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

Collaborative Autonomous Surveys in Marine Environments Affected by Oil Spills

This chapter presents results on collaborative autonomous surveys using a fleet of heterogeneous autonomous robotic vehicles in marine environments affected by oil spills. The methods used for the surveys are based on a class of path following controllers with mathematically proven convergence and robustness. Use of such controllers enables easy mission planning for autonomous marine surveys where the paths consist of lines and curves. The control algorithm uses simple dynamic models and simple control laws and thus enables quick deployment of a fleet of autonomous vehicles to collaboratively survey large areas. This enables using a mobile network to survey an area where the different member nodes may have slightly different capabilities. A mapping algorithm used to reconcile data from heterogeneous marine vehicles on multiple different paths is also presented. Vehicles with heterogeneous dynamics are thus used to aid in the reconstruction of a time varying field. The algorithms used were tested, mainly on student-built marine robots that collaboratively surveyed a coastal lagoon in Grand Isle, Louisiana that was polluted by crude oil during the Deepwater Horizon oil spill. The results obtained from these experiments show the effectiveness of the proposed methods for oil spill surveys and also provide guidance for mission designs for future collaborative autonomous environmental surveys