Communication in a Swarm of Miniature Robots: The e-Puck as an Educational Tool for Swarm Robotics

Swarm intelligence, and swarm robotics in particular, are reaching a point where leveraging the potential of communication within an artificial systempromises to uncover newand varied directions for interesting research without compromising the key properties of swarmintelligent systems such as self-organization, scalability, and robustness. However, the physical constraints of using radios in a robotic swarm are hardly obvious, and the intuitive models often used for describing such systems do not always capture them with adequate accuracy. In order to demonstrate this effectively in the classroom, certain tools can be used, including simulation and real robots. Most instructors currently focus on simulation, as it requires significantly less investment of time, money, and maintenance--but to really understand the differences between simulation and reality, it is also necessary to work with the real platforms from time to time. To our knowledge, our coursemay be the only one in the world where individual students are consistently afforded the opportunity to work with a networked multi-robot system on a tabletop. The e-Puck, a low-cost small-scale mobile robotic platform designed for educational use, allows us bringing real robotic hardware into the classroom in numbers sufficient to demonstrate and teach swarm-robotic concepts.We present here a custom module for local radio communication as a stackable extension board for the e-Puck, enabling information exchange between robots and also with any other IEEE 802.15.4-compatible devices. Transmission power can be modified in software to yield effective communication ranges as small as fifteen centimeters. This intentionally small range allows us to demonstrate interesting collective behavior based on local information and control in a limited amount of physical space, where ordinary radios would typically result in a completely connected network. Here we show the use of this module facilitating a collective decision among a group of 10 robots.

[1]  Alcherio Martinoli,et al.  Modeling Swarm Robotic Systems: a Case Study in Collaborative Distributed Manipulation , 2004, Int. J. Robotics Res..

[2]  Olivier Michel,et al.  Cyberbotics Ltd. Webots™: Professional Mobile Robot Simulation , 2004, ArXiv.

[3]  Chris Melhuish,et al.  Minimalist coherent swarming of wireless networked autonomous mobile robots , 2002 .

[4]  O. Bonaventure Software tools for networking , 2004 .

[5]  Olivier Michel,et al.  Cyberbotics Ltd. Webots™: Professional Mobile Robot Simulation , 2004 .

[6]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[7]  Nikolaus Correll,et al.  Modeling and Optimization of a Swarm-Intelligent Inspection System , 2004, DARS.

[8]  David E. Culler,et al.  Telos: enabling ultra-low power wireless research , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[9]  Jean-Louis Deneubourg,et al.  Collective Decision-Making Based on Individual Discrimination Capability in Pre-social Insects , 2006, SAB.

[10]  Alcherio Martinoli,et al.  Macroscopic Modeling of Aggregation Experiments using Embodied Agents in Teams of Constant and Time-Varying Sizes , 2004, Auton. Robots.

[11]  Francesco Mondada,et al.  Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms , 1993, ISER.

[12]  Nikolaus Correll,et al.  Comparing Coordination Schemes for Miniature Robotic Swarms: A Case Study in Boundary Coverage of Regular Structures , 2006, ISER.

[13]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[14]  David E. Culler,et al.  Mica: A Wireless Platform for Deeply Embedded Networks , 2002, IEEE Micro.

[15]  Gerardo Beni,et al.  From Swarm Intelligence to Swarm Robotics , 2004, Swarm Robotics.

[16]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[17]  Olivier Bonaventure Software tools for networking , 2004, IEEE Network.

[18]  B. Sriraman Call for papers. , 2021, Journal of back and musculoskeletal rehabilitation.

[19]  Roland Siegwart,et al.  Mobile micro-robots ready to use: Alice , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Robert Szewczyk,et al.  System architecture directions for networked sensors , 2000, ASPLOS IX.

[21]  Alcherio Martinoli,et al.  Efficiency and robustness of threshold-based distributed allocation algorithms in multi-agent systems , 2002, AAMAS '02.