A multi-level trade-off methodology for analyzing collaborative system-of-system alternatives

Systems engineering;System of systems;Collaboration;Design;Autonomy;Linear programming;Area coverage;Capabilities based approach;Underwater vehicle design

Molino, Nicholas Anthony ; Mavris, Dimitri N. Aerospace Engineering Domercant, Jean C. Schrage, Daniel P. Feron, Eric Taylor, James S. ; Mavris, Dimitri N.

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Systems engineering;    System of systems;    Collaboration;    Design;    Autonomy;    Linear programming;    Area coverage;    Capabilities based approach;    Underwater vehicle design;   

Others  :  https://smartech.gatech.edu/bitstream/1853/53585/1/MOLINO-DISSERTATION-2015.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

As unmanned vehicle capabilities have matured, the design and development of autonomous collaborative Systems-of-Systems (SoS) has gained increased attention. This has been motivated by the indication that significant improvements in overall effectiveness may be possible by employing many systems in cooperation with one another. However, as the potential combinations of vehicles, subsystems, and operational concepts becomes increasingly large, a systematic approach is needed for designing and analyzing alternatives. Furthermore, the discrete nature of the problem can cause variations in effectiveness that are counter-intuitive, such as a point of diminishing returns as the number of systems grows.Systems-of-systems are hierarchical in nature, consisting of top-level mission requirements that are decomposed into system- and subsystem-level performance measures. The overarching research objectives of this dissertation are to show that the analysis of alternatives should be performed at varying levels of the SoS hierarchy and to provide novel means for performing those analyses. In particular, it has been postulated that a formulation built on an energy-based approach to multi-level analysis of SoS components will enable more accurate and transparent subsystem and system trade-offs. Various steps of the design process are established and argued for or against, and significant focus is placed on the analysis of alternatives.The foundation of the new method is laid on structured SoS engineering principles. The full substance comes together by incorporating unique aspects developed within this dissertation. A new virtual experimentation approach is presented for creating sensor performance representations that are functions of vehicle operations. The sonar equation is used as a baseline sensor model for comparison against the new virtual experimentation method. Dozens of forward-looking and side-scan sonar experiments are designed, and data is provided to show the extent to which typical sensor modeling over-predicts performance without vehicle operations considered. In addition, comparisons are made between possible representations of vehicle performance. An underwater vehicle sizing and synthesis process is developed to enable comparisons between system-level component modeling approaches. The experiments attest to significant gaps in accuracy when performing sensor and operational trade-offs without energy-based modeling of the collaborative vehicles. Finally, a heuristic path-planning algorithm is formulated, and mixed-integer linear programming is used to choose between alternative SoS designs.The developed method is demonstrated through a representative example problem: a group of unmanned underwater vehicles (UUVs) operating in a collaborative fashion to search for underwater objects. The example scenario provides an application for illustrating the phenomenon discussed in regards to the analysis of alternatives of collaborative SoS. The significance of providing more or less analytic detail is traced and the effect on mission requirements is quantified. Counter-intuitive results are highlighted, such as the observation that the increased energy required for systems to effectively collaborate can often out-weigh the benefits gained in overall mission effectiveness.