**3. Successful cases by applying SPP strategy (too turbid)**

The idea of SPP for lake and reservoir water management is new, but this does not mean that the similar strategy has never been applied by far. In this section, we will discuss two cases to show that if SPP is not applied, the water related problems cannot be solved, while if the strategy is applied, all problems become solvable.

In 1854 Dr John Snow demonstrated the connection between water supply and Cholera as shown in Fig. 4. Several years later, Thomas Hawksley in Nottingham suggested that water supply systems should be piped to prevent external pollution.

Here the suggestion of pipe transport implied the idea of "separation, prevention and protection". Like the enclosed levee shown in Fig. 3, the pipe can *separate* clean water from the polluted water, and only clean water is allowed to enter the pipe network; likewise, the pipe protects the clean water inside the system and prevents the external pollutions. Consequently, the problem of waterborne illness death rate like Cholera, typhoid fever and dysentery had rapidly dropped to a very low level as shown in Fig. 5. This is probably the first time in history using the strategy of SPP for water resources management, and the concept of pipe water has been widely accepted in the world since then.

However, for large water system, so far there is no direct example of SPP strategy in application. Sometimes, people were forced to adapt the strategy of SPP to solve their problems. One of the examples is the Sanmenxia Reservoir in the middle reach of Yellow River, China. Yellow River basin is one of the largest basins in the world, and it is notorious in the world for high sediment transport rate in the middle reach. Between 1919 and 1960, the measured data at Sanmenxia Station showed that the mean annual runoff was 42.3×109 m3 and the long-term average annual sediment load was 1.57×109 t with the average concentration of the river water being 49.8 kg/m3, the highest in the world.

Sanmenxia Reservoir is created by the Sanmenxia dam. It is a large-scale multipurpose project, and the first one constructed on the main stream of the Yellow River. The construction of the dam was started in 1957, and the impoundment of water commenced in September 1960. The dam height is 106m at the pool level 323m with the storage capacity of 3.6×109 m3.

gravity via its outlets, and the water level quickly drops after flood period. If the proposed scheme is applied, the clean water will be protected, and sufficient high quality freshwater during flood period will be kept in the lake through the drought period until the next rainy season just like a reservoir. In other words, the water level is still high even after flood period. This water can be used by the human society to sustain their developments. Most importantly, during dry periods, all sluice gates are closed, the inner levee protects the lake water against external pollution, and it also prevents unnecessary

The core idea of SPP is that the water should be separated based on its quality or turbidity; the unwanted water should be prevented mixing with the clean water, and good quality water must be stored and protected. In other words, the residence time of unwanted water in an ecosystem should be as short as possible; while the detention time of wanted water (or

The idea of SPP for lake and reservoir water management is new, but this does not mean that the similar strategy has never been applied by far. In this section, we will discuss two cases to show that if SPP is not applied, the water related problems cannot be solved, while

In 1854 Dr John Snow demonstrated the connection between water supply and Cholera as shown in Fig. 4. Several years later, Thomas Hawksley in Nottingham suggested that water

Here the suggestion of pipe transport implied the idea of "separation, prevention and protection". Like the enclosed levee shown in Fig. 3, the pipe can *separate* clean water from the polluted water, and only clean water is allowed to enter the pipe network; likewise, the pipe protects the clean water inside the system and prevents the external pollutions. Consequently, the problem of waterborne illness death rate like Cholera, typhoid fever and dysentery had rapidly dropped to a very low level as shown in Fig. 5. This is probably the first time in history using the strategy of SPP for water resources management, and the

However, for large water system, so far there is no direct example of SPP strategy in application. Sometimes, people were forced to adapt the strategy of SPP to solve their problems. One of the examples is the Sanmenxia Reservoir in the middle reach of Yellow River, China. Yellow River basin is one of the largest basins in the world, and it is notorious in the world for high sediment transport rate in the middle reach. Between 1919 and 1960, the measured data at Sanmenxia Station showed that the mean annual runoff was 42.3×109 m3 and the long-term average annual sediment load was 1.57×109 t with the average

Sanmenxia Reservoir is created by the Sanmenxia dam. It is a large-scale multipurpose project, and the first one constructed on the main stream of the Yellow River. The construction of the dam was started in 1957, and the impoundment of water commenced in September 1960. The dam height is 106m at the pool level 323m with the storage capacity of

loss of lake water.

3.6×109 m3.

clean water) should be as long as possible.

**3. Successful cases by applying SPP strategy (too turbid)**

if the strategy is applied, all problems become solvable.

supply systems should be piped to prevent external pollution.

concept of pipe water has been widely accepted in the world since then.

concentration of the river water being 49.8 kg/m3, the highest in the world.

Fig. 4. Dr. John Snow and the map he used in 1854 to identify the source of cholera; this discover leads to the first application of SPP strategy in water management

Fig. 5. Death rate of human being in the history, indicating the application of SPP could immediately reduce people's death rate

In its planning stage, engineers and decision makers used the same practice as they did for other reservoirs, i.e., no special measures had been taken to separate incoming water based on its quality, and nothing had been done to protect the reservoir water and to prevent external high turbidity water. Consequently the reservoir storage capacity was decreasing at an astonishing rate in the reservoir, which caused the unacceptable negative impact of rapid development of backwater sediment deposition. In 4 years time, the accumulative sediment silted in the reservoir reached alarming level, i.e., 4.44 ×109 m3, in other words, the reservoir's storage capacity had almost totally lost. Under such circumstance, engineers were forced to reconsider the strategy of water management, and the dam had to be reconstructed to minimize the adverse effect of reservoir sedimentation.

Novel SPP Water Management Strategy and Its Applications 249

This lake annually receives 173 million tons of sediment, 83% of which are carried into the lake from the Yangtze River, and 17% from other inflow rivers. Over the past 150 years, the lake area has reduced from about 6000km2 to about 2600km2 due to both natural siltation and human activity, consequently flood modulation of the lake capacity has been reduced significantly from about 30km3 in 1949 to 17km3 in 1995, about 50% of the lake's capacity has been silted. It is certain that the lake will continue to shrink, and in the near future it may serve only as a river channel during the non-flood reasons (Du et al. 2001). In another 50-100

Similar to other plain lakes, Dongting Lake has suffered many problems, if not solved properly, these problems may cause serious consequences to environment and bring about huge economic and life loss. These problems can be summarized as flood disasters in wet seasons (too much), water shortage for the ecosystem in dry seasons (too little), lake's

Apart from too much and too turbid problems, the lake water was polluted by the incoming wastewater due to rapid industrial development and population growth and agriculture fertilization. In this fertile "land of fish and rice", farmers every year use 18k tons of pesticide and 1700k tons excessive fertilizer, which flows into the lake via the rivers. After 1990s eutrophication tendency has been accelerated, according to the monitoring data, 1996-

2005 main pollutants are total phosphorus and total nitrogen (Guo et al., 2007). Li et al (2009) summarized the problems in the Donting Lake area as a chain:

space shrinkage flood level raised and flood disaster aggravated. Disaster in dry seasons : sedimentation marshland expansion water

Disaster in wet seasons : sedimentation marshland expansion water

shrinkage water pollution and fish resource depletion wetland and

→ →

The average reoccurrence interval (ARI) of flood disaster was 83 years from 276 B.C.-1525, and ARI = 20 years from 1525-1853; ARI = 5 years from 1853-1949 and ARI = 4 year from 1949-2000, recently this area suffers the flood disaster almost every year. Obviously this is caused by the sedimentation that keeps reducing the lake's capability for flood retention

All problems that the lake is facing can be well solved if the proposed SPP strategy is applied as shown in Fig. 6, in which the length of inner levee is about 240km. This is feasible

*1) Sedimentation*: in the current condition, almost all incoming sediment particles are deposited in the lake, i.e., 1730 million tons of sediment, and the lake functions like a sedimentation tank where the clean water run out of the tank. But with the inner dike, the incoming flow can be separated into wanted/unwanted waters, i.e., high turbid but safe floodwater will by-pass the lake and drain to the downstream. Only a small part of excessive floodwater is allowed to enter the lake, which can mitigate the flood disaster, and the majority of the water stored in the lake comes from the falling limb in the hydrograph shown in Fig. 2, thus clean and low turbid water is stored. Currently the lake's storage capacity is only 1/10 of the annual incoming water volume, if SPP is used and then the

→ →

→ →

years, this lake may totally disappear if there is no human's interference.

shrinkage (too turbidity) and water pollution (too dirty).

