Cells and Robots: Modeling and Control of Large-Size Agent Populations (Springer Tracts in Advanced Robotics, 32)

__Cells and Robots__ is an outcome of the multidisciplinary research extending over Biology, Robotics and Hybrid Systems Theory. It is inspired by modeling reactive behavior of the immune system cell population, where each cell is considered as an independent agent. In our modeling approach, there is no difference if the cells are naturally or artificially created agents, such as robots. This appears even more evident when we introduce a case study concerning a large-size robotic population scenario. Under this scenario, we also formulate the optimal control of maximizing the probability of robotic presence in a given region and discuss the application of the Minimum Principle for partial differential equations to this problem. Simultaneous consideration of cell and robotic populations is of mutual benefit for Biology and Robotics, as well as for the general understanding of multi-agent system dynamics. The text of this monograph is based on the PhD thesis of the first author. The work was a runner-up for the fifth edition of the Georges Giralt Award for the best European PhD thesis in Robotics, annually awarded by the European Robotics Research Network (EURON). front-matter.pdf......Page 1 Introduction......Page 13 Analogy Between an Individual Robot and a Cell......Page 14 Robot Teams and Cell Populations......Page 16 Related Work......Page 17 Book Outline......Page 19 Immune System and T-Cell Receptor Dynamics of a T-Cell Population......Page 21 Surface T-Cell Receptor Dynamics in a Mixture of Interacting Cells......Page 22 T-Cell Receptor Triggering Experimental Setup......Page 24 Summary......Page 26 Problem Formulation......Page 27 T-Cell Hybrid Automaton Model......Page 28 T-Cell Population Hybrid System Model......Page 30 Micro-Agent Individual Model......Page 32 Stochastic Micro-Agent......Page 33 Summary......Page 35 Statistical Physics Background......Page 36 Micro-Agent Population Dynamic Equations......Page 38 Summary......Page 45 T-Cell Receptor Dynamics: A Numerical Example......Page 46 Micro-Agent vs. Ordinary Differential Equation Model......Page 51 T-Cell Receptor Expression Dynamics Model Test......Page 53 T-Cell Receptor Dynamics in $Conjugated$ State......Page 57 Model Hypothesis Test......Page 59 Parameter Identification......Page 61 Summary......Page 62 Stochastic Micro-Agent Model Uncertainties......Page 64 Discrete Parameter Uncertainty Case......Page 65 Continuous Parameter Uncertainty Case......Page 71 Numerical Example......Page 74 Summary......Page 77 Stochastic Modeling and Control of a Large-Size Robotic Population......Page 78 Robotic Population Mission Scenario......Page 79 Robotic Population Position Prediction......Page 82 Robotic Population Optimal Control Problem......Page 84 Example of Using the PDE Minimum Principle for Robotic Population Control......Page 88 Complexity of Numerical Optimal Control......Page 93 Numerical Optimal Control......Page 95 Summary......Page 100 Conclusions and Future Work......Page 101 Stochastic Model and Data Processing of Flow Cytometry Measurements......Page 106 Probability Density Estimation Algorithm......Page 108 Richardson-Lucy Deconvolution Algorithm......Page 112 Estimated T-Cell Receptor Probability Density Function......Page 115 Steady State T-Cell Receptor Probability Density Function and Average Amount......Page 119 Optimal Control of Partial Differential Equations......Page 121 References......Page 125 Backmatter......Page 132 Index......Page 130

cells And Robots Is An Outcome Of The Multidisciplinary Research Extending Over Biology, Robotics And Hybrid Systems Theory. It Is Inspired By Modeling Reactive Behavior Of The Immune System Cell Population, Where Each Cell Is Considered As An Independent Agent. In Our Modeling Approach, There Is No Difference If The Cells Are Naturally Or Artificially Created Agents, Such As Robots. This Appears Even More Evident When We Introduce A Case Study Concerning A Large-size Robotic Population Scenario. Under This Scenario, We Also Formulate The Optimal Control Of Maximizing The Probability Of Robotic Presence In A Given Region And Discuss The Application Of The Minimum Principle For Partial Differential Equations To This Problem. Simultaneous Consideration Of Cell And Robotic Populations Is Of Mutual Benefit For Biology And Robotics, As Well As For The General Understanding Of Multi-agent System Dynamics.

the Text Of This Monograph Is Based On The Phd Thesis Of The First Author. The Work Was A Runner-up For The Fifth Edition Of The Georges Giralt Award For The Best European Phd Thesis In Robotics, Annually Awarded By The European Robotics Research Network (euron).

"Cells and Robots is an outcome of the multidisciplinary research extending over Biology, Robotics and Hybrid Systems Theory. It is inspired by modeling reactive behavior of the immune system cell population, where each cell is considered as an independent agent. In our modeling approach, there is no difference if the cells are naturally or artificially created agents, such as robots. This appears even more evident when we introduce a case study concerning a large-size robotic population scenario. Under this scenario, we also formulate the optimal control of maximizing the probability of robotic presence in a given region and discuss the application of the Minimum Principle for partial differential equations to this problem. Simultaneous consideration of cell and robotic populations is of mutual benefit for Biology and Robotics, as well as for the general understanding of multi-agent system dynamics."--Jacket This monograph has arisen from the multidisciplinary research extending over biology, robotics and hybrid systems theory. It is inspired by modeling reactive behavior of the immune system cell population, where each cell is considered an independent agent. The authors formulate the optimal control of maximizing the probability of robotic presence in a given region and discuss the application of the Minimum Principle for partial differential equations to this problem.