Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes

The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.

[1]  Luis Magdalena,et al.  An Open Localization and Local Communication Embodied Sensor , 2008, Sensors.

[2]  Álvaro Gutiérrez,et al.  Self-Organized Distributed Localization Based on Social Odometry , 2011 .

[3]  Gaurav S. Sukhatme,et al.  LOST: localization-space trails for robot teams , 2002, IEEE Trans. Robotics Autom..

[4]  Azzedine Boukerche,et al.  Trust-based security for wireless ad hoc and sensor networks , 2007, Comput. Commun..

[5]  Audun Jøsang,et al.  AIS Electronic Library (AISeL) , 2017 .

[6]  Pedro Malagón,et al.  Improving security in WMNs with reputation systems and self-organizing maps , 2011, J. Netw. Comput. Appl..

[7]  Christiaan J. J. Paredis,et al.  Heterogeneous Teams of Modular Robots for Mapping and Exploration , 2000, Auton. Robots.

[8]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[9]  Ole Ravn,et al.  Location Estimation for an Autonomously Guided Vehicle using an Augmented Kalman Filter to Autocalib , 1998 .

[10]  José Manuel Moya,et al.  Distributed intrusion detection system for wireless sensor networks based on a reputation system coupled with kernel self-organizing maps , 2010, Integr. Comput. Aided Eng..

[11]  Marco Dorigo,et al.  Collective decision-making based on social odometry , 2010, Neural Computing and Applications.

[12]  Marco Dorigo,et al.  Open E-puck Range & Bearing miniaturized board for local communication in swarm robotics , 2009, 2009 IEEE International Conference on Robotics and Automation.

[13]  Francisco C. Santos,et al.  Social Odometry in Populations of Autonomous Robots , 2008, ANTS Conference.

[14]  M. Dorigo,et al.  Path Formation in a Robot Swarm Self-organized Strategies to Find Your Way Home , 2022 .

[15]  Francisco C. Santos,et al.  Social Odometry: Imitation Based Odometry in Collective Robotics , 2009 .

[16]  Marco Dorigo,et al.  Path formation in a robot swarm , 2008, Swarm Intelligence.

[17]  Pedro Malagón,et al.  Eliminating routing protocol anomalies in wireless sensor networks using AI techniques , 2010, AISec '10.

[18]  Atta Badii,et al.  Unsupervised Genetic Algorithm Deployed for Intrusion Detection , 2008, HAIS.

[19]  Alberto Muñoz,et al.  Self-organizing maps for outlier detection , 1998, Neurocomputing.

[20]  Francesco Mondada,et al.  The e-puck, a Robot Designed for Education in Engineering , 2009 .

[21]  Liqiang Feng,et al.  Where am I? : sensors and methods for autonomous mobile robot positioning , 1994 .

[22]  Wolfram Burgard,et al.  A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[23]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[24]  Josh Bongard.  Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard, and Dieter Fox. (2005, MIT Press.) 647 pages , 2008, Artificial Life.

[25]  Pedro Malagón,et al.  Detecting and Confining Sybil Attack in Wireless Sensor Networks Based on Reputation Systems Coupled with Self-organizing Maps , 2010, AIAI.

[26]  Francisco C. Santos,et al.  Cooperation Prevails When Individuals Adjust Their Social Ties , 2006, PLoS Comput. Biol..