→

biodiversity reduction.

space

(Yang, 2004).

because:

The engineers and decision makers realized that there was no hope to solve the sedimentation problem without separation, then they divided the dam's operation into two time periods or two different modes of operation:

After the separation, the new design was found effective, till now, the dam is still providing the basin with flood control, irrigation, and hydropower generation, even though some benefits are lower than the original design. The engineering experiences and management practices of Sanmenxia Dam are valuable assets to the sustainable management of reservoirs/lakes on sediment-laden or nutrient-rich rivers. It can be seen that the above operation strategy is similar to the SPP introduced in this chapter as the unwanted water (high turbid floodwater) has a short residence time in the reservoir, and the clean water in non-flood season (wanted water) has a relative long residence time for the eco-system. This project not only demonstrated the perils of existing water management, but also demonstrated that a sediment balance may be achieved across an impounded reach even in a large reservoir built on a river with an extremely high sediment load.


**Abstract**

In recent years, we have registered for the professional and lay public several published scientific and popularization papers on the use of virtual reality in cultural institutions, especially from abroad. The applicability of the components of virtual reality at present is not only dependent on the technical or economic nature, but also on the degree of mutual teamwork forming the basic framework of the project. The presented study consists of a theoretical nature in the context of the analysis of museum virtualization with an impact on positive and negative use in practice. The purpose of the practical section is direct examples of use in practice with a connection to the educational environment.

**Keywords:** museum, virtual reality, cyber-exposition, online exhibition, source

### **1. Introduction**

The instinct of the collector and exhibitor has been present in man since the earliest phase in the form of cave paintings and sculptural art in the form of Paleolithic and later Mesolithic Venus. This trend of preserving and expanding the "art of past generations" took place in all subsequent periods and centuries. However, it gradually manifested itself as an attribute of preserving and documenting the authenticity of time through objects.

Today's exhibition language is the product of a combination of current knowledge of science and exhibition practices, knowledge and needs of the visitor of the 21st century. Among the priority needs for visitors to a modern museum is not only to learn about collection items, but also to examine them in detail, e.g. try them, touch them, or make a true copy of them. At present, the needs of visitors can be saturated with technology in the form of 3D printers, as well as the direct craft creation of castings of selected collection items.

Alternatives to adapting modern cyber technology are almost limitless; from visual presentations (from educational aids such as PowerPoint, Impress, Kyenote and others) to the technological creation itself (eg. software - graphic reconstruction of monuments and collection objects, 3D shapes and forms of architecture of cities using 3D printing, or 3D Puzzle of world cities and cultural heritage and others).

Today we can no longer talk only about the form of "help and finding solutions to problems" but it is necessary to create modern procedures (online presentation of field results associated with video calling, practical adaptation of acquired theoretical knowledge into practice, for example when researching issues using a virtual archive, verification of architectural knowledge in constructed 3D and XD virtual monuments, and the like) in combining the acquired knowledge with technological advances.


**Meet the editor**

Orhan Korhan graduated with a BS degree from the Eastern Mediterranean University in 2000, an MS degree from the University of Louisville in 2002, and a PhD from the Eastern Mediterranean University in Industrial Engineering in 2010. He has been working at the Eastern Mediterranean University since 2009. He became an assistant professor in 2010 and associate professor in 2014.

He is a board member of the Continuing Education Center and currently positioned as an academic affairs coordinator at the Rector's Office. He has been assigned to scientific committee of several international conferences and published several books, book chapters, and papers in various countries. His current research interests include work-related musculoskeletal disorders, educational ergonomics, human-computer interaction, and facilities planning and design.


**Assessment of Evolutionary Relationships for Prioritization of Myxobacteria for Natural Product Discovery**

**Andrew Ahearne 1,†, Hanan Albataineh 1,†, Scot E. Dowd 2 and D. Cole Stevens 1,\***

† These authors contributed equally to this work.

**Abstract:** Discoveries of novel myxobacteria have started to unveil the potentially vast phylogenetic diversity within the family Myxococcaceae and have brought about an updated approach to myxobacterial classification. While traditional approaches focused on morphology, 16S gene sequences, and biochemistry, modern methods including comparative genomics have provided a more thorough assessment of myxobacterial taxonomy. Herein, we utilize long-read genome sequencing for two myxobacteria previously classified as *Archangium primigenium* and *Chondrococcus macrosporus*, as well as four environmental myxobacteria newly isolated for this study. Average nucleotide identity and digital DNA–DNA hybridization scores from comparative genomics suggest previously classified as *A. primigenium* to instead be a novel member of the genus *Melittangium*, *C. macrosporus* to be a potentially novel member of the genus *Corallococcus* with high similarity to *Corallococcus exercitus*, and the four isolated myxobacteria to include another novel *Corallococcus* species, a novel *Pyxidicoccus* species, a strain of *Corallococcus exiguus*, and a potentially novel *Myxococcus* species with high similarity to *Myxococcus stipitatus*. We assess the biosynthetic potential of each sequenced myxobacterium and suggest that genus-level conservation of biosynthetic pathways support our preliminary taxonomic assignment. Altogether, we suggest that long-read genome sequencing benefits the classification of myxobacteria and improves determination of biosynthetic potential for prioritization of natural product discovery.

**Keywords:** myxobacteria; *Myxococcus* sp.; *Corallococcus* sp.; *Melittangium* sp.; *Archangium* sp.; biosynthetic gene clusters

#### **1. Introduction**

Over the last decade, 34 novel species of myxobacteria have been described including representatives from 10 newly described genera within the order *Myxococcales*(Table S1) [1–14]. Prevalent in soils and marine sediments, predatory and cellulolytic myxobacteria contribute to nutrient cycling within microbial food webs. Perhaps most-studied for their cooperative lifestyles, myxobacteria have been an excellent resource for investigations concerning developmental multicellularity and two-component signaling, swarming motilities and predatory features, and the discovery of biologically active metabolites [15–23]. Each of these areas of interest have benefited from the increased utility and accessibility of next-generation sequencing (NGS) technologies. The driving force behind the recent surge in efforts to discover novel species of myxobacteria can also be attributed to advances in sequencing technologies. Genome sequencing of myxobacteria has demonstrated that they possess large genomes replete with biosynthetic gene clusters, and myxobacteria have recently been deemed a "gifted" taxon for the production of specialized metabolites with drug-like properties [24–29]. These efforts, combined with a thorough metabolic survey of over 2000 strains within the order *Myxococcales*, concluded that the odds of novel metabolite discovery increase when exploring

**Citation:** Ahearne, A.; Albataineh, H.; Dowd, S.E.; Stevens, D.C. Assessment of Evolutionary Relationships for Prioritization of Myxobacteria for Natural Product Discovery. *Microorganisms* **2021**, *9*, 1376. https://doi.org/10.3390/ microorganisms9071376

Academic Editors: David Whitworth and Ulrich Stingl

Received: 31 March 2021 Accepted: 21 June 2021 Published: 24 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

novel genera of myxobacteria [30]. Motivated by these observations, we sought to isolate novel myxobacteria from lesser-studied North American soils.

