sensors Review Research Trends and Future Perspectives in Marine Biomimicking Robotics Jacopo Aguzzi 1,2, * , Corrado Costa 3, * , Marcello Calisti 4,5 , Valerio Funari 2,6 , Sergio Stefanni 2 , Roberto Danovaro 2,7 , Helena I. Gomes 8 , Fabrizio Vecchi 2 , Lewis R. Dartnell 9 , Peter Weiss 10 , Kathrin Nowak 11 , Damianos Chatzievangelou 12,† and Simone Marini 13 1 Department of Renewable Marine Resources, Instituto de Ciencias del Mar (ICM-CSIC), 08003 Barcelona, Spain 2 Stazione Zoologica Anton Dohrn (SZN), 80122 Naples, Italy; valerio.funari@bo.ismar.cnr.it (V.F.); sergio.stefanni@szn.it (S.S.); r.danovaro@univpm.it (R.D.); fabrizio.vecchi@szn.it (F.V.) 3 Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), 00015 Rome, Italy 4 The BioRobotics Institute, Scuola Superiore Sant’Anna (SSAA), 56127 Pisa, Italy; marcello.calisti@santannapisa.it 5 Lincoln Institute for Agri-food Technology (LIAT), University of Lincoln, Lincoln LN6 7TS, UK 6 Consiglio Nazionale delle Ricerche (CNR), Istituto di Scienze Marine (ISMAR), 40129 Bologna, Italy 7 Department of Life and Environmental Science, Università Politecnica delle Marche, 60121 Ancona, Italy 8 Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK; Helena.Gomes1@nottingham.ac.uk 9 School of Life Sciences, University of Westminster, London W1W 6UW, UK; lewis.dartnell@gmail.com 10 Spartan Space, 13275 Marseille, France; p.weiss@spartan-space.com 11 Compagnie Maritime d’Expertises (COMEX), 13275 Marseille, France; k.nowak@comex.fr   12 Department of Physics and Earth Sciences, Jacobs University, 28759 Bremen, Germany; damchatzi@gmail.com 13 Consiglio Nazionale delle Ricerche (CNR), Istituto di Scienze Marine (ISMAR), 19032 La Spezia, Italy; Citation: Aguzzi, J.; Costa, C.; Calisti, simone.marini@sp.ismar.cnr.it M.; Funari, V.; Stefanni, S.; Danovaro, * Correspondence: jaguzzi@icm.csic.es (J.A.); corrado.costa@crea.gov.it (C.C.) R.; Gomes, H.I.; Vecchi, F.; Dartnell, † Last active affiliation. L.R.; Weiss, P.; et al. Research Trends and Future Perspectives in Marine Abstract: Mechatronic and soft robotics are taking inspiration from the animal kingdom to create Biomimicking Robotics. Sensors 2021, new high-performance robots. Here, we focused on marine biomimetic research and used innovative 21, 3778. https://doi.org/ 10.3390/s21113778 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 Academic Editor: Antonio M. Pascoal 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, Received: 14 April 2021 whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational Accepted: 27 May 2021 soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this Published: 29 May 2021 scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural Publisher’s Note: MDPI stays neutral processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks with regard to jurisdictional claims in while, at the same time, the natural designs must increasingly inform to optimize energy consumption. published maps and institutional affil- iations. Keywords: marine biomimetics; bibliographic statistics; energy provision; biomaterials; locomotor designs; optimal energy consumption Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. 1. Introduction This article is an open access article We are experiencing a “robotics revolution” [1] under the paradigm of mechatron- distributed under the terms and conditions of the Creative Commons ics, which combines mechanical, computer, telecommunications, and control engineering, Attribution (CC BY) license (https:// all multidisciplinarily integrated for the creation of autonomously operating robots [2]. creativecommons.org/licenses/by/ 4.0/). Sensors 2021, 21, 3778. https://doi.org/10.3390/s21113778 https://www.mdpi.com/journal/sensors Sensors 2021, 21, 3778 2 of 21 An entirely new class of flying, walking, and swimming robots is currently under develop- ment [3]. Unprecedented robotic capabilities are being implemented, likely contributing to the next industrial era in worldwide economies, including the marine sector [4]. The term biomimicking refers to the use of Nature as inspiration for the design and development of technological systems, as a promising methodology to generate a new breed of autonomous machines [5,6]. In the last decades, marine mechatronic is taking inspiration from several animal phyla to create mobile robots [7–9]. To give a few examples, salamanders [10], insects [11], birds [12], swimming robo-tuna, -salmon, -manta, and -jellyfish (respectively by [13–16] and their walking counterparts as the 6-legged Crabster CR200 and Silver 2, or robo-lobsters [17,18], have been created. Additionally, SCUBA droids [19], remotely controlled through immersive 3D visual interfaces [20], are biomimicking human functionalities to be used for remote in situ operations [21]. Marine robotic development is subjected to energy provision and traditional tools for that task are batteries, chemical fuel cells and (super)capacitors [22]. New biologically- inspired reactors such as microbial fuel cells (MFC) and other bioelectrochemical systems (BES) are being proposed as reservoirs to powering robotic platforms [23,24]. MFC and BES are biologically catalyzed electrochemical systems, in which microorganisms perform the oxidation and reduction of raw chemical substrates (as nutrients) at the electrodes, to generate energy for motion, sensing and computation [25,26]. In this scenario, environmental sciences benefit from such a development in robotics, including the biomimicking sector, to provide new data picturing the complex changes in natural and human-impacted ecosystems [27]. Robotics provide a generous contribution to a breakthrough in “Earth System Science”, embracing the idea that geosphere, hydrosphere, atmosphere, and anthroposphere are interconnected into global change frameworks [28,29]. Therefore, new robotic solutions are increasingly capable of operating without human supervision across ecosystems and under virtually any habitat condition [30] in a coop- erative and intercommunicating mode [31–34]. Robots are being used for a wide range of agricultural, industrial, and broad environmental activities, e.g., [35–37], including forefront deep-sea and extra-terrestrial operational scenarios, e.g., [31,38–40]). For exam- ple, in the oceanic domains, robots’ data-gathering provides long-lasting time series on species presence and their abundances and environmental data in geographically and three-dimensionally extended water column-seabed scenarios [21,31,40]. This highly inte- grated spatiotemporal monitoring is finally opening new possibilities to understand how environmental processes shape life responses in cause-effects relationships, e.g., [41,42]. Here, we used innovative bibliographic statistics tools for information content met- rics and mapping as the traditional survey approaches, based on some studies selected from the literature, often do not fully represent the state of the art of a specific scientific domain. They produce a bias, caused by the arbitrary choice of the discussed studies, that do not offer a comprehensive background for theory development [43,44]. As a conse- quence, many surveys are based on the systematic study of the literature, across different scientific domains, like building engineering [45], business and marketing [46], waste management [47] and medicine [48]. The statistical approach proposed in this work is based on [49–51] and aimed to highlight the most promising bio-inspired robotics marine research areas, identifying temporal trends, some established and emerging research tar- gets, and the degree of international cooperation. We focused on marine biomimetics for three main reasons: (i) ocean exploration is challenging and the exploration and monitoring require the development of innovative platforms technologies, also fulfilling the needs for space research, e.g., [52,53]; (ii) underwater