New Modular Product-Platform-Planning Approach to Design Macroscale Reconfigurable Unmanned Aerial Vehicles

Abstract

The benefits of a family of macroscale reconfigurable unmanned aerial vehicles to meet distinct flight requirements are readily evident. The reconfiguration capability of an unmanned-aerial-vehicle family for different aerial tasks offers a clear cost advantage to end users over acquiring separate unmanned aerial vehicles dedicated to specific types of missions. At the same time, it allows the manufacturer the opportunity to capture distinct market segments, while saving on overhead costs, transportation costs, and after-market services. Such macroscale reconfigurability can be introduced through effective application of modular product-platform-planning concepts. This paper advances and implements the Comprehensive Product Platform Planning framework to design a family of three reconfigurable twin-boom unmanned aerial vehicles with different mission requirements. The original Comprehensive Product Platform Planning method was suitable for scale-based product-family design. In this paper, important modifications to the commonality matrix and the commonality constraint formulation in Comprehensive Product Platform Planning are performed. These advancements enable the Comprehensive Product Platform Planning to design an optimum set of distinct unmanned-aerial-vehicle modules, different groups of which could be assembled to configure twin-boom unmanned aerial vehicles that provide three different combinations of payload capacity and endurance. The six key modules that participate in the platform planning are 1) the fuselage/pod, 2) the wing, 3) the booms, 4) the vertical tails, 5) the horizontal tail, and 6) the fuel tank. The performance of each unmanned aerial vehicle is defined in terms of its range per unit fuel consumption (miles/gallon). It is found that, when the average unmanned-aerial-vehicle performance (miles/gallon) and the commonality among the unmanned-aerial-vehicle variants are simultaneously maximized, a one-third reduction in the number of unique modules is accomplished at a 66% compromise in performance. On the other hand, when simultaneously maximizing performance and minimizing costs, the best tradeoff unmanned-aerial-vehicle-family designs provide a remarkable 26% reduction in cost for a 6% compromise in performance. In this case, the cost savings are attributed to both material reduction and increased module sharing across the three unmanned-aerial-vehicle variants. It is also observed that, among the best tradeoff unmanned-aerial-vehicle families, the individual unmanned aerial vehicles are most likely to share the horizontal tail and tail booms, and are least likely to share the wing.