Recently, comparative genomic analyses have been utilized to provide efficient preliminary classification of novel myxobacteria, and we considered that such an approach would expedite prioritization of strains for future metabolic studies [3,8,11,31–37]. While traditional myxobacterial classification efforts relied on morphology, biochemistry, and the conservation of 16S gene sequences, updated methods including genome-based taxonomy have provided excellent preliminary taxonomic classification of myxobacterial isolates [38–40]. Considering that genome sequencing would also afford the biosynthetic potential of any isolated myxobacteria, we sought to employ long-read sequencing to generate high-quality draft genomes hoping to avoid fragmented, partial biosynthetic pathways. For example, of the 11 currently sequenced myxobacteria from the genus *Corallococcus*, 68% of the 621 total putative biosynthetic gene clusters (BGCs) predicted by the analysis platform AntiSMASH are positioned on a contig edge and are potentially incomplete (Table S2). In fact, the only two *Corallococcus* genomes sequenced with long-read techniques (*Corallococcus coralloides* DSM 2259T and *C. coralloides* strain B035) each included 34 predicted BGCs with none located on a contig edge [41,42]. Ideally, larger contigs generated from long-read sequencing might benefit the comparative genomic analyses and provide a more complete assessment of biosynthetic potential.

In addition to four environmental isolates of putative myxobacteria included in this study, we acquired two previously characterized myxobacteria from the American Type Culture Collection (ATCC): *Archangium primigenium* ATCC 29,037 and *Chondrococcus macrosporus* ATCC 29039. Previously miscategorized as *Polyangium primigenium*, the original morphological descriptions for *A. primigenium* were remarkably apt for the strain acquired from the ATCC and cultivated in our lab, including obvious fruiting body formation and carotene-like pigmentation (Figure 1) [43,44]. The original description of *A. primigenium* fruiting bodies initially piqued our interest in the strain as members of the genus *Archangium* typically do not or very rarely form defined fruiting bodies when cultivated with standard laboratory conditions [45,46]. *Archangium* species have previously been referred to as "degenerate forms" of myxobacteria due to diminished fruiting bodies with no sporangioles or absent fruiting body formation [46]. Comparatively, little historical data is available for *C. macrosporus* ATCC 29039. The strain was deposited at the ATCC by distinguished taxonomist Professor V. B. D. Skerman and was subsequently included in a methodology study focused on isolating myxobacteria from soils [47–49]. The decision to change the genus *Chondrococcus* to instead be *Corallococcus* has been validated with many novel *Corallococcus* species being described afterwards [8,40,50]. However, we were curious to determine the status of *C. macrosporus* ATCC 29039. Considering the proposed reassignment of *Corallococcus macrosporus* DSM 14697T to the genus *Myxococcus*, it was unclear if *C. macrosporus* ATCC 29,039 should also be reassigned. Both characterized using traditional approaches that heavily relied on morphology, we sought to determine how genomic comparisons might impact the current taxonomic assignments of these available myxobacteria.

**Figure 1.** Myxobacterial fruiting bodies from strains NCSPR001, NCCRE002, SCHIC003, SCPEA002, and the strains *C. macrosporus* ATCC 29,039 and *A. primigenium* ATCC 29037.

#### **2. Materials and Methods**

#### *2.1. Bacterial Strains and Growth Conditions*

*A. primigenium* and *C. macrosporus* were procured from the ATCC as strain numbers ATCC 29037 and ATCC 29039, respectively. The remaining strains were isolated from soil as described later. All strains were cultured either on VY/2 or VY/4 agar plates (5 or 2.5 g/L baker's yeast, 1.5 g/L CaCl2·2H2O, 0.5 mg/L vitamin B12, 15 g/L agar, pH 7.2). Swarming and fruiting bodies on agar plates were observed under a Zeiss discovery V12 stereo microscope and photographed using a Zeiss axiocam105.

#### *2.2. Isolation of Environmental Myxobacteria*

Soil samples, collected in Asheville, NC and Tryon, SC, were taken from the base of trees and dried in open air before storage. Detailed location data are provided as Supplemental Information (Table S3). Myxobacteria were isolated using a slightly modified Coli-spot method [51]. A 1 mg/mL solution of cycloheximide/nystatin was used to wet the soil sample to a paste-like consistency before inoculation onto an *Escherichia coli* baited WAT agar plate (1 g/L CaCl2·2H2O, 15 g/L agar, 20 mM HEPES). To prepare the baiting plate, a lawn of *E. coli* was grown overnight on tryptone soya broth (TSB) with agar (1.5%), and the cells were scraped and suspended in 2 mL of sterile deionized water. Four hundred μL of the *E. coli* suspension was spread over the surface of a WAT agar plate to create a bait circle of approximately 2 inches in diameter and let dry. Once the *E. coli* was dried, a pea sized amount of soil paste was placed at the center of the bait circle. Plates were incubated at 25 ◦C for up to a month, and degradation of the *E. coli* was monitored over time. Visible degrading swarms were seen after a few days, and swarm edges or fruiting bodies were passaged onto VY/4 media for purification. Purification was accomplished by repeated swarm edge transfer.

#### *2.3. Genomic DNA Isolation, Sequencing, Assembly, and Annotation*

Genomic DNA for NGS was obtained from actively growing bacteria on VY/2 or VY/4 plates using NucleoBond high molecular weight DNA kit (Macherey-Nagel, Bethlehem, PA, USA). The quantity and quality of the extraction were checked by Nanodrop (Thermo Scientific NanoDrop One) and followed by Qubit quantification using Qubit®® dsDNA HS Assay Kit (ThermoFisher Scientific, Suwanee, GA, USA).

Sequencing for all samples was performed on a Pacific Biosciences single-molecule real-time (SMRT) sequencing platform at the MR DNA facility (Shallowater, TX, USA). The SMRTbell libraries for the sample were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences, Menlo Park, CA, USA) following the manufacturer's user guide. Following library preparation, the final concentration of each library was measured using the Qubit® dsDNA HS Assay Kit (ThermoFisher Scientific, Suwanee, GA, USA), and the average library sizes were determined using the Agilent 2100 Bioanalyzer

(Agilent Technologies, Santa Clara, CA, USA). Each library pool was then sequenced using the 10-h movie time on the PacBio Sequel (Pacific Biosciences, Menlo Park, CA, USA). De Novo Assembly of each genome was accomplished using the PacBio SMRT Analysis Hierarchical Genome Assembly Process (HGAP). Genome annotation was done using Rapid Annotation using Subsystem Technology (RAST) with further annotation requested by the NCBI Prokaryotic Genome Annotation Pipeline [52]. Sequencing data have been deposited in NCBI under the accession numbers JADWYI000000000.1, JAFIMU000000000, JAFIMS000000000, JAFIMT000000000, CP071090, and CP071091 for strains *A. primigenium*, *C. macrosporus*, NCSPR001, NCCRE002, SCPEA002, and SCHIC003, respectively.

#### *2.4. Comparative Genomic Studies*

The genome sequence data were uploaded to the Type (Strain) Genome Server (TYGS), a free bioinformatics platform available under https://tygs.dsmz.de (accessed 10 January 2021), for a whole genome-based taxonomic analysis. TYGS was used to calculate the dDDH values and construct minimum evolution trees using the Genome BLAST Distance Phylogeny approach (GBDP) [53,54]. GBDP trees were visualized using MEGA-X [55]. The average nucleotide identity (ANI) was calculated using the ANI/AAI-Matrix calculator [56,57].

