A meta-interaction model for designing cellular self-organizing systems

2012-07-30The Cellular Self-Organizing (CSO) system takes the nature inspired biological processes of self-organization and emergence towards complex, multi-agent systems. Self-organization can be observed in many natural systems, and researchers hope to harness the biological advantages of simple individuals, versatile collective functionality, and robustness. The point in being cellular is to emphasize the simple nature of each agent and the idea of a large system population. A single simple cell may not be successful on its own, but a collective system of cells can be extremely adaptable and functional. ❧ Technological development is facing increased challenges as design engineers begin to tackle problem domains with greater uncertainty. Future engineered systems must be able to function in unpredictable environments such as deep ocean, rough terrain, and outer space while performing uncertain tasks like hazardous waste cleanup and search-and-rescue missions. CSO systems can provide the adaptability in order to manage uncertainties that traditional systems cannot. As the uncertainty of the problem domain increases, engineering design methods must be advanced in order to properly address the changing needs and constraints. ❧ This thesis details a new CSO approach inspired by natural phenomena in order to extend the design envelope towards an artificial nature. While natural systems had the luxury of evolution over millions of years, achieving bottom-up adaptability by design represents a major challenge to the systems engineering and design research community. Two fundamental issues must be addressed: one is the analysis problem of predicting the global emergence from local interactions; and the second is the design problem of compiling local rules based on a desired global function. ❧ The presented approach broadens the traditional design and re-design methods by utilizing the self-organization process exhibited in natural systems. The goal is to design systems that excel in unpredictable environments where it is impossible for the designer to conceptualize every possible contingency. The key is to focus on the behaviors of the system. This work suggests a meta-behavioral model based on cellular self-organization that can be used as a design approach towards CSO systems. Specifically, interactive behaviors are keyed in on as interaction is the intrinsic property of complexity, and thus adaptability. ❧ In this CSO framework, a system is composed of multiple mechanical (e.g., robotic) cells, which self-organize themselves through individual actions and mutual interactions. To deepen our understanding and provide design methods for the development of CSO, we focus on the relationship between local agent interactions and emergent collective system behavior. More specifically, a parametric approach centered upon interactive behaviors will be used to develop a Meta-Interaction Model (MIM) of the behavioral model of agent interactions. Using the parametric approach provides tunable dynamical variables towards managing collective behavior, leading to various desired global functions. Furthermore, parameterizing local behaviors provides an opportunity to analyze the relationship between different types of local interactions in addition to the relationship between the local interaction and the collective functionality. ❧ The MIM approach is used to design for applications with uncertainty by designing with uncertainty. Instead of designing single specific capabilities, the MIM method designs for emergent functional capacities. This is a fundamental change in design theory. By doing so, designers are trading deterministic functionality for self-organizing and emergent adaptability. The MIM technique can be used to manage adaptability by specifying interaction patterns of agents in a multi-agent system thus guiding the emergence of functional capacities. ❧ A simulation based study of the Cohesion-Avoidance-Alignment-Random-Momentum (COARM) Behavioral Model is completed in order to analyze the COARM meta-model. The study shows the increased complexity with the increased behavioral set. It also proves Alignment as the stabilizing simple behavior for collective synchronized motion, although not the only attractor for such collective behavior. Random behavior demonstrates the ability to handle task application uncertainty. ❧ Ultimately, the approach introduces a new design space based on behaviors. The space is created through the parameter variables that can be tuned to control system behavior. The simulation study specifically analyzes interactive behaviors. The resulting system's multi-functionality is demonstrated through different mechanical applications. The MIM approach provides a design framework for developing CSO systems. It gives top-down control over functional bottom-up organization. Moreover, in the future, it can easily lend itself as an outlet for other artificial intelligent means such as learning, evolutionary algorithms, and control feedback