organisms show unique adaptations to extreme conditions, which could be of inspiration for entirely novel robotic developments; and fi- nally, (iii) marine organisms have dynamic reactions when they perceive the presence of current platforms [54–59] as remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs) and internet operated vehicles (IoVs) as crawlers, so that the biomimetic development will improve the exploration capability, to picture biodiversity to an extent never attained before. Sensors 2021, 21, 3778 3 of 21 2. Materials and Methods 2.1. Database Search The Scopus database was consulted on 27 August 2020 and used to retrieve biblio- graphic records related to biomimicking for 1950–2020. The search was restricted to scientific publications written in English, avoiding biomedicine and agricultural oriented results. To identify relevant publications, the following keywords were used in the combined fields of title, abstract and keywords (per publication): TITLE-ABS-KEY (bioinspired OR bioinspiration OR “biologically-inspired” OR biophysics OR bionics OR biomimicking OR biomimics OR biomimetics OR biomimic OR biomimetic OR bio-mimicking OR bio- inspired OR “microbial fuel cell” OR “bio fuel cell” OR bioenergy OR bio-energy) AND (marine OR sea OR underwater OR water OR ocean OR exocean OR exo-ocean OR astrobi- ology OR moon OR moons OR “planets” OR deep-sea OR deepsea OR acid-mine-drainage) AND (design OR cybernetics OR robot OR mechatronic OR vehicle OR engineering OR mechanical OR technologies OR materials) AND NOT (agricultural OR agriculture OR tumour OR orthopaedic OR fouling OR pharmacology OR medicine OR tumor OR an- tibiotics OR lubricants OR lubricant OR dentist OR dentistry OR dental OR dentin OR surgical OR bone OR surgery OR disease OR clinic OR clinical OR “biomedical device” OR biomedicine OR solution OR endoprosthesis OR trauma OR orthopaedic OR surgery OR “in vitro” OR drug OR anticoagulant OR inflammatory OR “oil spill” OR saliva OR salivary OR therapy OR healing OR wound OR europium OR urban OR megacity OR megacities OR child OR children OR dietary OR nurse OR “medical imaging” OR tomography OR “magnetic resonance” OR x-ray OR nutrition OR pregnancy OR “body water” OR nursing OR infancy OR “comparative study”) AND PUBYEAR >1950. The publications resulting from the query were validated in order to avoid false positives, especially in the cases were the singular and plural forms of a term might bear a different meaning. 2.2. Bibliometric Mapping and Clustering This study is based on the latest advancements in science mapping, to provide the most comprehensive and systematic review of bio-inspired robotics marine research. While a traditional narrative review may base its findings on 50–200 studies subjectively chosen by a researcher, this study uses the entire Elsevier Scopus database with a statistically formal approach that has slimmed down a list of 6980 articles on since 1950. The results are robust and reproducible, which infers the reliability of the offered methodology. This is a modern and quantitative way of reviewing a subject area [60]. A general quantitative description of the bibliographic records was conducted, based on all the publications returned by the query. A word cloud of country names with different font sizes as a proxy of the number of publications (based on co-authors affiliations), was constructed. The number of publications for a single country was computed considering all the co-authorships of each published item. Bibliometric maps were created on retrieved publications, using the VOSviewer software version 1.6.5.0. The software was specifically developed for creating, visualizing and exploring science’s bibliometric maps [61]. A term-map, also called co-word map, is a two-dimensional representation of a research field. The larger the number of publications in which two terms occurring in titles, abstracts and keywords, co-occur, the stronger the terms are related [49–51,62]. In a term-map, strongly related terms are located close to each other, and the weaker the relationship is between terms, the bigger the distance is between them [63]. For this purpose, VOSviewer uses a clustering technique that can be seen as a kind of weighted variant-of-modularity-based clustering [64,65]. After the clustering, terms that belong to the same domain of knowledge have the same color. Only terms occurring at least eight times were extracted from the retrieved publications and a subset of more relevant labels was displayed in the maps to avoid text overlapping. Before starting with the analysis in VOSviewer, a thesaurus was created to ensure consistency for different term spelling and synonyms. As an example, “biomimetic”, Sensors 2021, 21, 3778 4 of 21 “biomimetic approach”, “biomimicry” or “biomimetic system” were listed as synonymous with “biomimetic”. Another example was “energy”, “energy production”, “energy source” or “power” substituted with “energy” or “microbial fuel cells”, “microbial fuel cells tech- nology”, “MFC”, “MFCs”, “MFC system”, and “MFC technology” all named as “microbial fuel cells”. A final example was represented by “fish robot”, “robot fish” or “robotic fish” collectively named as “robotic fish”. For the scope of this paper, we prepared three types of maps: (i) a term-clustering map; (ii) a term-year map, and (iii) a term-citation map. 2.3. The Term-Year Map For the term-year map, the color of a term indicates the average publication year of all the publications in which the term occurs. As for the previous term-citation map, we used colors that range from blue (mean year term presence 2011 or earlier), through yellow (2014), to red (2016 or later). Therefore, blue terms mainly occur in older publications, while red terms are in more recent publications. 2.4. The Term-Citation Map In the term-citation map, the color of a term is determined by the average citation im- pact of the publications where the term occurs. The number of citations of each publication is divided by the average number of citations of all publications appearing in the same year, to avoid biases related to the age of a publication (older publications are expected to be more cited). This produces a publication’s normalized citation score range from 0 to 2.5. In the map, the colors are assigned according to the score, ranging from blue (average score of 0) to green (average score of 1.25) to red (average score of 2.5). Therefore, a blue (cold) or red (hot) term indicates low and high average citation impacts, respectively [66]. 2.5. Statistical Analyses The mean values of the average publication year or term-citation map for each group obtained by the cluster analysis was tested on the null hypothesis of the same median (Kruskal-Wallis Test) being the distributions not normal (Shapiro-Wilk Test) and homoscedastic (Levene Test); Mann-Whitney pairwise comparisons and Bonferroni cor- rected Post Hoc Test were applied. Basic statistics were performed using the software PAST [67]. 