#### *2.5. BIG-SCAPE Analysis*

Genome data for all myxobacteria belonging to the *Cystobacterineae* suborder were downloaded from the NCBI database. A list of all myxobacteria used in this analysis are listed in List S1. These genomes in addition to genomes of *A. primigenium*, *C. macrosporus*, and the environmental isolates were analyzed by the AntiSMASH platform (version 5 available at https://docs.antismash.secondarymetabolites.org; accessed 1 February 2021) to assess specialized metabolite gene clusters using the "relaxed" strictness setting [58,59]. A total of 1826 predicted BGCs (.gbk files) were then processed locally using the BiG-SCAPE program (version 20181005, available at https://git.wageningenur.nl/medema-group/BiG-SCAPE; accessed 1 February 2021), with the MiBIG database (version 2.0 available at https://mibig. secondarymetabolites.org; accessed 1 February 2021) as reference [60,61]. BiG-SCAPE analysis was supplemented with Pfam database version 33.1 [62]. The singleton parameter in BiG-SCAPE was selected to ensure that BGCs with distances lower than the default cutoff distance of 0.3 were included in the corresponding output data. The hybrids-off parameter was selected to prevent hybrid BGC redundancy. Generated network files separated by BiG-SCAPE class were combined for visualization using Cytoscape version 3.8.2 (http://www.cytoscape.org; accessed 1 February 2021) [63]. Annotations associated with each BGC were included in Cytoscape networks by importing curated tables generated by BiG-SCAPE.

#### **3. Results**

#### *3.1. Comparative Genomics and Taxonomic Assessment of Archangium Primigenium, Chondrococcus Macrosporus, and Environmental Isolates*

Genome sequencing provided high quality draft genomes for each of the six investigated myxobacteria, as indicated by the summary of general features in Table 1. The total genome sizes ranged from ~9.5–13 Mb, and the %GC content varied around ~69–71%. Of the six genomes, both environmental strains SCHIC003 and SCPEA002 were assembled on a single contig. Overall, the assemblies for each genome provided much lower total contig counts (1–17) than recently sequenced myxobacterial genomes [3,8]. Interestingly, a minimum evolution of phylogenetic trees generated from the whole genome sequence data clustered *A. primigenium* with *Melittangium boletus* DSM 14713T and not with the three currently sequenced strains from the genus *Archangium* (Figure 2, Figures S1 and S2). Accordingly, ANI and dDDH values supported the placement of *A. primigenium* in the genus *Melittangium* (Table 2) as a novel species with both values well below the established cutoffs for classification of distinct species (<95% ANI; <70% dDDH) [31,34,35,37,64]. These data suggest *A. primigenium* is currently misclassified as a member of the genus *Archangium* and should instead be placed in the genus *Melittangium*.

**Figure 2.** Minimum evolution tree from the whole genomes of different myxobacteria including the six strains under investigation in this study using the GBDP approach. The numbers in bold above branches are GBDP pseudo-bootstrap support values > 60% from 100 replications, with an average branch support of 100.0%. Branch pseudo-bootstraps less than 50% are not shown. The numbers below branches are branch lengths scaled in terms of GBDP distance formula d5. The tree was rooted at the midpoint.

**Table 1.** Genome properties and general features of myxobacteria under investigation in this study.

**Table 2.** 16S rRNA identity, ANI, and dDDH values for pairwise comparisons between *A. primigenium* with the most similar fully sequenced myxobacteria.

The calculated ANI and dDDH values for the sequenced *C. marcosporus* strain acquired from the ATCC support the original assignment to the genus *Chondrococcus*, now *Corallococcus* [31,50]. As opposed to the recently reclassified *Myxococcus macrosporus* DSM 14697T, previously *Corallococcus macrosporus*, the minimum evolution phylogenetic tree suggested *C. macrosporus* ATCC 29039 to be a member or the genus *Corallococcus* most similar to *Corallococcus exercitus* DSM 108849T (Figure 2, Figures S1 and S3) [50]. The isolated strains NCCRE002 and NCSPR001 were also determined to be members of the genus *Corallococcus* (Figure 2, Figures S1 and S3). Comparative genome analyses implied that strain NCCRE002 is an isolate of *Corallococcus exiguus* DSM 14696T. However, the ANI and GBDP trees suggested that strain NCSPR001 is a novel member of the genus *Corallococcus* most similar to *Corallococcus coralloides* DSM 2259T (Table 3).

The isolated SCHIC003 and SCPEA002 strains were initially determined to be members of the genus *Myxococcus*. However, inclusion of sequenced representatives from the genus *Pyxidicoccus* (considered to be synonymous with *Myxococcus*) [3] in our comparative analysis grouped strain SCPEA002 within the *Pyxidicoccus* clade (Figure 2, Figures S1 and S4). Most similar to *Pyxidicoccus caerfyrddinensis* CA032AT, dDDH and ANI analysis suggested the SCPEA002 strain to be a novel member of the genus *Pyxidicoccus* (Table 4). Similarly, comparative genome analysis determined that strain SCHIC003 is likely be a novel member of the genus *Myxococcus*, albeit highly similar to *Myxococcus stipitatus* DSM 14675T with ANI and dDDH values just below the cutoffs for species differentiation [31,37, 64] (Table 4 and Figure 2, Figures S1 and S3).

**Table 3.** Differentiation chart comparing *C. macrosporus* ATCC 29039, NCCRE002, and NCSPR001 draft genome data with sequenced members of the genus *Corallococcus*. The top half uses total genome comparison methods (ANI and dDDH) while the bottom half uses 16S rRNA sequence for pairwise comparison. Orange shading represents species that would be designated as the same using the designated method. Blue shading represents unique species using the designated method, <98.65% 16S identity%, or < 95%/70% for ANI/dDDH.

**Table 4.** Differentiation chart comparing SCPEA002 and SCHIC003 draft genome data with sequenced members of the genera *Myxococcus* and *Pyxidicoccus*. The top half uses total genome comparison methods (ANI and dDDH) while the bottom half uses 16S rRNA sequence for pairwise comparison. Orange shading represents species that would be designated as the same using the designated method. Blue shading represents unique species using the designated method, <98.65% 16S identity%, or < 95%/70% for ANI/dDDH.

#### *3.2. Biosynthetic Potential and Genus Level Correlations*

Analysis of our draft genomes using the biosynthetic pathway prediction platform AntiSMASH revealed a range of 29–42 total predicted BGCs with *C. macrosporus* including the highest total of BGCs. However, the draft genome for *C. macrosporus* also included the highest total of four partial BGCs positioned on the edges of contigs. No BGCs occurring on contig edges were observed from *A. primigenium*, NCSPR001, or SCPEA002. All of the sequenced strains included highly similar (≥75% similarity score) biosynthetic pathways for the signaling terpene geosmin [65,66], the signaling lipids VEPE/AEPE/TG-1 [67,68], and carotenoids [69–72] (Figure 3). Excluding SCHIC003, each genome included a BGC highly homologous to the pathway associated with the myxobacterial siderophore myxochelin [73,74]. Pathways somewhat similar (similarity scores of 66%) to the myxoprincomidec506 BGC were observed in every genome except the *A. primigenium* genome [75]. Clusters with ≥75% similarity to pathways from *M. stipitatus* DSM 14675T associated with the metabolites rhizopodin [76,77] and phenalamide A2 [78] were observed in the SCHIC003 draft genome as well as clusters also present in the *M. stipitatus* DSM 14675T genome deposited in the AntiSMASH database [79], including the dkxanthene [80], fulvuthiacene [81], and violacein [82–84] BGCs (Figure 4). Considering previously characterized BGCs from each genus associated with the six investigated myxobacteria, the corallopyronin BGC from *C. coralloides* B035 [85,86] was absent from all three of the putative *Corallococcus* strains, the melithiazol BGC from *Melittangium lichenicola* Me I46 [87] was not present in *A. primigenium*, and neither the disciformycin/gulmirecin BGC [88,89] or the pyxidicycline BGC [90] from *Pyxidicoccus fallax* were present in SCPEA002.

