This unique reference represents a cross-section of forefront robotics research, ranging from robotics and systems to learning, autonomy and failure detection, from vision and navigation to localization and mapping, which are based on the papers presented at the 1st European Robotics Symposium (EUROS-06) held in Palermo, Italy from 16-18 March, 2006. The European Robotics Symposium (EUROS) is a brand-new International scientific event promoted by EURON, the European Robotics Network. Since its inception in 2000, EURON links most of the European robotics research teams and today (2006) involves more than 165 universities and companies across all of its member states. The EUROS meeting will be held in Europe every other year, but international participation at all levels is strongly encouraged making it a meeting place for high-quality presentation of interesting international research on robotics.

Proceedings of the 1st European Robotics Symposium EUROS 2006. Presents the latest in European Robotics

Inhalt
An Ontology of Robotics Science.- A Multi-Agent System Architecture for Modular Robotic Mobility Aids.- How to Construct Dense Objects with Self-Reconfigurable Robots.- In Situ Autonomous Biomechanical Characterization.- Incremental Learning of Task Sequences with Information- Theoretic Metrics.- Representation, Recognition and Generation of Actions in the context of Imitation Learning.- Reduction of Learning Time for Robots Using Automatic State Abstraction.- Efficient Failure Detection for Mobile Robots Using Mixed-Abstraction Particle Filters.- Optimization and FailSafety Analysis of Antagonistic Actuation for pHRI.- Bilateral Control of Different Order Teleoperators.- Navigation and planning in an unknown environment using vision and a cognitive map.- Exploiting Distinguishable Image Features in Robotic Mapping and Localization.- Image-Based Visual Servoing for Mobile Robots with Catadioptric Camera.- Multi-knowledge Approach for Mobile Robot Identification of a Changing Environment.- Robust Monte-Carlo Localization using Adaptive Likelihood Models.- Metric Localization with Scale-Invariant Visual Features using a Single Perspective Camera.