Morphogenesis in robot swarms

Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering

MetaChem: An Algebraic Framework for Artificial Chemistries

We introduce MetaChem, a language for representing and implementing Artificial Chemistries. We motivate the need for modularisation and standardisation in representation of artificial chemistries. We describe a mathematical formalism for Static Graph MetaChem, a static graph based system. MetaChem supports different levels of description, and has a formal description; we illustrate these using StringCatChem, a toy artificial chemistry. We describe two existing Artificial Chemistries -- Jordan Algebra AChem and Swarm Chemistries -- in MetaChem, and demonstrate how they can be combined in several different configurations by using a MetaChem environmental link. MetaChem provides a route to standardisation, reuse, and composition of Artificial Chemistries and their tools

Algebraic approaches to artificial chemistries

We have developed a new systematic framework, MetaChemisty for the description of artificial chemistries (AChems). It encompasses existing systems. It has the flexibility and complexity to allow for new features and new systems. A joint description language will allow comparisons to be drawn between systems. This will allow us to write metrics and benchmarks for artificial chemistries. It also enables us to combine existing systems in different ways to give a wealth of more complex and varied systems. We will be able to build novel chemistries quicker through reuse of code and features between chemistries allowing new chemistries to start from a more complex base line.We have also developed an algebraic artificial chemistry, Jordan Algebra Artificial Chemistry (JA AChem). This chemistry is based on existing algebra which is leverage to ensure features such as isomers and isotopes are possible in our system. The existence of isotopes leads naturally to the existence of elements for this chemistry. It is a chemistry with both constructive and destructive reactions making it a good candidate for further study as an open-ended system.We analyse the effect of changing probabilistic processes in JA AChem by modifying the probability spawning functions that control them. We also look at the algebraic properties of these probability spawning functions. We have described Swarm Chemistry, Sayama (2009),in the MetaChem showing it is at least more expressive than the previous framework for artificial chemistries, Dittrich et al. (2001).We use the framework to combine two artificial chemistries using a simple environment link structure to produce eight new modular AChems with a modular approach. This link structure requires minimal addition to existing code for artificial chemistry systems and no modification to most modules

Chemotaxis-based spatial self-organization algorithms

Self-organization is a process that increases the order of a system as a result of local interactions among low-level, simple components, without the guidance of an outside source. Spatial self-organization is a process in which shapes and structures emerge at a global level from collective movements of low level shape primitives. Spatial self-organization is a stochastic process, and the outcome of the aggregation cannot necessarily be guaranteed. Despite the inherent ambiguity, self-organizing complex systems arise everywhere in nature. Motivated by the ability of living cells to form specific shapes and structures, we develop two self-organizing systems towards the ultimate goal of directing the spatial self-organizing process. We first develop a self-sorting system composed of a mixture of cells. The system consistently produces a sorted structure. We then extend the sorting system to a general shape formation system. To do so, we introduce morphogenetic primitives (MP), defined as software agents, which enable self-organizing shape formation of user-defined structures through a chemotaxis paradigm. One challenge that arises from the shape formation process is that the process may form two or more stable final configurations. In order to direct the self-organizing process, we find a way to characterize the macroscopic configuration of the MP swarm. We demonstrate that statistical moments of the primitives' locations can successfully capture the macroscopic structure of the aggregated shape. We do so by predicting the final configurations produced by our spatial self-organization system at an early stage in the process using features based on the statistical moments. At the next stage, we focus on developing a technique to control the outcome of bifurcating aggregations. We identify thresholds of the moments and generate biased initial conditions whose statistical moments meet the thresholds. By starting simulations with biased, random initial configurations, we successfully control the aggregation for a number of swarms produced by the agent-based shape formation system. This thesis demonstrates that chemotaxis can be used as a paradigm to create an agent- based spatial self-organization system. Furthermore, statistical moments of the swarm can be used to robustly predict and control the outcomes of the aggregation process.Ph.D., Computer Science -- Drexel University, 201