Route Swarm: Wireless Network Optimization through Mobility

In this paper, we demonstrate a novel hybrid architecture for coordinating networked robots in sensing and information routing applications. The proposed INformation and Sensing driven PhysIcally REconfigurable robotic network (INSPIRE), consists of a Physical Control Plane (PCP) which commands agent position, and an Information Control Plane (ICP) which regulates information flow towards communication/sensing objectives. We describe an instantiation where a mobile robotic network is dynamically reconfigured to ensure high quality routes between static wireless nodes, which act as source/destination pairs for information flow. The ICP commands the robots towards evenly distributed inter-flow allocations, with intra-flow configurations that maximize route quality. The PCP then guides the robots via potential-based control to reconfigure according to ICP commands. This formulation, deemed Route Swarm, decouples information flow and physical control, generating a feedback between routing and sensing needs and robotic configuration. We demonstrate our propositions through simulation under a realistic wireless network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on Intelligent Robots and Systems (IROS) 201

Stationary and Mobile Target Detection using Mobile Wireless Sensor Networks

In this work, we study the target detection and tracking problem in mobile sensor networks, where the performance metrics of interest are probability of detection and tracking coverage, when the target can be stationary or mobile and its duration is finite. We propose a physical coverage-based mobility model, where the mobile sensor nodes move such that the overlap between the covered areas by different mobile nodes is small. It is shown that for stationary target scenario the proposed mobility model can achieve a desired detection probability with a significantly lower number of mobile nodes especially when the detection requirements are highly stringent. Similarly, when the target is mobile the coverage-based mobility model produces a consistently higher detection probability compared to other models under investigation.Comment: 7 pages, 12 figures, appeared in INFOCOM 201

Efficiently learning metric and topological maps with autonomous service robots

Models of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., corridors or rooms. This enables the robot to construct annotated metric as well as topological maps of the environment. All techniques have been implemented and thoroughly tested using real mobile robot in a variety of environments