**Figure 3.** Common specialized metabolites from myxobacteria associated with characterized BGCs present in the six investigated strains of myxobacteria. \* Myxochelin BGC not present in SCHIC003 genome data. # Myxoprincomide BGC not present in *A. primigenium* genome data.

**Figure 4.** (**A**) Specialized metabolites produced by members of the genera *Corallococcus, Melittangium,* and *Pyxidicoccus* with no associated BGCs observed in any of the six investigated myxobacterial strains. (**B**) Comparisons of the rhizopodin, phenalamide A2, and fulvuthiacene BGCs from SCHIC003 genome data and the characterized pathways from *M. stipitatus* and *M. fulvus.* All SCHIC003 gene products, excluding RizC, had coverages ≥99% with the indicated homolog. \* RizC located on a contig edge and is incomplete in SCHIC003 genome data.

> Utilizing the BiG-SCAPE platform to render BGC sequence similarity networks, we sought to determine the extent of homology between BGCs from our six sequenced myxobacteria and BGCs from all currently sequenced members of the suborder *Cystobacterineae* [91]. The resulting sequence similarity network included 1080 BGCs connected by 3046 edges (not including self-looped nodes/singletons) and depicted genus-level homologies across all BGCs from the newly sequenced myxobacteria corroborating our suggested taxonomic assignments (Figure 5 and Table 5). For example, BGCs from the three newly sequenced samples *C. macrosporus*, NCSPR001, and NCCRE002 were almost exclusively clustered with BGCs from members of the genus *Corallococcus*, and BGCs from SCHIC003 and SCPEA002 samples clustered with the genera *Myxococcus* and *Pyxidicoccus* (Figure 5). However, SCPEA002 BGCs do not cluster as frequently with *Pyxidicoccus* BGCs as they do *Myxococcus* BGCs, and the majority (76.5%) were not clustered with any BGC within the network (Table 5). This is likely due to the highly fragmented nature of available *Pyxidicoccus* genomes resulting in many incomplete or partial BGCs. Therefore, few *Pyxidicoccus* pathways appear in the similarity network, and the percentage of unique pathways associated with SCPEA002 is likely overestimated. Regardless, the limited number of SCPEA002 BGCs clustered with BGCs from *Myxococcus/Pyxidicoccus* genomes indicates a potential to discover novel metabolites despite placement in the highly scrutinized clade. The only clustered groups with numerous edges formed between BGCs from the genera

*Myxococcus* and *Corallococcus* included characterized biosynthetic pathways for ubiquitous signaling lipids VEPE/AEPE/TG-1, carotenoids, and the siderophore myxochelin as well as two uncharacterized BGCs predicted to produce ribosomally synthesized and post-translationally modified peptides (RiPPs).

**Figure 5.** BiG-SCAPE BGC sequence similarity networks (c = 0.3) as visualized with Cytoscape. The network is generated from *A. primigenium*, *C. macrosporus*, NCCRE002, NCSPR001, SCHIC003, SCPEA002, and all myxobacteria belonging to the Cystobacterineae suborder with genomes deposited in NCBI. Each node represents one BGC identified by AntiSMASH 5.0, where the colors and shapes of the nodes represent different genera and AntiSMASH-predicted classes, respectively. Nodes representing BGCs from newly sequenced myxobacteria included in this study are enlarged. BGCs included as singletons in the original BiG-SCAPE analysis removed.

**Table 5.** Overview of BiG-SCAPE BGC sequence similarity networks of the six strains under investigation in this study.

\* genomes with fragmented biosynthetic pathways, likely resulting in fewer clustered pathways than truly exist.

Interestingly, a total of 23 *A. primigenium* BGCs (out of 32 BGCs) appear as singletons in the network with no homology to any of the included BGCs from *Cystobacterineae*. In fact, aside from the VEPE/AEPE/TG-1 cluster and a terpene cluster that included members of the genera *Archangium* and *Cystobacter*, all remaining BGCs from *A. primigenium* had connecting edges to BGCs from *Melittangium boletus* DSM 14713T. Out of 21 edges formed by *A. primigenium* in the network, four edges were formed with four species of *Corallococcus* (a total of 11 *Corallococcus* species in the network), four edges were formed with all species of *Cystobacter* (three species in the network), six edges were formed with all species of *Archangium* (three species in the network), and seven edges were formed with the only *Melittangium* species in the network, *M. boletus* DSM 14713T. Overall, these data corroborate our preliminary taxonomic assignments and suggest that the prioritization of *A. primigenium* for subsequent discovery efforts is most likely to yield novel metabolites.

#### **4. Discussion**

As novel myxobacteria continue to be isolated and explored for natural product discovery, efficient approaches for approximate taxonomic placement will assist the prioritization of lesser studied genera. Utilizing long-read genome sequencing and comparative genomic analyses, we determine preliminary taxonomic placement for four myxobacteria isolated from North American soils and two myxobacteria deposited at the ATCC. This approach indicated that previously classified *A. primigenium* ATCC 29037 is instead a novel member of the genus *Melittangium*, and that three of our four environmental isolates included potentially novel members of the genera *Corallococcus, Myxococcus,* and *Pyxidicoccus*. Previously classified *Chondorococcus macrosporus* ATCC 29039 was also determined to be a potentially novel member of the genus *Corallococcus*, with high similarity to *C. exercitus* DSM 108849T and phylogenetically distinct from *M. macrosporus* DSM 14697T previously assigned to the genus *Corallococcus*. Subsequent bioinformatic analysis of biosynthetic pathways included in the newly sequenced genomes corroborated our preliminary taxonomic placements for each sample. Ultimately, this process identified *A. primigenium* to be a member of the lesser studied genus *Melittangium* and indicated that it should be prioritized for continued natural product discovery efforts. Of the environmental isolates, BGCs from SCPEA002 were determined to include the least amount of overlap with BGCs from other *Myxococcus/Pyxidicoccus* species. While environmental isolates SCHIC002 and NCSPR001 were also identified as novel members of the genera *Myxococcus* and *Corallococcus*, respectively, the apparent overlap in BGCs from thoroughly explored myxobacteria determined from sequence similarity network analysis suggests a limited potential for discovery of novel specialized metabolites. Overall, comparative genomic techniques including the assessment of biosynthetic potential enabled a phylogenetic approximation and suggested prioritization of *A. primigenium* for natural product discovery efforts from a sample set of six newly sequenced myxobacteria.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/microorganisms9071376/s1. Supplemental Table S1: Novel myxobacteria described in

literature spanning 2011-2021; Table S2: Comparison of biosynthetic gene clusters (BGCs) located on contig edges from previously sequenced members of the genus *Corallococcus*, Table S3: Locations of soil samples used for environmental strains isolation, List S1: A list of all myxobacteria and their accession numbers used in BiG-SCAPE; Figure S1: Minimum evolution tree from the 16S rRNA of the 6 strains under investigation in this study and all myxobacteria Type strains deposited in DSMZ; Figure S2: Minimum evolution tree from the whole genomes of *A. primigenium* and different members of the family *Archangiaceae* using the GBDP approach; Figure S3: Minimum evolution tree from the whole genomes of *C. macrosporus* ATCC 29039, NCCRE002, NCSPR001 and different members of genus *Corallococcus* using the GBDP approach; Figure S4: Minimum evolution tree from the whole genomes of SCHIC003, SCPEA002, and different members of *Myxococcus* and *Pyxidicoccus* using the GBDP approach.

