Download Cells and Robots: Modeling and Control of Large-Size Agent by Rod Mollise PDF

By Rod Mollise

Cells and Robots is an consequence of the multidisciplinary learn extending over Biology, Robotics and Hybrid platforms conception. it truly is encouraged by way of modeling reactive habit of the immune process phone inhabitants, the place each one phone is taken into account as an autonomous agent. In our modeling procedure, there is not any distinction if the cells are certainly or artificially created brokers, reminiscent of robots. This seems to be much more obvious once we introduce a case learn referring to a large-size robot inhabitants situation. below this situation, we additionally formulate the optimum keep watch over of maximizing the likelihood of robot presence in a given area and speak about the appliance of the minimal precept for partial differential equations to this challenge. Simultaneous attention of telephone and robot populations is of mutual gain for Biology and Robotics, in addition to for the final figuring out of multi-agent approach dynamics.

The textual content of this monograph relies at the PhD thesis of the 1st writer. The paintings was once a runner-up for the 5th version of the Georges Giralt Award for the easiest ecu PhD thesis in Robotics, every year presented by means of the ecu Robotics examine community (EURON).

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Additional info for Cells and Robots: Modeling and Control of Large-Size Agent Populations

Example text

One of the most interesting results of this theory, for the problem investigated in this work, is the Maxwell-Boltzmann formula. 1) where v is the particle velocity, m is the mass of gas molecules, k is the Boltzmann constant and T is the absolute temperature. The probability density function f (v) relates the dynamics of the individual particle (micro-dynamics), represented by the particle velocity v, to the particle population macro-measurement, D. Milutinovic and P. Lima: Cells & Robots, STAR 32, pp.

The right column contains two equivalent descriptions of the CT M CμA model. The block diagram denotes that the event sequence generator is the part of the Stochastic Micro-Agent. The state diagram presents the internal structure of the Stochastic Micro-Agent and also the Markov Chain nature of the stochastic transition over the discrete states, denoted by the transition rate diagram. 6 Summary In view of biological facts, we introduce in this section the Micro-Agent model of the T-cell. This is a single-input, single-output hybrid automaton-based model and we use this model as a building block of the T-cell population model.

16. 015, Fig. 15. 5 Summary 51 Fig. 16. 015(solid line), experimental TCR PDF ρexp (x, tj ) (dashed), j = 1, 2, . . 8, Flow Cytometry data from [45] does not match better the experimental TCR PDF can be ascribed to the following reasons: • The individual T-cell TCR dynamics is more complex than the time invariant linear one. For example, the parameter k2 depends on time. • The experimental TCR PDFs are estimated incorrectly due to the incorrect stochastic measurement model. • The experimental TCR PDFs are estimated using very similar, but not identical, T-cell populations.

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