3. Results 3.1. Journals, Subject Categories and Countries A total of 6980 publications were retrieved from the Scopus database encompassing 70 years (i.e., 1950–2020). The selected pool of scientific references includes 63.1% of them are research papers, while the remaining 36.9% are distributed in this way: 23.7% of conference papers, 8.9% reviews, 3.1% book chapters, and 1.3% is of miscellaneous origin (e.g., letters to editors, short communications, notes). The query results showed that a total of 146 journals published at least eight or more papers in the field of biomimicking. The top ten journals, in terms of the number of papers, are reported in Table 1, and the top three are the following: Bioresource Technology (1991; n = 167; 2.39%) Bioinspiration and Biomimetics (2006; n = 147; 2.11%) and Proceedings of SPIE the International Society for Optical Engineering (1963; n = 107; 1.53%). Early biomimicry papers were published in general engineering/electronics journals, and then as the field developed, more papers were published in dedicated biomimetics journals as they came into existence. The complete list of publications numbers per subject category is reported in Table A1, Appendix A. Optical Engineering (1963; n = 107; 1.53%). Early biomimicry papers were published in gen- eral engineering/electronics journals, and then as the field developed, more papers were published in dedicated biomimetics journals as they came into existence. The complete Sensors 2021, 21, 3778 5 of 21 list of publications numbers per subject category is reported in Table A1, Appendix A. Table 1. The top ten journals, in terms of year of official foundation, number of papers (n) pub- lished from 1950 to Table 2020 on biomimicking 1. The along top ten journals, with their in terms relative of year frequency of official (%).number of papers (n) published foundation, from 1950 to 2020 on biomimicking along with their relative frequency (%). Rank Journal Year n % 1 Rank Bioresource Technology Journal Year 1991 167 n 2.39 % 2 1 Bioinspiration and Biomimetics Bioresource Technology 1991 2006 147 167 2.11 2.39 3 Proceedings of 2SPIE the International Society Bioinspiration and for Optical Engineering Biomimetics 2006 1963 107 147 1.53 2.11 4 ACS Proceedings Applied Materials of SPIE and the International Interfaces 2009 105 1.50 3 1963 107 1.53 5 Journal ofSociety BionicforEngineering Optical Engineering 2004 80 1.15 4 ACS Applied Materials and Interfaces 2009 105 1.50 6 5 Langmuir Journal of Bionic Engineering 2004 1985 70 80 1.00 1.15 7 6Environmental Science Langmuir and Technology 1985 1967 48 70 0.69 1.00 8 7 Environmental Advanced Science and Technology Materials 1967 1989 48 46 0.66 0.69 9 8 Advanced Biomass and Bioenergy Materials 1989 1991 46 46 0.66 0.66 9 Biomass and Bioenergy 1991 46 0.66 10 Water Science and Technology 1969 45 0.64 10 Water Science and Technology 1969 45 0.64 In Figure 1, the number of biomimicking publications produced within the time in- In Figure 1, the number of biomimicking publications produced within the time terval 1950 to 2020 is shown to highlight the temporal trends. Before 2004, the total num- interval 1950 to 2020 is shown to highlight the temporal trends. Before 2004, the total ber of publications was low number (350; 5.0%), of publications wasthen it drastically low (350; 5.0%), thenand continuously it drastically increased increased and continuously with a sharper slope withuntil 2019, with a sharper slope data until for 2020 2019, being with dataincomplete (asincomplete for 2020 being per the date(asofper thethe date of query). the query). FigureFigure 1. Temporal 1. Temporal trend intrend in biomimicking biomimicking publications. publications. Publications Publications in interval in the time the time1950 interval 1950with to 2020, to n being 2020, with the number n being of papers the per issued number year. of papers One shouldissued note per thatyear. data One should noteofthat for publications 2020data are for not publica- represented since tions of 2020 underestimated (i.e., are not represented the search was conductedsinceonunderestimated August 2020, and(i.e., thepublished not all search was conducted items on were for that year August included in 2020,database). the Scopus and not all published items for that year were included in the Scopus database). As represented by Asthe represented groupingbyofthe thegrouping papers of in the thepapers in the categories, top subject top subject categories, the pub- the publi- cation lication is reported is reported in Table in Table 2. Only 2. Only the first ten the first ten categories categories are reported, are reported, as they account for as they account 90.4% of the total available papers. for 90.4% of the total available papers. Sensors 2021, 21, 3778 6 of 21 sors 2021, 21, 3778 6 of 22 Table 2. Number (n) of publications per each subject category as a different discipline. Table 2. Number (n) of publications per each subject category as a different discipline Discipline Discipline n n Engineering Engineering 2985 2985 Materials Science Materials Science 1707 1707 Chemistry Chemistry 1402 1402 Chemical Engineering Chemical Engineering 1290 1290 Environmental Science 1259 Environmental Science 1259 Computer Science 1193 Computer Science 1193 Biochemistry, Genetics and Molecular Biology 1152 Biochemistry, Genetics Physics and and Molecular Biology Astronomy 1109 1152 Physics Energy Astronomy and 896 1109 Energy Mathematics 525 896 Mathematics Others 1438 525 Others 1438 Authors that published at least one paper focused on biomimicking, were affiliated Authors that published at least one paper focused on biomimicking, were affiliated to a total of 100 countries (see Table A2 for the complete country list). Figure 2 shows to a total of 100 countries (see Table A2 for the complete country list). Figure 2 shows the the word cloud chart representing the countries (in blue text), where the larger font size word cloudrepresents chart representing the countries a higher number (in blue text), of publications: where China the larger (24.1%, fontUnited n = 2183), size rep- States (18.9%, resents a higher number of publications: China (24.1%, n = 2183), United States (18.9%, n = 1714), United Kingdom (5.0%, n = 451), India (4.2%, n = 384), Germany (4.0%, n n = 364), = 1714), United Kingdom (5.0%, n = 451), India (4.2%, n = 384), Germany (4.0%, n = 364), Japan (3.7%, n = 339), South Korea (3.2%, n = 294), Italy (2.7%, n = 241), and Australia (2.6%, Japan (3.7%,nn==234). 339), South Korea (3.2%, n = 294), Italy (2.7%, n = 241), and Australia (2.6%, n = 234). Figure 2. WordFigure cloud2.chart Wordofcloud chart ofaffiliations. the authors’ the authors’The affiliations. differentThe fontdifferent sizes offont sizes of theare the countries countries are to the proportional proportional to the number of publications. The position of the country in this visual map is ran- number of publications. The position of the country in this visual map is randomized. domized. 2021, 21, 3778 Sensors 2021, 21, 3778 7 of 22 7 of 21 Sensors 2021, 21, 3778 7 of 22 Figure 3 displays Figurethe countries’ 3 displays color-coded the collaborativecollaborative countries’ color-coded clustering, with two differ- clustering, with two dif- ent major groups: Asia-North ferentFigure major 3groups:America displays (USA the countries’ Asia-North and Canada; color-coded America green) (USA and versus collaborative Europe (red). clustering, Canada; green) versus Thetwo differ- with Europe (red). partnership between The majorUSA ent partnership and groups: ChinaUSA between (andand Asia-North Hong Kong) China America (andisand (USA evidenced Hong Kong) is Canada; by the versus association evidenced green) by of(red). The the association Europe both with Canada of bothand partnershipCommonwealth with Canada betweenandUSA countries, Commonwealth and as Australia China (and countries, Hong Kong)(but not New (but as Australia is evidencedZealand). notthe by The New Zealand). association of United Kingdom bothand The withGermany United Kingdom Canada and areCommonwealth andthe core for thecountries, Germany are European the core as cluster for the in (but association European Australia not Newwith cluster in association Zealand). The Spain, France,with Spain, United and France, Kingdom Italy, and plusand Italy, Germany Asian plus are Asian countries the of corecountries of Singapore for the European Singapore and andinIndia. India.cluster However, theHowever, association with the patchy distributionpatchy Spain, of distribution France, the of the and Italy, collaborations collaborations plus (sameAsian but(same countries color of color but no Singapore no connections) connections) ofand India.coun- satellite of satellite However, the countries patchy around both distribution of red the and green clusters collaborations (sameindicates color occasional, but no temporally connections) tries around both red and green clusters indicates occasional, temporally sparse and non- of sparse satellite and coun- non-sustained sustained collaborations collaborations tries around(i.e., both red and below the (i.e., green 50 below the clusters linkages). 