A Morphogenetically Assisted Design Variation Tool

Abstract

The complexity and tight integration of electromechanical systems often makes them "brittle" and hard to modify in response to changing requirements. We aim to remedy this by capturing expert knowledge as functional blueprints, an idea inspired by regulatory processes that occur in natural morphogenesis. We then apply this knowledge in an intelligent design variation tool. When a user modifies a design, our tool uses functional blueprints to modify other components in response, thereby maintaining integration and reducing the need for costly search or constraint solving. In this paper, we refine the functional blueprint concept and discuss practical issues in applying it to electromechanical systems. We then validate our approach with a case study applying our prototype tool to create variants of a miniDroid robot and by empirical evaluation of convergence dynamics of networks of functional blueprints.

A Dimensionless Graceful Degradation Metric for Quantifying Resilience

“…Example of a perturbation survey from which graceful degradation can be computed. This survey, from [13], varied one configuration parameter of a self-adaptive design assistant and measured using two metrics; the range from 0.1 to 0.35 is acceptable; below 0.02 and above 0.4 are failing.…”

“…3 This radically reduces the number of performance evaluations to be made. Figure 3 shows an example of a perturbation survey along one dimension, taken from [13], of a self-adaptive design assistant, measured on two metrics. This one-dimensional slice shows a classic profile of graceful degradation: when the parameter is too high or too low the system fails, but a broad range in the middle is acceptable and the transition from acceptable to failing is not sharp.…”

Using Morphogenetic Models to Develop Spatial Structures

“…Finally, functional blueprints [21] are an engineering approach in which a system is specified according to its desired performance, with programs to incrementally adjust structure to improve performance when the goals are not met. The work presented in this paper is complementary, providing guidance on how functional blueprints could be best represented and applied.…”