Position Tracking and Path Planning in Uncertain Maps for Robot Formations* *This work was partially supported by the Spanish projects MICINN-FEDER DPI2009-08126 and DPI2009-13710, and the European project UE-ICT-2009-248942.

Abstract This paper presents a complete working system for robot formations, where path planning and localization tasks are integrated in such a way that environment uncertainty is considered in each of the tasks. Feature-based and grid-based mapping strategies are combined in a probabilistic way to compute an obstacle-free and of minimum-risk plan towards the goal. The formation benefits from the cooperative perception to obtain a joint vision of the environment, represented in a leadercentric way to minimize the effects of the uncertainty. The system has been tested and validated by means of a set of simulations as well as in real experiments.

[1]  Nicholas Roy,et al.  Adapting probabilistic roadmaps to handle uncertain maps , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[2]  Kostas J. Kyriakopoulos,et al.  Navigation of Multiple Kinematically Constrained Robots , 2008, IEEE Transactions on Robotics.

[3]  Pieter Abbeel,et al.  LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information , 2010, Int. J. Robotics Res..

[4]  Luis Montano,et al.  Cooperative robot team navigation strategies based on an environment model , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  G. Lachapelle,et al.  Consideration of time-correlated errors in a Kalman filter applicable to GNSS , 2009 .

[6]  Stergios I. Roumeliotis,et al.  Distributed multirobot localization , 2002, IEEE Trans. Robotics Autom..

[7]  P. Abbeel,et al.  LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information , 2011 .

[8]  Maria Teresa Lazaro,et al.  Localization of probabilistic robot formations in SLAM , 2010, 2010 IEEE International Conference on Robotics and Automation.

[9]  José A. Castellanos,et al.  Robocentric map joining: Improving the consistency of EKF-SLAM , 2007, Robotics Auton. Syst..

[10]  José Luis Villarroel,et al.  Real Time Communications over 802.11: RT-WMP , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[11]  Giuseppe Oriolo,et al.  A Bayesian framework for optimal motion planning with uncertainty , 2008, 2008 IEEE International Conference on Robotics and Automation.

[12]  Stergios I. Roumeliotis,et al.  Distributed Multi-Robot Localization , 2000, DARS.

[13]  Xiaoming Hu,et al.  Observer-Based Leader-Following Formation Control Using Onboard Sensor Information , 2008, IEEE Transactions on Robotics.

[14]  Alan C. Schultz,et al.  Continuous localization using evidence grids , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[15]  A. Bryson,et al.  Linear filtering for time-varying systems using measurements containing colored noise , 1965 .

[16]  Florent Lamiraux,et al.  Motion planning for humanoid robots in environments modeled by vision , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.