**Author Contributions:** Conceptualization, supervision, and administration D.C.S.; formal analysis and data curation A.A., H.A., S.E.D., and D.C.S.; methodology, validation, and writing A.A., H.A., and D.C.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by funds from the National Institute of Allergy and Infectious Diseases (1 R15 AI137996-01A1) and the National Institute of General Medical Sciences (1 P20 GM130460-01A1).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not Applicable.

**Data Availability Statement:** Sequencing data have been deposited in NCBI under the accession numbers JADWYI000000000.1, JAFIMU000000000, JAFIMS000000000, JAFIMT000000000, CP071090, and CP071091 for strains *A. primigenium*, *C. macrosporus*, NCSPR001, NCCRE002, SCPEA002, and SCHIC003, respectively.

**Acknowledgments:** The authors would like to acknowledge the University of Mississippi School of Pharmacy for startup support.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*4.2. Cell Culture*

A549 lung epithelial cells (CCL-185, ATCC, Manassas, VA, USA) were cultured in Ham's F-12K medium (Thermo Fisher) with 10% FBS as recommended by the ATCC. Cells were grown in a humidified incubator with 5% CO2 at 37 ◦C. Cells were cultured in 75-cm2 cell culture flasks and subcultured when they reached ~80% confluence at a 1:5 dilution. Cells were lifted using TrypLE Express (Thermo Fisher, Waltham, MA, USA) at 37 ◦C. Cells were maintained at a maximum of 10 passages for the experiments in this work.


**1. Introduction**

Unmanned surface vehicles (USVs) are a kind of specific ships with the ability of autonomous mission execution, which are widely used in various applications, including marine resource exploration, water resource transportation, patrol and defense in key areas and river regulation [1,2]. Progress has been made in a large number of research areas, including environmental perception [3,4], formation control [5,6], navigation [7,8], and so on. Environmental perception and trajectory generation are the two most important techniques when the USVs are executing in unknown environments. In particular, when the environment contains dynamic obstacles, USVs struggle to achieve accurate trajectory planning and tracking due to the lack of effective obstacle information. As a result, the autonomous navigation system may fail.

During the navigation process of a USV, the sensing devices, such as radar or camera, are located at a low observation point, which is detrimental to environmental perception because the adjacent obstacles in the front and behind will block each other. Moreover, the input of the sensors often contains noise caused by hull shaking on the water. This makes precise environmental perception a difficult problem for USVs and affects the success rate of trajectory generation. Usually, simultaneous localization and mapping (SLAM) [9] technology is required to construct the global map. However, this kind of method requires a huge computational load, and it is intractable to deal with dynamic objects in the water environment.

**Citation:** Huang, T.; Chen, Z.; Gao, W.; Xue, Z.; Liu, Y. A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints. *Sensors* **2023**, *23*, 1845. https://doi.org/10.3390/s23041845

Academic Editor: Sašo Blažiˇc

Received: 10 November 2022 Revised: 19 January 2023 Accepted: 2 February 2023 Published: 7 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

A feasible solution is to design a USV-UAV cooperative system to tackle the above problems, where the unmanned aerial vehicle (UAV) plays the role as a flying sensor. As shown in Figure 1, the USV has long cruise capability, but its perception is disturbed and limited by the circumstance. Hence the UAV flies over the USV, providing more stable and comprehensive information. Semantic segmentation [10,11] and 3D projection are used in this paper to transfer obstacle information in the field of vision of the UAV to the coordinate system of the USV. Semantic segmentation extracts pixel information of environmental obstacles, and a camera projection model helps to transfer the pixel information to 3D information. By doing this, global map information around the USV can be obtained efficiently and in real-time, implying the USV-UAV cooperative system can improve the perception ability of the USV effectively, allowing the USV to perform tasks in more complex water circumstances.

**Figure 1.** An illustration of the USV-UAV cooperative system, where the UAV provides wide obstacles and map information to guide the USV to generate an obstacle avoidance trajectory.

An initial obstacle avoidance trajectory is firstly generated by a graph-based search method [12]. However, such a method was originally designed for path searching on vast geographical scenarios, which does not consider the USV's dynamic characteristics. On the other hand, USV is famous for its under-actuated motion characteristics [13], which makes it hard to be controlled well, even when an optimal trajectory is planned. In this paper, we design a numerical optimization method to optimize the trajectory. Specifically, we take the hull dynamic constraints into account when modeling the optimization problem. As a result, the generated trajectory not only allows the obstacle avoidance rule, but also fits the motion characteristics of a USV. This makes the generated trajectory easier to be tracked under the same control conditions.

Finally, a control method with the lowest energy consumption per execution task is designed under a new numerical optimization problem. It ensures that the power consumption is optimal when the USV is actuated to track the given optimal trajectory, which is a very useful technique in real-world applications. The performance of the trajectory generation and tracking is comprehensively compared and analyzed in the simulated environments, and it verifies the effectiveness of our proposed novel framework.

In summary, the contributions of this paper are listed as follows.

• The lowest energy consumption control law is proposed to track the generated trajectory efficiently and accurately, and extensive experiments are conducted to verify the effectiveness of the USV-UAV cooperative system.

#### **2. Related Works**

#### *2.1. Trajectory Planning for USV*

Trajectory planning aims to automatically generate an obstacle avoidance trajectory for a USV when the local or global map is given. Among existing methods, the mainstream trajectory planning methods are mainly divided into two categories, i.e., path search and trajectory generation.

There are two research directions for the path search methods, including graph search and random sampling. Typical graph search methods include the A\* [14] and Dijkstra [15] algorithm, as well as their derivatives [16]. These methods mainly discretize the known map into interconnected grids and find the shortest path according to the heuristic parameters. The disadvantage of this kind of method is that the search dimension in the large map is expanding, and the calculation time shows a rapid upward trend. Among random sampling methods, typical varieties include RRT [17] and its derivatives [18], which dynamically find feasible paths by randomly sampling points in the map and constructing random exploratory trees. The method can show better performance for large maps, but its shortcomings are also very obvious. It is easy to be guided to a locally optimal solution, and it is difficult to generate feasible paths in narrow areas when system's computing resources are limited. The common problem of the above methods is that the generated path curvature is discontinuous, and trajectory smoothing is needed afterward.

For the trajectory generation methods, curve interpolation methods, such as B-spline [19], are commonly used to smooth the trajectory. The smoothness of the trajectory and motion state is guaranteed by the continuity theorem of higher-order derivatives of a curve. Meanwhile, numerical optimization methods are also widely used, such as minimum snap [20] and near-optimal control [21].

Some methods can also combine path search with trajectory generation, such as domain reduction-based RRT\* [22] and Hybrid A\* [23]. In this paper, the proposed method belongs to the numerical optimization method . It adds the dynamic and kinematic constraints of unmanned craft in the trajectory generation part so that the generated trajectory is more in line with the dynamic characteristics of the hull.

#### *2.2. The USV-UAV Cooperative System*

With the rapid development of automation and artificial intelligence technology, unmanned aerial vehicle (UAV) technology has made significant progress in recent years. Compared with USV, the advantage of UAV is that it has a broader field of vision and faster movement speed and can provide more comprehensive and effective data information for USV. In addition, UAV has the advantages of flying height and that its communication ability is less affected by the environment. It can be used to provide communication relay services for multiple USVs located in different positions. Due to the strong complementarity between USV and UAV in perception, communication, operation time, and other aspects, researchers have focused on the coordination of having UAV serve USV and have successfully verified that this method can effectively solve the problem mentioned above of self-awareness of a USV. Ref. [24] focused on the search and rescue of USVs in flood scenes and proposed a collaborative mode of manipulating a UAV to establish the global map first, providing complete map information and target localization for subsequent USV planning. Ref. [25] proposed a cooperative formation control algorithm for a single USV and multiple UAVs. The method is based on the leader-follower distributed consensus model, and the position and orientation of each boat are determined by the RGB image color-space features acquired by the UAV camera. Ref. [26] considered the strong search capability of the UAV in the air, combined with the actual target strike capability of the USV, and proposed a two-stage cooperative path planning algorithm on the water and underwater based on

the particle swarm optimization algorithm. Ref. [27] proposed an effective game incentive mechanism for the task assignment problem in the cooperative operation of USVs and UAVs, which reduced the task cost and improved the task efficiency. Ref. [28] proposed that the LVS-LVA framework to be applied the cooperative motion control of USV-UAV.

