Abstract-This paper addresses the reactive collision avoidance for vehicles that move in very dense, cluttered, and complex scenarios. First, we describe the design of a reactive navigation method that uses a "divide and conquer" strategy based on situations to simplify the difficulty of the navigation. Many techniques could be used to implement this design (since it is described at symbolic level), leading to new reactive methods that must be able to navigate in arduous environments (as the difficulty of the navigation is simplified). We also propose a geometry-based implementation of our design called the nearness diagram navigation. The advantage of this reactive method is to successfully move robots in troublesome scenarios, where other methods present a high degree of difficulty in navigating. We show experimental results on a real vehicle to validate this research, and a discussion about the advantages and limitations of this new approach.

Abstract-This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of the relative displacement. The contribution is a probabilistic modelling of this process that takes into account all the uncertainties involved: the uncertainty of the displacement of the sensor and the measurement noises. Furthermore, it also considers all the possible correspondences resulting from these uncertainties. This technique has been implemented and tested on a real vehicle. The experiments illustrate how the performances of this method are better than previous geometric ones in terms of robustness, accuracy and convergence.

Abstract-The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work we describe the RoboEarth semantic mapping system. The semantic map is composed of (1) an ontology to code the concepts and relations in maps and objects, and (2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: (1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; (2) the sharing of semantic maps between robots, and (3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot.To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology.Note to Practitioners-RoboEarth is a cloud-based knowledge base for robots that transforms a simple robot into an intelligent one thanks to the web services provided. As mapping is a mandatory element on most of the robot systems, we focus on the RoboEarth semantic mapping for robot systems, showing the benefits of the combination of SLAM (Simultaneous Localization And Map building), and knowledge-based reasoning. We show the qualities of our system by means of two experiments: (1) building a map of a novel environment boosted by prior information and (2) efficient searching for a novel object thanks to the knowledgebased reasoning techniques. We can conclude that RoboEarth enables the execution of the proposed methods as web and cloud services that enable advanced perception in a simple robot.

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The growing interest in robot teams for surveillance or rescue missions entails new technological challenges. Robots have to move to complete their tasks while maintaining communication among themselves and with their human operators, in many cases without the aid of a communication infrastructure. Guaranteeing connectivity enables robots to explicitly exchange information needed in collaborative task execution, and allows operators to monitor or manually control any robot at all times. Network paths should be multi-hop, so as not to unnecessarily restrict the team's range. In this work we contribute a complete system which integrates three research aspects, usually studied separately, to achieve these characteristics: a multi-robot cooperative motion control technique based on a virtual spring-damper model which prevents communication network splits, a task allocation algorithm that takes advantage of network link information in order to ensure autonomous mission completion, and a network layer which works over wireless 802.11 devices, capable of sustaining hard real-time traffic and changing topologies. Link quality among peers is the key metric used to cooperatively move the robots and maintain uninterrupted connectivity, and the basis for novel ideas presented in each subsystem. Simulations and experimental results with real robots are presented and discussed.KEY WORDS-cooperative navigation, link-quality-based communication, multi-robot task allocation, networked robot systems.