50 linkages). indicates occasional, temporally sparse and non- sustained collaborations (i.e., below the 50 linkages). Figure of 3. the Figureof3.the Clustering Clustering of the countries’ countries’ collaborations collaborations and theirlinks. and theirClustering reciprocal reciprocal links. is Clustering Clustering based is based Figure 3. Clustering countries’ collaborations on the authors’ and addresses. their reciprocal Different colorslinks. is based represent different on theclusters countries’ authors’ addresses. (only the first 50 on represent Different colors the authors’ addresses. different Different colors countries’ (onlyrepresent different linkagescountries’ in terms ofclusters (only theillustrated). first 50 linkages in termsclusters of relevance the first were 50 illustrated). relevance were linkages in terms of relevance were illustrated). 3.2. 3.2.The TheTerm-Clustering Term-ClusteringMapMapIdentifying IdentifyingMajor MajorResearch ResearchAreas Areas 3.2. The Term-Clustering Map Identifying Major Research Areas The The outputs of term-clustering map analysis are presentedininFigure outputs of term-clustering map analysis are presented Figure4.4.Three Threemajor major The outputs of term-clustering color-coded groups are map analysis identified based arethe on presented in level differential Figureof 4. Three major between interconnection color-coded groups are identified based on the differential level of interconnection be- color-coded each groups tween are and cluster each identified andbased among cluster the onthethe clusters. among differential level of interconnection be- clusters. tween each cluster and among the clusters. Figure4.4. Term-map Figure Term-map analysis analysis based on biomimicking biomimicking publications. publications.Different Differentcolors colorsrepresent representthe the terms terms belonging belonging to to different different clusters. clusters. Terms Terms are are represented represented by by a a circle circle (node), (node), Figure 4. Term-map analysis based on biomimicking publications. Different colors represent the whose whose diameter diameter and andlabel the the size labelrepresent size represent the number of publications, where thatappears. term appears. Top 100 linkages terms belonging to different clusters.the number Terms are of publications, represented bywhere that a circle term (node), Top 100 whose diameter linkages were were represented. represented. and the label size represent the number of publications, where that term appears. Top 100 linkages were represented. Sensors 2021, 21, 3778 8 of 21 In Figure A1, the clusters are enlarged, to show the connections among their elements better. The terms grouped in the three clusters can be summarized and hence named as energy provision (red), biomaterials (green), and design and control (blue). According to the most relevant terms, the red cluster identifies the biologically-based energy solutions (see also Figure A1A). The four top-ranked terms concerning the averaged normalized citation were: “energy” (9635), “microbial fuel cell” (7398), “wastewater” (5663), and “electricity” (4103). The terms refer to processes based on “bio-energy” production (“enzyme”, “biocathode”, “biocatalyst”, “biofuel”), centred on bacteria (“bacterium”, “cul- ture” and “microbe”) and the utilization of microbial communities’ metabolism, to sustain that energy provision (e.g., “microbial fuel cell”, “bioenergy” and “energy” in general), with special attention to aerobic and anaerobic solutions (i.e., “chemical oxygen demand”, “oxygen reduction” and “anaerobic condition”). That cluster is also related to energy provi- sion based on electricity production (i.e., “biocathode”, “bioelectricity”, “electrical energy”, “electrode”, “battery”, “coulombic efficiency”, and “conversion”), including new renewable ways for its production (i.e., “gas”, “hydrogen”, “photosynthesis”). Industrial procedures (e.g., “wastewater” and “water quality”, as well as “waste”, “contamination”, “residue”, “metal” and “emission”) are also included. Operational factors affecting microbial energy provision by “microbial fuel cells” in different marine deployment areas are also evidenced (i.e., “marine sediment”, “sediment microbial fuel cells” and “benthic microbial fuel cells”), as is “ecosystem” in general. The biomaterials (green) cluster (see also Figure A1B) has a relevant aspect concerning the development of nature-inspired composites for both energy provision (green items close to the red cluster; see above) and new robotic functionalities (green items close to the blue cluster; see below). The analysis related to the four top-ranked terms for this cluster concerning the averaged normalized citation shows the following: (i) “device” (4007); (ii) “membrane” (3953); (iii) “temperature” (2528); and (iv) “fabrication” (2280). In the left part of this cluster, there are terms mainly related to energy provision as, e.g., for “charge”, “conductivity”, “salt”, “ion”, “cation”, “sulphate”, “pH value”, “acid” and “tempera- ture”. On the central and right part, terms are related to new biomimicking composites (e.g., “polymer”, “fibre”, “composite material”, “biomimetic membrane”, “biomimetic superhydrophobic surfaces”) at different size scales (e.g., “nanostructure”, “film”), cable to operate under different conditions (e.g., “resistance”, “aqueous medium”, “pressure”, “permeability”, “superhydrophobicity”). Interestingly, animal “biomimetic” approaches appear within this cluster side at the level of specific solutions as “mussel”, “gecko”, “bee- tle”, for membrane functionalities related to, e.g., adhesion (i.e., “adhesive”). In this cluster right half there are also items as “tissue” (i.e., “skin” and “shell”), or “polymer” whose engineering is bio-inspired (e.g., “silk”, “spider silk”, “cellulose”), along with “fabrication” methods (e.g., “lipid” and “lipid bilayer” or “biological material” as “peptide” and “pro- tein”) for “permeability” (with “pore” and “porosity”), “absorption” and “suspension” functionalities in “liquid water” “fog”, “aqueous medium” or “ice”. The design and control (blue) cluster (see also Figure A1C) has terms dealing with robotic “design” and “model” within all types of platforms (including the biomimicking ones), dedicated to the “environmental monitoring” as well as the “exploration” of the “marine environment”, “river”, “lake” and all “aquatic environments” in general, including also space research (“Mars”). The analysis related to the four top-ranked terms concerning the averaged normalized citation indicates the following: “design” (10857), “model” (7452), “robot” (4625) and “fish” (3698). This cluster encompasses the system control, actuators and computational features (i.e., “neural network”, “genetic algorithm”, and “algorithm”). This cluster shows a high level of elements interconnection at the level of prototype and platforms design for autonomous robotic animal-inspired solutions (“animal” and “robotic fish”, exemplified by “shark”, “jellyfish”, and “squid”). The method developments for underwater motility (“thrust” and “propulsion”) likely discriminate underwater vehicles from robots adopting more biomimicking solutions for locomotion. Advanced motility systems (e.g., “walking” and “swimming”) by biomimicked appendages (e.g., “wing”, 21, 3778 9 of 22 21, 3778 9 of 22 Sensors 2021, 21, 3778 9 of 21 appendages (e.g., appendages (e.g., “wing”, “wing”, “leg” “leg” and and “fin”) “fin”) appear appear related related to to hydrodynamic hydrodynamic simulations simulations (e.g., “particle (e.g., “particle image image velocimetry”, velocimetry”, “analytical “analytical model” model” and and “mathematical “mathematical model”), model”), with with “leg” and “fin”) appear related to hydrodynamic simulations (e.g., “particle image ve- a higher relevance a higher relevance in term of interconnection in term of“analytical locimetry”, interconnection model” than than “autonomous and“autonomous “mathematical underwater underwater vehicle”. vehicle”. model”), with a higher relevance in term of interconnection than “autonomous underwater vehicle”. 