A topological navigation system for indoor environments based on perception events

The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.

[1]  Alessandro Minelli,et al.  The Development of Animal Form: Ontogeny, Morphology, and Evolution , 2003 .

[2]  Ramon Barber,et al.  A planner for topological navigation based on previous experiences , 2004 .

[3]  Steven Skiena,et al.  Implementing discrete mathematics - combinatorics and graph theory with Mathematica , 1990 .

[4]  Gregory Dudek,et al.  A global topological map formed by local metric maps , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[5]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[6]  Petter Ögren,et al.  Robot navigation under uncertainties using event based sampling , 2014, 53rd IEEE Conference on Decision and Control.

[7]  C. Breazeal,et al.  Robots that imitate humans , 2002, Trends in Cognitive Sciences.

[8]  Ramón Barber,et al.  Object Detection Applied to Indoor Environments for Mobile Robot Navigation , 2016, Sensors.

[9]  Luis Merino,et al.  Integration of Monte Carlo Localization and place recognition for reliable long-term robot localization , 2014, 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[10]  Frédéric Lerasle,et al.  Topological navigation and qualitative localization for indoor environment using multi-sensory perception , 2002, Robotics Auton. Syst..

[11]  Avinash C. Kak,et al.  Vision for Mobile Robot Navigation: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Keiji Nagatani,et al.  Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization , 2001, IEEE Trans. Robotics Autom..

[13]  Daniel Maier,et al.  Real-time navigation in 3D environments based on depth camera data , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[14]  Kurt Konolige,et al.  Navigation in hybrid metric-topological maps , 2011, 2011 IEEE International Conference on Robotics and Automation.

[15]  Sebastian Thrun,et al.  Integrating Grid-Based and Topological Maps for Mobile Robot Navigation , 1996, AAAI/IAAI, Vol. 2.

[16]  In-So Kweon,et al.  Metric localization using a single artificial landmark for indoor mobile robots , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[18]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[19]  Naoki Akai,et al.  A navigation method based on topological magnetic and geometric maps for outdoor mobile robots , 2015, 2015 IEEE/SICE International Symposium on System Integration (SII).

[20]  Atsushi Nakazawa,et al.  Learning from Observation Paradigm: Leg Task Models for Enabling a Biped Humanoid Robot to Imitate Human Dances , 2007, Int. J. Robotics Res..

[21]  Arturo de la Escalera,et al.  A visual landmark recognition system for topological navigation of mobile robots , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[22]  Maja J. Mataric,et al.  Behaviour-based control: examples from navigation, learning, and group behaviour , 1997, J. Exp. Theor. Artif. Intell..

[23]  Ramon Barber,et al.  A ROS-BASED MIDDLE-COST ROBOTIC PLATFORM WITH HIGH-PERFORMANCE , 2015 .

[24]  Ben J. A. Kröse,et al.  Navigation using an appearance based topological map , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[25]  Lúcia Valéria Ramos de Arruda,et al.  Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps , 2011, Applied Intelligence.

[26]  Benjamin Kuipers,et al.  A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations , 1991, Robotics Auton. Syst..

[27]  Hugh F. Durrant-Whyte,et al.  Natural landmark-based autonomous vehicle navigation , 2004, Robotics Auton. Syst..

[28]  Friedrich Fraundorfer,et al.  Topological mapping, localization and navigation using image collections , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.