Mission based Optimum System Selector for Bio-inspired Unmanned Untethered Underwater Vehicles

Abstract

This paper is a part of the Nature in Engineering for Monitoring the Oceans (NEMO) project, investigating bio-inspiration to improve the performance of Unmanned Untethered Underwater Vehicles (UUUVs). Since biological systems (i.e. marine animals) are natives to the oceans, successfully surviving through time, they have been the source of this approach. NEMO's earlier investigations highlighted biological capabilities desirable for UUUV operations, including speed, speed range and manoeuvrability. These are significantly superior compared to current engineered systems. However, not all desirable characteristics are evident in the same species. Considering the mismatch between the “missions” of biological and engineered systems, no single specific biological system is able to fulfil all the desired UUUV mission requirements. Therefore, means are required to obtain the myriad of information from the biological world and adjust them to engineering needs. This paper describes the algorithm of an Optimum System Selector (OSS) demonstrating its methodology and explaining modules such as estimating the drag of biological systems and indication of their propulsive efficiency. The OSS is implemented to output the appropriate combination for a bio-inspired UUUV design, based on its mission. The OSS comprises missions as inputs, the decision maker, and the outputs. Mission profiles also account for capabilities unique to biological systems such as high manoeuvrability. The decision maker takes into account three main modules; speed and propulsion, manoeuvrability and upright stability. The fitness-for-purpose function of the selector consists of the energetic cost of the proposed combination, as well as the trade-off between the three modules due to the multi-functionality of the biological systems. The output consists of body and control surfaces design, propulsion and manoeuvring systems. Through this method, OSS is an excellent guide to transform complex biological data for the future design and development of UUUVs.