An holistic bio-inspired approach for improving the performance of unmanned underwater vehicles

Abstract

This research, as a part of the Nature in Engineering for Monitoring the Oceans (NEMO) project, investigated bio-inspiration to improve the performance of Unmanned Underwater Vehicles (UUVs). Initially, the capabilities and performance of current AUVs were compared with Biological Marine Systems (BMSs), i.e. marine animals (Murphy & Haroutunian, 2011). This investigation revealed significant superiority in the capabilities of BMSs which are desirable for UUVs, specifically in speed and manoeuvring. Subsequently, an investigation was carried out on BMSs to find means to make use of their superior functionality towards engineering improved UUVs. It was discovered that due to a mismatch between the purpose of each species evolution and the desired mission of an UUV, all desired characteristics are not evident in a single species. Moreover, due to the multi-functionality of biological systems, it is not possible to independently study each configuration. Therefore, an holistic approach to study BMSs as a system with numerous configurations was undertaken. An evolutionary search and selection algorithm was developed to obtain the myriad of biological information and adjust them to engineering needs (Haroutunian & Murphy, 2012). This Optimum System Selector (OSS) was implemented to output aspects of the appropriate design combination for a bio-inspired UUV, based on its specified mission. The OSS takes into account the energetic cost of the proposed combination as well as the trade-off between size, speed and manoeuvrability. Appreciating the uncertainty in existing measured biological data, the developed code was successfully verified in comparison with BMSs data. Energetic cost of transport is a key factor in selecting a design combination based on desired missions. This is key to the accuracy of the algorithm. Therefore, in another essential research theme, a sophisticated study has been carried out on the understanding, calculating, predicting and comparison of various biological and engineered underwater systems energetics (Phillips et al., 2012). The results of the OSS compared with existing AUVs, showed improvements in the overall capabilities. Therefore, this method is an excellent guide to transform complex biological data for the future design and development of UUVs.