3 research outputs found
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
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