Although, most of these methods are cooperative ways to provide UAV environmental data and perceptual information for the navigation task of a USV. With the development of computer vision technology, the accuracy and robustness of the perception algorithm they use need to be improved. In addition, they did not consider the trajectory of the USV and its tracking control link, and the proposed collaborative framework can not be fully applied to the autonomous navigation task of USVs.

#### **3. Cooperative Trajectory Generation**

In the USV-UAV cooperative system, the USV has a stable environmental self-supporting ability, and the UAV is flexible and environmentally adaptable. In the process of autonomous navigation of the USV, relying on the wide field of vision and strong environmental perception provided by the UAV, it can generate a more reasonable trajectory and skillfully avoid various kinds of obstacles.

## *3.1. Environmental Perception and 3D Projection*

Environmental perception is vital when the USV is performing in unknown water areas. Different observation angles have a significant influence on the observed results. As shown in Figure 2, the USV and UAV have different angles of view. The USV observes the environment from a horizontal perspective, which may lead to serious visual occlusion, whereas the UAV performs environmental perception from a top-down perspective, which enables more accurate map-view information.

(a) USV angle of view (b) UAV angle of view

**Figure 2.** Perspective difference between USV and UAV.

Concerning the accuracy of obstacle recognition and the calculation efficiency, we use semantic segmentation technology [29,30] based on deep learning to extract pixel-level obstacle information from the image data obtained by the UAV's camera. For a given image, the position, shape and size of the obstacles in the environment can be judged by assigning each pixel with a two-categorical label: '0' indicates a safety area and '1' denotes an area in which the obstacles are located.

In this paper, we use DeepLab [10] as the semantic segmentation network and replace the backbone with MobileNet [31]. On the one hand, it reduces the amount of computation. On the other hand, in the process of feature extraction, with the help of the atrous spatial pyramid pooling (ASPP) module, it can effectively improve the global receptive field and the recognition effect. The overall network architecture is illustrated in Figure 3.

**Figure 3.** The network architecture of the semantic segmentation algorithm deployed on the UAV.

After obtaining the pixel coordinates of obstacles in the image, it needs to convert the obstacle coordinate information into a unified global coordinate. We define the coordinate system of the UAV as *U*, the camera coordinate system as *C*, and the global coordinate system as *G*. Thus the transformation from *U* to *C* can be represented by *TUC* = [*R*|*T*] ∈ *R*4×4, where *R* is the rotation matrix and *T* is the translation matrix. *TGU* · *TUC* denotes the transformation matrix from *G* to *C*. Assuming that the coordinates of the obstacle point *m* in the pixel coordinate system are (*u*, *v*), according to the imaging principle of the pinhole camera model, the relationship between its position in the camera coordinate system can be expressed as

$$\begin{cases} u = f\_x \cdot \frac{\chi}{z} + c\_x \\ v = f\_y \cdot \frac{\chi}{z} + c\_{y\prime} \end{cases} \tag{1}$$

where *fx* and *fy* denote the focal length in the *x* and *y* direction and *cx* and *cy* are the positions of the origin of the image plane, which can usually be regarded as the center of the image. Thus, the relationship between the 3D points in the global coordinate system *M* = (*x*, *y*, *z*) and the pixel coordinate system *m* = (*u*, *v*) is denoted by

$$\mathbf{s} \cdot \begin{bmatrix} u \\ v \\ 1 \\ 1 \end{bmatrix} = \begin{bmatrix} f\_x & 0 & c\_x \\ 0 & f\_y & c\_y \\ 0 & 0 & 1 \\ 0 & 0 & s \end{bmatrix} \cdot T\_{GII} \cdot T\_{UIC} \cdot \begin{bmatrix} x \\ y \\ z \\ 1 \end{bmatrix} \tag{2}$$

where *s* is the scaling factor, which can be regarded as the depth information of each pixel. In this paper, a binocular camera carried by the UAV is used to obtain the pixel depth *s*. Through this way of 3D coordinate projection, the pixel information sensed by the UAV in real-time can be projected into the global coordinate system, forming the 3D perception ability of the USV to the environment.

## *3.2. Initial Trajectory Generation*

In order to generate an obstacle avoidance trajectory, this paper applies the Hybrid A\* algorithm [23] to provide an initial path, as shown in Algorithm 1. Given the initial state of the USV (*s* = (*x*0, *y*0, *ϕ*0)) and the navigation target state (*e* = (*xf* , *yf* , *ϕf*)), the algorithm first puts the initial state into the open list. Then it iteratively reads the node with the lowest cost in the open list as the current parent node, and generates the next child node according to the current node state, system motion mode and obstacle map. Unlike the A\* algorithm, the Hybrid A\* algorithm adds the orientation dimension to the coordinate system. Therefore, the criteria for reaching the target state is that the distance between the coordinates of the node and the target point is less than the threshold of the reaching distance, and the collision-free Reeds–Shepp curve can be generated through the node state and the target point state.

**Algorithm 1** Trajectory Search with Hybrid A\*

#### **4. Trajectory Optimization and Tracking**

The USV is an under-actuated robot operation system where the number of control variables of the system is less than the degrees of freedom of the system. In the trajectory optimization process, if the dynamic constraints of this under-actuated characteristic are added to the optimization process, an optimal trajectory more in line with the characteristics of ship motion can be generated.

#### *4.1. Trajectory Optimization with Dynamics*

The motion model of the USV is a mathematical model with 6 degrees of freedom when it is complete. For simplicity, we can ignore the motion of the hull in the heave, roll and pitch directions, and simplify it into a 3-degrees of freedom with surge, sway and yaw, represented by *x*, *y* and *ϕ*. The mathematical expression of the hull dynamics can be expressed as

$$\begin{cases} \dot{\eta} = \mathcal{J}(\eta)\nu \\ M\dot{\nu} = \pi - \mathcal{C}(\nu)\nu - D\nu \end{cases} \tag{3}$$

where *η* = (*x*, *y*, *ϕ*) ∈ *R*3×1 denotes the state variables, and *ν* = (*u*, *v*,*r*) ∈ *R*3×1 denotes the speed variables. *J* ∈ *R*3×3 is the transition matrix, and *C* ∈ *R*3×3 is the Coriolis centripetal force matrix. *M* ∈ *R*3×3 is the inertial matrix, and *D* ∈ *R*3×3is the damping matrix. *τ* = (*τu*, 0, *τr*) ∈ *R*3×1 is the thrust matrix. For a catamaran, the thrust matrix can be expressed as

$$\begin{cases} \tau\_{\rm tr} = T\_1 + T\_2\\ \tau\_{\rm l} = \left(T\_1 - T\_2\right) \cdot B\_{\prime} \end{cases} \tag{4}$$

where *T*1 and *T*2 are the thrusts of two propellers, and *B* is their distance. The USV can be viewed as a linear time-invariant (LTI) system. Its state variables *X* and control variable *τ* can be represented by

$$\begin{cases} \mathbf{X} = [\mathbf{x}, \mathbf{y}, \mathbf{q}, \mathbf{u}, \mathbf{v}, \mathbf{r}]^T\\ \mathbf{r} = [\mathbf{r}\_{\mathbf{u}}, \mathbf{0}, \mathbf{r}\_{\mathbf{r}}]^T. \end{cases} \tag{5}$$

