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Call for papers
Important dates
Accepted papers
Invited speakers

List of accepted papers:

Of ants and elephants
Asaf Shiloni, Noa Agmon, and Gal A. Kaminka
Multi-robot systems optimization and analysis using MILP and CLP
Christian Reinl, Florian Ruh, Frieder Stolzenburg, and Oskar von Stryk
Formal behavior specification of multi-robot systems using hierarchical state machines in XABSL
Max Risler and Oskar von Stryk
Combining supervisory control of discrete event systems and reinforcement learning to control MRS
Goncalo Neto and Pedro U. Lima
A temporal logic for multi-agent MDP's
Wojciech Jamroga
Greedy approaches for solving task-allocation problems with coalitions
Xiaoming Zheng and Sven Koenig
A fast conjunctive resampling particle filter for collaborative multi-robot localization
Andrea Gasparri, Stefano Panzieri, and Federica Pascucci

A Fast Conjunctive Resampling Particle Filter (FCR-PF) for the Multi-Robot Localization problem is proposed. The idea is to exploit a plain Particle Filter to keep low the computational load and focus the attention to the collaboration among robots in order to improve the estimation capabilities. Collaboration can be setup at any time when two robots are within their range of visibility. It consists of exchanging sensory data along with the most significant subset of the particle distribution. These information are exploited both locally by each robot to solve environmental ambiguities and globally to refine the estimation process. In this way, a broader exploration of the state space is achieved with a significant increase of the convergence rate, while the asymptotic computational complexity is kept the same as for the plain Particle Filter. Simulations have been executed to underscore the effectiveness of the proposed collaborative localization algorithm.

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