Research Trends and Future Perspectives in Marine Biomimicking Robotics

Abstract

Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950-2020), evidencing a sharp research increase in 2003-2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

Keywords: bibliographic statistics; biomaterials; energy provision; locomotor designs; marine biomimetics; optimal energy consumption.

Figure A1

The enlarged view of the 3 clusters (A–C) from the term-map analysis.

Figure 1

Temporal trend in biomimicking publications. Publications in the time interval 1950 to 2020, with n being the number of papers issued per year. One should note that data for publications of 2020 are not represented since underestimated (i.e., the search was conducted on August 2020, and not all published items for that year were included in the Scopus database).

Figure 2

Word cloud chart of the authors’ affiliations. The different font sizes of the countries are proportional to the number of publications. The position of the country in this visual map is randomized.

Figure 3

Clustering of the countries’ collaborations and their reciprocal links. Clustering is based on the authors’ addresses. Different colors represent different countries’ clusters (only the first 50 linkages in terms of relevance were illustrated).

Figure 4

Term-map analysis based on biomimicking publications. Different colors represent the terms belonging to different clusters. Terms are represented by a circle (node), whose diameter and the label size represent the number of publications, where that term appears. Top 100 linkages were represented.

Figure 5

Term-year map based on biomimicking publications. The scale represented the earlier (blue) or more recent (red) years when the term appeared. Terms are represented by a circle (node whose diameter and label size represent the number of publications where the term occurs. Top 100 linkages were represented.

Figure 6

Term-citation map (average normalized citation) based on biomimicking publications. The scale represented the earlier (blue) or more recent (red) years when the term appeared. Terms are represented by a circle (node whose diameter and label size represent the number of publications where the term appears. Top 100 linkages were represented.