In the third section, we mainly present hardware implementation of our mobile robots capable of performing not only self-refueling energy but also self-sharing energy. We describe the mechanical and electrical design of a mobile robot, called the CISSbot 1 . The robots are designed towards truly autonomous robots in large populations through energy trophallaxis. Unlike present mobile robots, the CISSbots are energetically autonomous robots because they are able to not only autonomously refuel

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... ergy by picking batteries up at a charging station, but also share energy by exchanging batteries to other robots. The CISSbots basically consist of their own processing power, sensors, and actuators. However, to achieve the capability of battery exchange, the CISSbots need a special design of battery exchange mechanism. In this section, we present the realization of the design, both the mechanics and the electronics of the CISSbot. Details on battery exchange technique and power management are clarified. Finally, the section issues an outline of our future work on the CISSbots. A Probabilistic Model of Randomized Robot Trophallaxis An Introduction to Probabilistic Modelling Various mathematical modelling paradigms exist for dynamical systems, which all aim to provide system predictability, i.e. answer questions regarding future state of the system evolving from some initial state or set of initial states. The appropriate model paradigm depends highly on the nature of the questions to be answered, i.e. the scope of required information as well as the form of the answer provided by the model. In the present case, we ask for distribution of energy resources throughout the population of mobile robots as well as the survival state of the population, i.e. how many robots have survived energy starvation over a certain time frame. Of particular interest is the impact, that individual robot behaviour may have on energy distribution and survival. Any such model should include all aspects of robot behaviour suspected to impact population state. Here we suggest: mobility, energy sharing policy and recharging as well as energy consumption. Deterministic models appear as differential equations, as discrete state transition systems or combinations of the two former when hybrid modelling is applied. Common to deterministic models is their ability to provide exact answers to exactly formulated questions. That is, when all pre-conditions are exactly stated the future may be exactly predicted. When pre-conditions are only partly known, non-deterministic modelling in the shape of differential inclusions or non-deterministic state transition systems, may be applied. The precision of non-deterministic models follows the precision by which preconditions are given. From a deterministic modelling perspective, large groups of interacting mobile robots correspond to a highly complex nonlinear hybrid model, which is likely to be highly sensitive to pre-conditions, i.e. chaotic. Thus, any imprecision in pre-conditions would turn results from such a model useless even for moderate time horizons. On the other hand chaotic systems like large interacting robot populations are likely to possess so called mixing properties. Mixing implies, that from partly known pre-conditions, almost any future development is possible even within moderate time horizons. Thus precision of answers Recent Advances in Multi-Robot Systems 204 Recent Advances in Multi-Robot Systems 208