Randomized Robot Trophallaxis

Energy is the critical resource of most living mechanisms. Recent research in robotics has been mostly considered in behavioural autonomy rather than in energy autonomy. This chapter presents our study in “randomized robot trophallaxis”. The chapter consists of three main parts: modeling, simulation, and implementation. In the first section, we model energy trophallaxis in multi-robot system through probabilistic modelling. Deterministic modelling of large groups of interacting mobile robots leads to highly complex nonlinear hybrid models, likely to be highly sensitive to preconditions, i.e. chaotic. Thus, any imprecision in pre-conditions would turn results from such a model useless even for moderate time horizons. However, chaotic systems often exhibit smooth ergodic properties, i.e. time averages have limit values independent of initial conditions and only smoothly dependent on model parameters, etc. Randomness and ergodic properties may exist naturally in such systems or even be intentionally enforced by introducing inherent uncertainty/randomization into the behaviour of individual robots in order to prevent non productive cyclic behaviour such as deadlock or livelocks. Ergodic properties and randomness calls for probabilistic modelling. We propose a combined probabilistic model covering energy exchange between robots, energy consumption in individual robots, charging at predefined charging stations and finally random mobility, where the latter comes in the shape of highly versatile Markovian mobility model. Stationary results furnish overall system performability analysis, such as the impact of individual behaviour on overall system survivability. The section presents the proposed model and comprises central parts of model development as well as illustrative numerical results. In the second section, we simulate aspects of energy autonomy inspired by natural phenomena of animal behaviour. Trophallaxis is a natural phenomenon, biologically observed from social insects or vertebrate animals, to exchange food between colony members. This section describes the concept, “Randomized Robot Trophallaxis”, based on a group of autonomous mobile robots with capabilities of self-refueling energy and selfsharing energy. We firstly clarify the concept “Randomized Robot Trophallaxis” by given examples of natural animal societies. Secondly, we examine the concept by simulation results in order to point out considerable advantages of trophallactic features when deploying multiple mobile robots. The section is concluded with discussion of “randomization” and its appearances in multi-robot system. O pe n A cc es s D at ab as e w w w .in te hw eb .c om

[1]  Y. Charlie Hu,et al.  Deployment of mobile robots with energy and timing constraints , 2006, IEEE Transactions on Robotics.

[2]  C. Melhuish,et al.  Energetically Autonomous Robots , 2003 .

[3]  J. Medlock,et al.  Spreading disease: integro-differential equations old and new. , 2003, Mathematical biosciences.

[4]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[5]  Hannes Hartenstein,et al.  Stochastic properties of the random waypoint mobility model: epoch length, direction distribution, and cell change rate , 2002, MSWiM '02.

[6]  D. Sumpter,et al.  From nonlinearity to optimality: pheromone trail foraging by ants , 2003, Animal Behaviour.

[7]  Marco Dorigo,et al.  Autonomous Self-assembly in a Swarm-bot , 2005, AMiRE.

[8]  Henrik Schiøler,et al.  An Approach to Sociable Robots through Self-distributed Energy , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Francesco Mondada,et al.  Self-assembly on Demand in a Group of Physical Autonomous Mobile Robots Navigating Rough Terrain , 2005, ECAL.

[10]  Eiichi Yoshida,et al.  Hardware design of modular robotic system , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[11]  Henrik Hautop Lund,et al.  Self-Reconfigurable Robots with ATRON Modules , 2005 .

[12]  Eiichi Yoshida,et al.  A motion planning method for a self-reconfigurable modular robot , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[13]  David B. Fogel,et al.  SWARM-BOT: Design and Implementation of Colonies of Self-Assembling Robots , 2006 .

[14]  David W. Payton,et al.  Pheromone Robotics and the Logic of Virtual Pheromones , 2004, Swarm Robotics.

[15]  Yamir Moreno,et al.  Dynamics of rumor spreading in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Y. Charlie Hu,et al.  Energy-efficient mobile robot exploration , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[17]  Jean-Yves Le Boudec,et al.  Perfect simulation and stationarity of a class of mobility models , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[18]  Karl Crailsheim,et al.  Protein trophallaxis and the regulation of pollen foraging by honey bees (Apis mellifera L.) , 1998 .

[19]  H. Schwefel,et al.  Probabilistic Modelling of Information Propagation in Wireless Mobile Ad-Hoc Network , 2005 .

[20]  Luca Maria Gambardella,et al.  SWARM-BOTS: Physical Interactions in Collective Robotics , 2005 .