3.3. Publication 3.3. Publication Trends: Trends: Years Years and and Citation Citation Rate Rate The term-year term-year 3.3.map Publication based on Trends: Years and Citation on “biomimicking” “biomimicking” papersRate(Figure 5) presents a temporal The map based papers (Figure 5) presents a temporal trend where where energy The term-year energy provision provision map based (red cluster cluster on “biomimicking” of Figure Figure 4) at at the level papers the level fuel (Figure of fuel cells and and5) design presents a temporal trend (red of 4) of cells design and control (blue trend where cluster of energy Figure 4) provision at the (red level of cluster the of Figure design itself 4) at thethe shows level of fuel most cells and design recent and control (blue cluster of Figure 4) at the level of the design itself shows the most recent field of of research. and research. Also, control Also, the (blue the different cluster different levels of levels of Figure of elements 4) at the level elements interconnection of interconnection forthe design for eachitself each of shows of the the threethe most recent three field field of research. Also, the different levels of elements interconnection for each of the recognized clusters recognized clusters (see (see Figure Figure 4) 4) are are aa proxy proxy of of the the robotic robotic field’s field’s maturation maturation level.level. Con- Con- trol and and propulsion three propulsion methodsrecognized methods appear clusters appear as (see as the Figure the oldest 4) oldest (within are a proxy (within the blue of the robotic blue cluster), field’s cluster), biomimetic biomimetic maturation level. trol Control and propulsion methods appear as thethe oldest (within the blue cluster), biomimetic designs have designs have an an intermediate intermediate age, age, and and membranes membranes engineering engineering are are the the newest newest (along (along withwith designs have an intermediate age, and membranes engineering are the newest (along with bioenergy sustained bioenergy sustained solutions). solutions). The The different different maturation maturation level level of of the the research research scenario scenario bioenergy sustained solutions). The different maturation level of the research scenario within each within each of of the thewithin three clusters three clusters is each of the is also also three visible visible clusters for is for citations citations also visible ranges ranges (Figure for(Figure citations 6).ranges 6). The four The four top- 6). The four top- (Figure ranked terms ranked terms for fortop-ranked the red the red cluster cluster terms (energy provision) for theprovision) (energy red cluster in(energy in terms of terms ofprovision) averaged in averaged citation termsrate citation rate are: of averaged are: citation “ionic liquid” (3.72), “ionic liquid” (3.72), “biochar” rate “biochar” (2.92), are: “ionic(2.92), liquid” “water splitting” (3.72),splitting” “water “biochar” (2.91), (2.92), (2.91), and and “battery” “water (2.41). splitting” “battery” These (2.41).(2.91), Theseand “battery” four items four items for for the the(2.41). greenThese green cluster four cluster (biomaterials) items for the are: (biomaterials) are: green “salt” cluster “salt” (4.01), “biomimetic (biomaterials) (4.01), “biomimetic superhydro- are:superhydro- “salt” (4.01), “biomimetic phobic surface” (3.00), “gecko” superhydrophobic (83.56), and surface” “metal (3.00), ion” “gecko” (2.84). phobic surface” (3.00), “gecko” (83.56), and “metal ion” (2.84). The four top-ranked The (83.56), four and top-ranked “metal termsThe four top- ion” (2.84). terms for the blue cluster (design ranked and terms control) for the blueare: “filter” cluster (1.77), (design and “gravity” control) for the blue cluster (design and control) are: “filter” (1.77), “gravity” (1.07), “environmen- (1.07), are: “environmen- “filter” (1.77), “gravity” (1.07), tal monitoring” tal monitoring” (1.07), (1.07), and “insect” “environmental and “insect” (1.00). (1.07), and “insect” (1.00). monitoring” (1.00). Figure 5. Term-year map based on biomimicking publications. The scale represented the earlier Figure 5.Figure 5. Term-year Term-year map map based on based on biomimicking biomimicking publications. publications. The scale represented The scale represented the earlier the earlier (blue) or more recent (red) (blue) or more recent (red) years when the term appeared. Terms are represented by a circle (node (blue) years when theor more term recent (red) appeared. Termsyears when the term are represented appeared. by a circle (node Terms whose are represented diameter bysize and label a circle (node represent the number whose diameter and label size represent the number of publications where the term occurs. Top whose diameter and label size represent the number of publications of publications where the term occurs. Top 100 linkages were represented. where the term occurs. Top 100 linkages were represented. 100 linkages were represented. Figure 6. Term-citation Term-citation map (average normalized map (average citation) based on biomimicking publications. Figure 6.Figure 6. Term-citation map normalized citation) based (average normalized on biomimicking citation) publications.publications. based on biomimicking The scale represented the The earlier (blue) scale or represented more recent the (red) earlier years (blue) when the or more term recent appeared. (red) Terms years are when the represented term by a appeared. circle Terms (nodeTerms whose diameter The scale represented the earlier (blue) or more recent (red) years when the term appeared. and labelare represented size represent by the a circle number (node of whose publicationsdiameter where and the label term size appears. represent Top 100 the number linkages were of publica- represented. are represented by a circle (node whose diameter and label size represent the number of publica- tions where the term appears. Top 100 linkages were represented. tions where the term appears. Top 100 linkages were represented. Considering the Considering the mean mean values values of of the the average average publication publication year year for for the the three three groups groups obtained by the cluster analysis (see Figure 4), the red cluster (energy provision) showed obtained by the cluster analysis (see Figure 4), the red cluster (energy provision) showed aa more more recent recent mean mean value value (2014.4), (2014.4), followed followed by by the the green green one one (biomaterials; (biomaterials; 2014.3) 2014.3) and and Sensors 2021, 21, 3778 10 of 21 Considering the mean values of the average publication year for the three groups obtained by the cluster analysis (see Figure 4), the red cluster (energy provision) showed a more recent mean value (2014.4), followed by the green one (biomaterials; 2014.3) and the blue one (design and control; 2013.3). Considering the mean values of the average normalized citation rate for the three groups obtained by the cluster analysis (see Figure 4), the red cluster (energy provision) showed a higher normalized citation rate (1.46), followed by the green one (biomaterials; 1.30) and the blue one (design and control; 0.56). For both the publication years and citation trend, the goodness of the data was tested on the null hypothesis of the same median, being the distributions not normal (Shapiro-Wilk test; p < 0.05) and homoscedastic (Levene Test; p > 0.05); Kruskal-Wallis rejected the null hy- pothesis of equality of median. Mann-Whitney pairwise comparisons Bonferroni corrected post-hoc test showed a significant difference (p < 0.001) between all the clusters. 