Many systems undergo significant architecture‐level change throughout their lifecycles in order to adapt to new operating and funding contexts, to react to failed technology development, or to incorporate new technologies. In all cases early architecture selection and technology investment decisions will constrain the system to certain regions of the tradespace, which can limit the evolvability of the system and its robustness to exogenous changes. In this paper we present a method for charting development pathways within a tradespace of potential architectures, with a view to enabling robustness to technology portfolio realization and later architectural changes. The tradespace is first transformed into a weighted, directed graph of architecture nodes with connectivity determined by relationships between technology portfolios and functional architecture. The tradespace exploration problem is then restated as a shortest path problem through this graph. This method is applied to the tradespace of in‐space transportation architectures for missions to Mars, finding that knowledge of pathways through the tradespace can identify negative coupling between functional architectures and particular technologies, as well as identify ways to prioritize future technology investments.

Abstract-We study the problem of distributed coverage control in a network of mobile agents arranged on a line. The goal is to design distributed dynamics for the agents to achieve optimal coverage positions with respect to a scalar density field that measures the relative importance of each point on the line. Unlike previous work, which implicitly assumed the agents know this density field, we only assume that each agent can access noisy samples of the field at points close to its current location. We provide a simple randomized protocol wherein every agent samples the scalar field at three nearby points at each step and which guarantees convergence to the optimal positions. We further analyze the convergence time of this protocol and show that, under suitable assumptions, the squared distance to the optimal coverage configuration decays as O(1/t) with the number of iterations t, where the constant scales polynomially with the number of agents n. We illustrate these results with simulations.

Making architectural decisions in long lifecycle systems is challenging because the time between system definition and end of operations can span multiple decades, resulting in shifts in stakeholder needs and major advances in technologies. Space based communications using relay satellite constellations is one such example, requiring substantial up-front planning to define capabilities and size capacity due to the large investment of time and resources. Additionally, there are numerous viable system architectures. In this paper, we develop a graph-based decision method to assess and explore architectural flexibility in the future evolution of long lifecycle systems. The tradespace graph defines edges between similar architectures, quantifies the switching cost between architectures, using graphs to analyze the potential system evolution pathways. In a test case on NASA communication satellites, we find that hosting government communications payloads, in particular optical payloads, on commercial satellites could reduce cost and increase flexibility of the NASA network.

Abstract-In this paper we demonstrate a distributed coverage control method for a network of mobile agents moving in one dimension along a scalar information density field. The method requires each agent to take a finite number of measurements of the density field in the interval between its two neighbors and calculate its next position in order to drive the network nearer to the configuration for optimal coverage. We derive several results relating to the equilibrium properties of the sensor network and the convergence properties near fixed points. We illustrate with simulations of the algorithm.