

In recent years, there has been considerable interest in wireless sensor networks and networked robotic systems. In order to achieve the full potential of such systems, integrative approaches that design the communication, navigation and sensing aspects of the systems simultaneously are needed. However, most of the existing work in the control and robotic communities uses over-simplified disk models or path-loss-only models to characterize the communication in the network, while most of the work in networkingand communication communities does not fully explore the benefits of motion.This dissertation thus focuses on co-optimizing these three aspects simultaneously in realistic communication environments that experience path loss, shadowing and multi-path fading. We show how to integrate the probabilistic channel prediction framework, which allows the robots to predict the channel quality at unvisited locations, into the co-optimization design. In particular, we consider four different scenarios: 1) robotic routerformation, 2) communication and motion energy co-optimization along a pre-defined trajectory, 3) communication and motion energy co-optimization with trajectory planning, and 4) clustering and path planning strategies for robotic data collection. Our theoretical, simulation and experimental results show that the proposed framework considerably outperforms the cases where the communication, motion and sensing aspects of the system are optimized separately, indicating the necessity of co-optimization. They furthershow the significant benefits of using realistic channel models, as compared to the case of using over-simplified disk models