Multi-Attribute Tradespace Exploration (MATE) for Survivability is introduced as a general methodology for survivability analysis and demonstrated through an application to a satellite radar system. MATE for Survivability applies decision theory to the parametric modeling of thousands of design alternatives across representative distributions of disturbance environments. Survivability considerations are incorporated into the existing MATE process (i.e., a solution-generating and decision-making framework that applies decision theory to model-based design) by applying empirically-validated survivability design principles and value-based survivability metrics to concept generation and concept evaluation activities, respectively. MATE for Survivability consists of eight iterative phases: (1) define system value proposition, (2) generate concepts, (3) specify disturbances, (4) apply survivability principles, (5) model baseline system performance, (6) model impact of disturbances on dynamic system performance, (7) apply survivability metrics, and (8) select designs for further analysis. The application of MATE for Survivability to satellite radar demonstrates the importance of incorporating survivability considerations into conceptual design for identifying inherently survivable architectures that efficiently balance competing performance metrics of lifecycle cost, mission utility, and operational survivability. Nomenclature A T= threshold availability ∆V = change in velocity, m/s k i = multi-attribute utility scaling factor for attribute i TAT = time above critical value threshold, years T dl = time of design life, years U e = emergency utility threshold (zero by definition), utilities are dimensionless U i (x i ) = single-attribute utility function over attribute x i U ‾ L = time-weighted average utility loss from design utility, U 0 ‾ U t = time-weighted average utility U(t) = utility delivery over time; multi-attribute utility trajectory U(x) = multi-attribute utility function over attributes x at a point in time U x = required utility threshold
Our prior work [1] presented a decentralized algorithm for coordinating the construction of truss shaped objects out of multiple components (rods and connectors). In this paper, we consider how to transfer the theory to practice, implementing the algorithm to create a decentralized multi robot construction system. The system is composed of mobile manipulators and smarts parts with an embedded communication device. We discuss the delivery and assembly algorithms that comprise this system and the assumptions behind them. We present data from extensive hardware experiments with 4 robots coordinating an assembly task.
Multi-Attribute Tradespace Exploration for
Abstract-This paper presents a constraint-aware decentralized approach to construction with teams of robots. We present an extension to existing work on a distributed controller for robotic construction of simple structures. Our previous work described a set of adaptive algorithms for constructing truss structures given a target geometry using continuous and graph-based equal-mass partitioning [1], [2]. Using this work as a foundation, we present an algorithm which performs construction tasks and conforms to physical constraints while considering those constraints to parallelize tasks. This is accomplished by defining a mass function which reflects the priority of part placement and prevents physically impossible states. This mass function generates a set of pointmasses in R n , and we present a novel algorithm for finding a locally optimal, equalmass, convex tessellation of such a set.
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