A game theory-based approach for robots deploying wireless sensor nodes

International audienceWireless Sensor Networks (WSNs) are deployed in many fields of application. Depending on the application requirements, sensor nodes can either be mobile and autonomous or static. In both cases, they are able to cooperate together in order to monitor a given area or some given Points of Interest (PoIs). Static sensor nodes need one or several agent(s) (humans or robots) to deploy them. In this paper, we focus on the deployment of static sensor nodes in an area containing obstacles, using two mobile robots. We want to minimize the time needed by the two robots to deploy all the sensor nodes and to return to their starting position. We require that each sensor node is placed at a PoI position, no PoI position is empty and no PoI position is occupied by more than one sensor node. The problem consists in determining the best strategy for each robot in order to meet these constraints. We adopt a game theory approach to solve this problem

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

Safe, Remote-Access Swarm Robotics Research on the Robotarium

This paper describes the development of the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-agent research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium as well as connects these to the particular considerations one must take when making complex hardware remotely accessible. In particular, safety must be built in already at the design phase without overly constraining which coordinated control programs the users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.Comment: 13 pages, 7 figures, 3 code samples, 72 reference

Optimized trajectories of multi-robot deploying wireless sensor nodes

International audienceA main reason to the growth of wireless sensor networks deployed worldwide is their easy and fast deployment. In this paper we consider deployments assisted by mobile robots where static sensor nodes are deployed by mobile robots in a given area. Each robot must make a tour to place its sensor nodes. All sensor nodes must be placed at their precomputed positions. The Multi-Robot Deploying wireless Sensor nodes problem, called the MRDS problem, consists in minimizing the longest tour duration (i.e. the total deployment duration), the number of robots used and the standard deviation between duration of robots tours. After a formal definition of the MRDS problem, we show how to use a multi-objective version of genetic algorithms, more precisely the NSGA-II algorithm, to solve this multi-objective optimization problem. The solutions belonging to the best Pareto front are given to the designer in charge of selecting the best trade-off taking into account various criteria. We then show how to extend this method to take obstacles into account, which is more representative of real situations

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work