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

Characterization of vehicle penetration loss at wireless communication frequencies

Automotive window films are widely used for heat rejection, protection from ultraviolet radiations and glare control purposes. For an increased performance, these films are usually metallized since metals effectively reflect the impinging electromagnetic radiations. The expend of metallization in these films may affect the communication of radio signals into vehicles. In this perspective, the provision of reliable in-vehicle coverage is a major goal of both wireless network providers and automotive industry. In order to quantify the effects of automotive window films on communication signals inside a vehicle, this research study was undertaken with industrial cooperation. The thesis presents the characterization of Vehicle Penetration Loss (VPL) at major wireless communication frequencies based on empirical and numerical evaluation and by exploiting different window coatings including a commercially available automotive window film and Aluminium metal foil. The research involves ultra-wideband (UWB) car measurement campaign for the frequency range of 0.6-6.0 GHz in an indoor industrial environment at an isolated storage facility in Helsinki utilizing a regular sized hatchback car. Several realistic measurement scenarios were considered to obtain large measurement sets. The measurement data was post-processed using fine algorithms to exploit various channel characteristics to gain sufficient understanding of associated propagation phenomenon. Window films were also exclusively measured in a specialized environment to accurately assess the associated penetration loss. Apart from measurements, numerical analysis based on Finite-difference time-domain (FDTD) method for the assessment of VPL was carried out at discrete frequencies, 900 MHz and 1.2 GHz. The numerical approach can serve as a future alternate to measurements provided that adequate computational resources are available. The results infer that the use of metallized automotive films can severely affect the communication of radio signals into vehicles

Path Planning and Communication Strategies to Enable Connectivity in Robotic Systems

There has been considerable interest in the area of communication-aware robotics in recent years, where the sensing, communication and motion objectives of robotic systems are jointly optimized. One particular open problem in this area is that of exploiting the mobility of unmanned vehicles in order to improve or satisfy communication objectives in realistic communication environments. Progress in this field could not only affect robust networked operation of unmanned vehicles but also would improve communication systems of the future (e.g. 5G), thus contributing to both areas of robotics and communications. This mobility-enabled connectivity and communication is the main area of interest in this dissertation.This dissertation is focused on path planning and communication strategies for robotic systems seeking to satisfy certain communication objectives in realistic communication environments experiencing path loss, shadowing and multipath fading. We consider realistic communication environments by leveraging and incorporating a probabilistic channel prediction framework that allows the robots to predict the channel quality at unvisited locations. This thesis then contributes to the area of mobility and connectivity through three main topics 1) energy-optimal distributed beamforming, 2) finding the statistics of the distance traveled until connectivity, and 3) path planning for connectivity. First, in energy-optimal distributed beamforming, we utilize the motion of a group of initially unconnected mobile robots to enable new forms of connectivity. More specifically, we co-optimize their locations and transmission powers to cooperatively enable connectivity through distributed beamforming. We further bring a foundational theoretical understanding to robotic distributed beamforming. Next, in finding the statistics of the distance traveled until connectivity, we analytically characterize the probability density function of the distance traveled by an initially unconnected robot until it gets connected to a remote node as it moves along a given path. We utilize tools from the stochastic differential equation literature to develop this characterization. Finally, in path planning for connectivity, we actively plan the path of a mobile robot such that it finds a connected spot with a minimum expected traveled distance (i.e., energy). The scenario considered in this part is in fact a more general one, and tackles the problem of path planning on a graph to minimize the expected cost incurred until the successful completion of a task. This framework has applications beyond path planning for connectivity, in areas such as celestial body imaging, human-robot collaboration, and search scenarios. We bring a foundational understanding to this problem. We show how this problem is inherently hard to solve (NP-complete) and also propose a path planner, based on a game-theoretic framework, that provides an asymptotic optimality guarantee.Overall, this thesis proposes novel strategies for utilizing the mobility of unmanned vehicles and enabling connectivity while considering the underlying energy constraints. We also provide a rigorous theoretical analysis of the aforementioned problems using a wide range of tools from communications theory, game theory, optimal control and time series literature. Moreover, through extensive realistic numerical studies using real channel parameters/data, we show the efficiency and performance of our proposed approaches