4. Discussion Our results pointed out that the main emerging fields of marine biomimicking robotic research are the provision of energy via microbial fuel cells, biomaterials for the devel- opment of not yet fully operational soft-robotic solutions, and a more classic bioinspired design and control, associated with a multiplication of locomotion solutions. In this sce- nario, one central aspect is the lack of long-lasting energy provision, which to date con- ditions robots’ operation autonomy. Energy generation could represent a solution via internal systems in a homeostatic interaction with the surrounding environment. For this reason, marine bio-inspiration is becoming increasingly oriented toward the identification of metabolic processes by which living organisms obtain energy from their environment and the natural designs allowing the optimization of energy consumption. 4.1. The Temporal Trends in Biomimicking Robotics Research Biomimicking robotic research has poor development prior to 2000 (i.e., only 123 out of 6980 are publications related to this subject prior that year, constituting 1.76% of the total retrieved with the query). From 2003–2004 onwards, a sharp increase occurred, which is still lasting today without signs of saturation (see Figure 1). Since 2000, there has been an increasing interest in developing technological products for the maritime and offshore industries and science, with marine robotics enabling the execution of increasingly complex industrial and military missions [37]. After 2004, U.S. military research [68] and the industrial innovation in Europe and Asia [69] broadly stimulated general robotic research. At the same time, in 2004, the U.S. Commission on Ocean Policy evaluated that a large part of the U.S. marine infrastructure was obsolete, which advocated for research toward its renewal [70]. More recent research developments are related to efforts for energy provision and storage (see next section). For example, the advent of microbial fuel cells (MFC) contributed to the publication peak in 2016 [71] and added new perspectives for energy supply, espe- cially after the appearance of the miniaturized supercapacitive MFC [72]. Also, membrane separation technology and biosorbents are some of the branches that developed quickly after the 2000s due to the interest in purification and recovery of strategic metals from complex matrices, e.g., [73,74]. The latest advances in electrodialysis have provided clues on the control of electron transfer during the treatment of different materials using MFC, e.g., [75]. In the past five years, using biocatalysts in electrode materials led to significant improvements in efficiency compared to abiotic anode [76]. Bioelectrochemical systems (BES), anode surface modifications with nanomaterials, and bacterial gene modifications are becoming prevalent approaches to improve MFC performances [77]. In this last decade, biofuel production technologies contributed to energy provision research and new nano- materials or nanotechnologies [22,77], including actions taken to improve memory storage capacity in robotic applications for data exchange [23,78]. Sensors 2021, 21, 3778 11 of 21 Another reason justifying the recent growth in robotic biomimicry research is space exploration. Such an exploration is not only oriented to Mars, but it actually includes the sub-surface oceans of Europa and Enceladus and other icy moons as equivalents to deep-sea terrestrial environments [38,53,79,80]. Proposed applications plan to use vectors to carry biomimicking probes for their delivery into specific terrestrial, atmospheric and aquatic environments, orienting biomimicking research toward the design of the structural hardware and toward the emulation of specific behavioral aspects. Platforms operative cooperation is biomimicked considering the example of sociality in insects [81–83]. For in- stance, behavioral biomimicking is being used as a reference to build swarms of travelling platforms with differential distribution of tasks in the atmosphere of Mars (e.g., the flying bees for Mars [84]). 4.2. Biomimicking Energy Provision Our results on the biomimicking design for energy provision in the underwater environment (red cluster; see Figure 4) evidenced its relationships with the conditioning needs of locomotion functionalities. That biomimicking is only partially developed at the level of metabolism-inspired functionalities, with microbial fuel cells (MFCs) becoming a hot topic in the recent literature developments (see the previous section and Figure 5). Using MFCs to produce and distribute energy is provided by the electrolytic mediators through an external circuit. So this design is far to be seen as an equivalent of metabolism in living organisms since it is not diffused throughout the robotic structures. However, robotic systems powered by MFCs are usually termed ‘gastrobots’, which means robots with a stomach [23,85]. So, organ-like structures are being conceived to provide energy, exploiting metabolic reactions to power, e.g., motility. Such an energy system often requires a pulsed or opportunistic behavior to perform tasks and sub-tasks because the energy supply cannot support a continuous mode operation [23,78]. Biochemical applications simulating metabolism in robots have been developed but not yet coupled into biomechanics designs. Under this aspect, the challenge is linked to the power density that microbial fuel cells can output: computation, locomotion and interaction, especially in harsh environments, are extremely energy demanding for the bigger robots, and novel approaches are required to reduce such energy demands [86]. The energy density (per unit of mass) is still insignificant compared to standard fuels like gasoline or Li-ion batteries, so a combination of different technologies is still required for the autonomy of robotic systems [23,78]. However, the electricity produced by the microbial fuel cell can be further stored in a capacitor for burst-mode power delivery [23]. Also, a microbial fuel cell can be transformed into a microbial electrolysis cell (MEC) [87] to produce ancillary biofuel (hydrogen energy) eventually. This type of cell may be the biotic substitute for traditional batteries to supply the required voltage. Combining these two types of microbial cells may be suited for almost entirely self-sustained motility of small weights [77]. To achieve sufficient energy performances and maintain control over physio-chemical variations (homeostasis) is challenging, such that few results have been transferred into the industry [22]. The main limiting factor is the energy requirement. Nano-research is vital to lighten the mechanical structure and “organs”, but when it comes to working with bare microbial fuel cells, volumetric energy density is an essential parameter for mobile applications [22] due to the limited spaces available inside the devices. Even if a higher gravimetric energy density than a metal-based fuel cell is generated, smaller volumetric energy density will limit the application range of those cells alone [22,88]. From this viewpoint, MFC relying on organic cathode materials with a low mass density might demonstrate limited performances [24]. Sensors 2021, 21, 3778 12 of 21 In this scenario, the metabolism-inspired functionalities for energy provision sus- taining marine platforms autonomous operability has still poor connectivity within the overall energy provision research scenario (e.g., “benthic fuel cells” and “sediment fuel cells” appearing as separated in the red cluster; see Figure A1A). The U.S. Naval Research Laboratory has used sediment MFC to harvest energy from the seafloor for low-power consuming applications like meteorological buoys [89]. Biosensors powered by MFC have drawn increasing interest due to their sustainability and cost-effectiveness, with several applications for water quality monitoring [90,91], which can be used in aquatic robots. MFC-based biosensors are being used to detect metals such as copper, chromium, and zinc, as well as organic compounds [91], although there is a need to improve their sensitivity, as they have high detection thresholds. In the next future, the design of efficient and effective metabolic engineering ap- proaches will increasingly benefit from artificial intelligence (AI) methodologies [92]. AI can picture retrobiosynthesis (a reverse engineering-like approach, where metabolism can be disentangled in terms of originating reactions and their relationship) approaches, high- lighting key reaction rules present in biological systems [93]. Moreover, machine learning techniques are used to incorporate genomic data for predicting the optimal feed substrate of MFCs [94], while machine learning can be used for modelling and controlling the temperature inside those cells [95]. 4.3. Biomimicking Materials for Robotics Biomaterials biomimicking is the replication of biological processes at the molecular level with human-made materials [96]. In this context, our results indicated that the terms in this cluster (green; see Figure 4) have an identity related to the creation of new solutions at scales ranging from macro to micro. Biomimicry is used to solve conflictual engineering problems across very different multi-functional materials size ranges to merge flexibility and resistance [97,98]. The existence of that cluster in our analysis as separated from that of the design and control (see the next section) indicates that robots planning and construction is still conceived along two independent lines: one using traditional materials in large sizes mechanically-designed platforms and one looking for new functionalities at the level of membrane and surfaces for soft robotic frontline applications. The implementation of bio-inspired materials at nanoscales is a research field parallel but still poorly connected to robotic mechanical design. Microfabrication of membranes is not yet at a sufficient level of technological development to have substantially impacted the published literature we analyzed and therefore does not appear prominently within the green cluster. The rightmost terms (“gecko”, “beetle”, etc.), which may point toward the design of bioinspired systems (blue cluster), resulted instead primarily for the micro properties of the bioinspired mechanism, e.g., adhesion [99], or the properties of fabrication process of the animals, e.g., spider silk [100,101]. Another example is represented by the study of marine organisms’ biomacromolecules (e.g., hyaluronic acid, chitin and chitosan, peptides, collagen, enzymes, algal polysaccharides). Reef fish mucus, marine adhesives and structural coloration are biomimicked to recreate adhesives, collagen, or coating-antifouling materials [102–104]. Therefore, this cluster belongs to the bottom-up approach to biomimicking: the quest here is to create new materials, technologies and devices, which replicate specific biological solutions. Those solutions have to be intended as the building blocks for further gen- eral applications (e.g., gecko adhesion for vertical locomotor functionalities). Moreover, technology can rarely grant a perfect mimicking of the natural solution. In those cases, the phenomena are abstracted at a high level, eventually instantiating concrete products with still low diffusion in robotics applications [98]. Sensors 2021, 21, 3778 13 of 21 4.4. Biomimicking Design and Control The design and control cluster (blue; see Figure 4) highlighted that most robotic appli- cations mimic innovative automated body motions in associations with the morphology of different types of aquatic animals. That quest for improved locomotion highlights the need to move beyond the state of the art of traditional robotic platforms [2,105]. The result is creating biomimetic underwater drones with thrusters-independent swimming capability, as a step forward compared to the more conventional designs such as autonomous under- water vehicles (AUVs) and gliders [106]. This is confirmed in our results (see Figure A1C) by the relevance of terms we identified in our survey, all related to animal morphological models (i.e., “jellyfish”, “shark”, “fish”, “squid”, and even “insect”) with different levels of association with mobility (“manoeuvrability”, “propulsion”, “thrust”, “mobile robot”, etc.). Also, terms like “actuators” and “sensors” and their linksappear related to locomotion effectiveness rather than generic functionality. Interestingly, our research revealed how mobility functionalities are prioritized over manipulation (i.e., robot having appendages to interact with objects). The “manipulation” term appears only on the far-right side of the biomaterial cluster (see previous section and Figure A1B,C). By considering the vast amount of research in robot manipulation and robotic grippers [107,108], and the huge bioinspired efforts carried out in creating artificial hand prosthesis [109–111], it is surprising that marine robotics manipulation is underrepresented, and this may be a promising area for future developments, especially concerning bio-ecological and industrial applications. Two reasons could lie beyond that situation. First, locomotion efforts are still far from being completed and satisfactory, and therefore no attention is directed toward underwater bioinspired manipulation. This appears less probable due to the high request for innovative grippers and manipulators employed in the underwater domain [112,113]. A second reason seems more realistic: if we consider the usual bioinspired path, peculiar, smart, or novel biological solutions should be identified by life scientists and eventually proposed to engineers. We believe this path is presently underexploited, and that additional research into animals/systems that could be used as a reference for novel bioinspired grippers and arms would be profitable. The oil industry historically developed underwater robotic manipulators to work on metal components. Still, for biological applications such as sampling delicate/fragile species, innovative solutions in soft robotics are required, e.g., [114,115]. AI is not apparent in the design and control cluster, since its development is very recent. An AI-related key question emerging from this cluster is which combination of sensorial technologies allows full autonomy in robotic functionalities. A promising research field is within the hardware devices for underwater vision. It is clear that marine animals evolved several solutions to adapt their vision system to the underwater environment [116], and sev- eral attempts were performed into bio-inspired underwater vision. Examples span from the biomimicking of vision to guide vehicles as the use of underwater light polarization- sensitivity (by shrimps [117]), to the neuro-model driving anguilliform-swimming devices (by lamprey [116]), and celestial compass-based approaches (by ants [118]). Also, non-light oriented approaches were implemented as the use of turbulence and pressure changes to reconstructs 3D panoramas (i.e., seal whiskers [119]) or the sensing of weak electric fields near objects (muddy water fishes [120]). Other applications of AI are related to swarm intelligence [121,122], neural simula- tion [123,124], and evolutionary computing [125] for controlling fleets of vehicles. Similarly, neural networks are often used to detect and localize underwater objects [126,127], fins sen- sors can inform the design of control systems of fin-driven robots [128], while bio-inspired algorithms are applied for localizing odour or chemical sources by underwater robots [129]. Sensors 2021, 21, 3778 14 of 21 4.5. Limitations of Our Bibliographic Meta-Analysis There are some limitations in the term-map analysis regarding both the production and the interpretation of results. There are mainly two kinds of limitations: those generated by the data and those imposed by the map. For the first aspect, during the creation of the bibliometric dataset, the record’s availability could have been somehow limited mainly due to the arbitrary keywords’ choice for the primary search. Also, regarding this aspect, synonyms and homonyms represent another kind of problem that could arise. Although this problem was solved with an accurate manual thesaurus polishing by merging the synonymous terms (see the methodological section “Bibliometric Mapping and Cluster- ing”), more advanced approaches can be used based on the formal conceptualization of the marine science domain [130]. For example, the Semantic Web for Earth and Environmental Terminology (SWEET) ontology [131] and the Natural Environment Research Council (NERC; https://vocab.nerc.ac.uk/ assessed on 28 May 2021) vocabulary could be used for the automated merging of the terms resulting from the abstract, titles and keyword analysis. Similarly, natural processing language techniques can also be used to identify the most relevant terms, on which the map is built up [132,133]. For the second aspect, the interpretation of a bibliometric map is not always straightfor- ward. A term-map represents a simplified version of reality, leading to loss of information and a partial representation of the investigated field [134]. VOSviewer generates such an inevitable loss of information due to the two-dimensional representation of the terms in a Euclidean space. We are aware that those mapping and clustering procedures rely on dissimilar design principles and conventions. However, the VOS mapping technique and the weighted and parameterized variant of modularity-based clustering can both be derived from the same underlying principle [64]. This justified our choice of a unified approach to mapping and clustering bibliometric networks to identify sub-fields or specific sub-topics [135]. Despite these limitations, depending on researcher errors and bibliometric mapping constraints, term-map analysis represents a valuable tool to support experts to improve their knowledge on a specific domain [135]. Author Contributions: Conceptualization, J.A., C.C. and S.M.; methodology, J.A., C.C., M.C., V.F., S.S. and S.M.; formal analysis, J.A., C.C., M.C., V.F., S.S. and S.M.; data curation, J.A., C.C., and S.M.; writing—original draft preparation, J.A., C.C., M.C., V.F., S.S., R.D., H.I.G., F.V., L.R.D., P.W., K.N. and S.M.; writing—review and editing, J.A., C.C., M.C., V.F., S.S., R.D., H.I.G., F.V., L.R.D., P.W., K.N., D.C. and S.M.; visualization, C.C. and D.C.; funding acquisition, J.A., H.I.G. and S.M. All authors have read and agreed to the published version of the manuscript. Funding: This research was developed within the framework of the Tecnoterra Associate Research UNIT (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring; MartTERA ERA-Net Cofund), and RESBIO [TEC2017- 87861-R; Ministerio de Ciencia, Innovación y Universidades; PI: J.A.]. S.M. was funded by the projects: ENDURUNS [ERC Research Grant Agreement H2020-MG-2018-2019-2020 n.824348] and by JERICO-S3 [Horizon 2020; Grant Agreement no. 871153]. H.I.G. acknowledges the University of Nottingham for her funding through an Anne McLaren Fellowship. This work also acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation to ICM (CEX2019-000928-S). Data Availability Statement: The EndNote file containing the database of the publication extracted is available online at https://doi.org/10.6084/m9.figshare.13337327 (Figshare repository, accessed on 28 May 2021). Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Sensors 2021, 21, 3778 15 of 2215 of 21 Appendix Appendix A A Figure A1. The Figure enlarged view A1. The of theview enlarged 3 clusters of the 3(A–C) clustersfrom (A–C)the term-map from analysis. the term-map analysis. Table A1. Number of Table publications (n)ofper A1. Number the complete publications (n) per list of subject the complete list categories as different of subject categories disci- as different disciplines plines (see Table 2). (see Table 2). Subject Area n Subject Area n Engineering 2985 Engineering Materials Science 1707 2985 Materials Science Chemistry 1402 1707 Chemical Engineering 1290 Chemistry Environmental Science 1259 1402 Chemical Engineering Computer Science 1193 1290 Biochemistry, Genetics and Molecular Biology 1152 Environmental Science Physics and Astronomy 1109 1259 Computer EnergyScience 896 1193 Mathematics 525 Biochemistry, Genetics and Molecular Biology 1152 Agricultural and Biological Sciences 420 Physics Earth andand Astronomy Planetary Sciences 285 1109 Immunology and Energy Microbiology 174 896 Multidisciplinary 146 Mathematics Medicine 133 525 Social Sciences Agricultural and Biological Sciences 79 420 Business, Management and Accounting 53 Earth andNeuroscience Planetary Sciences 35 285 Pharmacology, Toxicology and Pharmaceutics Immunology and Microbiology 24 174 Economics, Econometrics and Finance 22 Multidisciplinary Arts and Humanities 19 146 Health Professions Medicine 18 133 Decision Sciences 17 Social Sciences Psychology 7 79 Business, Management Veterinaryand Accounting 4 53 Dentistry 1 Neuroscience Nursing 1 35 Pharmacology, Toxicology and Pharmaceutics 24 Economics, Econometrics and Finance 22 Arts and Humanities 19 Health Professions 18 Decision Sciences 17 Sensors 2021, 21, 3778 16 of 21 Table A2. Complete list of countries and their total number of issued scientific papers (n) as well as their relative percentage. Country n % Country n % Country n % Country n % China 2183 26.573 Mexico 36 0.438 Ethiopia 6 0.073 Monaco 1 0.012 United 1714 20.864 Israel 34 0.414 Lithuania 6 0.073 Nepal 1 0.012 States North United Saudi 451 5.490 34 0.414 Oman 5 0.061 Macedo- 1 0.012 Kingdom Arabia nia India 384 4.674 Pakistan 32 0.390 Croatia 4 0.049 Peru 1 0.012 Germany 364 4.431 Ireland 31 0.377 Kenya 4 0.049 Sri Lanka 1 0.012 Japan 339 4.127 Estonia 30 0.365 Senegal 4 0.049 Tanzania 1 0.012 South 294 3.579 Indonesia 29 0.353 Slovakia 4 0.049 Venezuela 1 0.012 Korea Italy 241 2.934 Thailand 28 0.341 Tunisia 4 0.049 Australia 234 2.848 Egypt 26 0.316 Ecuador 3 0.037 South France 210 2.556 26 0.316 Luxembourg 3 0.037 Africa Canada 187 2.276 Romania 21 0.256 Macao 3 0.037 Spain 175 2.130 Argentina 20 0.243 Mauritius 3 0.037 Czech Singapore 173 2.106 20 0.243 Puerto Rico 3 0.037 Republic New Sweden 98 1.193 20 0.243 Cuba 2 0.024 Zealand Taiwan 92 1.120 Viet Nam 20 0.243 Jordan 2 0.024 Hong Kong 86 1.047 Hungary 19 0.231 Kazakhstan 2 0.024 Belgium 84 1.023 Bangladesh 16 0.195 Russia 2 0.024 Iran 84 1.023 Chile 16 0.195 Serbia 2 0.024 Netherlands 84 1.023 Nigeria 14 0.170 Antarctica 1 0.012 Switzerland 84 1.023 Colombia 11 0.134 Azerbaijan 1 0.012 Malaysia 82 0.998 Iraq 11 0.134 Barbados 1 0.012 Brazil 80 0.974 Philippines 11 0.134 Belarus 1 0.012 Bosnia and Denmark 63 0.767 Qatar 11 0.134 1 0.012 Herzegovina United Greece 61 0.743 Arab 10 0.122 Burkina Faso 1 0.012 Emirates Poland 61 0.743 Bulgaria 9 0.110 Cyprus 1 0.012 French Portugal 57 0.694 Latvia 9 0.110 1 0.012 Polynesia Russian 57 0.694 Lebanon 9 0.110 Ghana 1 0.012 Federation Libyan Arab Austria 55 0.670 Algeria 8 0.097 1 0.012 Jamahiriya Finland 52 0.633 Morocco 8 0.097 Liechtenstein 1 0.012 Turkey 44 0.536 Slovenia 8 0.097 Mali 1 0.012 Norway 42 0.511 Ukraine 7 0.085 Malta 1 0.012 References 1. 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