The system dynamics are as follows

$$\begin{cases} \begin{aligned} \dot{x} &= \iota c \cos(\varrho) - v \sin(\varrho) \\ \dot{y} &= \iota s \sin(\varrho) + v \cos(\varrho) \\ \dot{\varrho} &= r \end{aligned} \\ \begin{cases} m\_{11}\dot{u} - m\_{22}\iota r + d\_{11}u = \tau\_u \\ m\_{22}\dot{v} - m\_{11}\iota r + d\_{22}v = 0 \\ m\_{33}\dot{r} + (m\_{22} - m\_{11})\iota \upsilon + d\_{33}r = \tau\_r \end{aligned} \end{cases} \tag{6}$$

Based on Hybrid A\*, the global trajectory is optimized twice with the following constraints, including position, velocity, angular velocity and control input, as well as waypoint state constraints. The reference waypoint state is the sub-optimal trajectory obtained by considering the vehicle model, which can only provide the simulated optimal information of obstacle avoidance, heading speed and other controls. In this paper, we consider the state vector error in the optimization objective function as a soft constraint. The final optimization objective can be represented as

$$\min \quad \frac{1}{2} \left\{ \sum\_{i=0}^{N} [(\mathbf{X}\_{i} - \mathbf{X}\_{i}^{\rm ref})^{T} \mathbf{W}\_{\mathbf{x}} (\mathbf{X}\_{i} - \mathbf{X}\_{i}^{\rm ref}) + \mathbf{\tau}\_{i}^{T} \mathbf{W}\_{\mathbf{\tau}} \mathbf{\tau}\_{i} \right] + \sum\_{i=1}^{N} (\mathbf{\tau}\_{i} - \mathbf{\tau}\_{i-1})^{T} \mathbf{W}\_{\mathbf{\boldsymbol{z}}} (\mathbf{\tau}\_{i} - \mathbf{\tau}\_{i-1}) \right\}, \tag{7}$$

where *Xref i* denotes the reference state variables generated by Hybrid A\*, and *Wx* = *diag*{50, 50, 20, 15, 15, 15}, *Wτ* = *diag*{5, 0, 5} and *Wu* = *diag*{3, 0, 3} represent the positive definite, cost and weight matrices, respectively. Moreover, to ensure adequate accuracy in the trajectory, we choose 0.05 s as the sampling period.

We adopt the methods of minimizing the control quantity and minimizing the continuous control difference to ensure that the global trajectory generated by optimization can take into account the trajectory index factors, such as the smoothing of the control quantity and the minimization of the energy consumption at the same time. The overall algorithm flow is shown in Algorithm 2.

#### *4.2. Tracking Control with NMPC*

Nonlinear model predictive control (NMPC) [32] is famous for its ability to improve local tracking precision. It performs periodic real-time optimization according to the prediction time window to achieve the purpose of iterative control to reduce tracking error. Through the numerical optimization algorithm proposed above, the global trajectory based on the kinematic and dynamic constraints of the USV can be obtained, in which the reference control quantity can be obtained. Therefore, the trajectory optimization uses the error index of control quantity as the optimization target. Setting the current time as *tj* and the prediction time window as *Wn*, the optimization problem in terms of NMPC can be formulated as

$$\begin{split} \min \quad & \frac{1}{2} \sum\_{i=t\_{\bar{i}}}^{t\_{\bar{f}} + \mathsf{W}\_{n}} [(\mathbf{X}\_{i} - \mathbf{X}\_{i}^{\mathrm{ref}})^{\mathrm{T}} \mathbf{W}\_{\mathsf{mpc}} (\mathbf{X}\_{i} - \mathbf{X}\_{i}^{\mathrm{ref}}) + (\boldsymbol{\pi}\_{i} - \boldsymbol{\pi}\_{i}^{\mathrm{ref}})^{\mathrm{T}} \mathbf{W}\_{\mathsf{mpc}\mathsf{r}} (\boldsymbol{\pi}\_{i} - \boldsymbol{\pi}\_{i}^{\mathrm{ref}}) \\ & \qquad \qquad + (\boldsymbol{\pi}\_{i} - \boldsymbol{\pi}\_{i-1})^{\mathrm{T}} \mathbf{W}\_{\mathsf{mpc}\mathsf{r}} (\boldsymbol{\pi}\_{i} - \boldsymbol{\pi}\_{i-1}) \big], \end{split} \tag{8}$$

where the first term represents the error between the state variable and the reference state variable, which is mainly used to improve the accuracy of state tracking and maintenance in the process of real-time control. The second term represents the error between the control variable and the reference control variable. This term is used to meet the index of the lowest energy consumption. Although this problem has been considered in detail in the context of optimization objectives in global trajectory planning, secondary planning in local tracking control can achieve better results. The third term can improve the smoothness of input variables in actual control and meet the needs of practical application control. *Wmpcx* = *diag*{10, 10, 4, 2, 2, 2}, *Wmpcτ* = *diag*{2, 0, 2} and *Wmpcu* = *diag*{4, 0, 4} represent the positive definite, cost and weight matrices, respectively. And considering the control requirements of real-time operation and stability, we choose *Wn* to be 30, 0.05 s as the sampling period, and the cycle of the NMPC algorithm call to be 0.1 s.

#### **5. Experimental Analysis**

In this section, we perform simulation experiments using the open source Otter USV simulator [33] within the ROS environment. The Otter USV simulator is a catamaran 2.0-m long, 1.08-m wide and 1.06-m high. When fully assembled, it weighs 65 kg, and has the ability to be disassembled into parts weighing less than 20 kg, such that a single operator can launch the Otter from a jetty, lake, beach or riverbank. A PX4 drone autopilot is used as the UAV, which is mounted with a monocular camera. The Otter USV is traveling within a 200 × 100 square meter area, with many blocks placed therein as obstacles. We set up several different obstacle terrains to test the crossing ability of the USV-UAV cooperative system.

#### *5.1. Obstacle Recognition Ability*

Firstly, we perform experiments on the ability of obstacle recognition by the USV monocular camera. The semantic segmentation algorithm is used to recognize objects. Several terrains are randomly placed in the virtual environment. Some of the segmentation results are shown in Figure 4, from which we can see that the proposed light-weight segmentation network can successfully identify obstacles in the environment. Although there are some empty areas in the middle or on the edges of the obstacles, the basic shape of the obstacles has been preserved. In the post-processing stage, image expansion can be used to increase the safe collision avoidance area and ensure the reliability of navigation. After that, 3D projection can be performed to convert the pixel information into 3D information in a global coordinate system.

**Figure 4.** Obstacle recognition results of different terrains.


**9. References**

300 Wireless Communications and Networks – Recent Advances

0 2 4 6 8 10 12 14 16 18

Average received SNR [dB]

Fig. 1.13 Comparison of adaptation policies over MRC diversity reception in the presence of

The nested infinite sums in (1.38) and (1.39), as can be seen from Table 1, for dual and triple

in this Table 1, the number of the terms need to be summed to achieve a desired accuracy, depends strongly on these parameters and it increases as these parameter values increase.

> = 5 dB

> = 5 dB

Table 1. Number of terms that need to be summed in (1.38) and (1.39) to achieve accuracy at

Cases when wirelless channels are affected by general and nonlinear fading distributions are disscused in this chapter. The analytical study of the *κ*-*μ* fading channel capacity, e.g., under the OPRA, ORA, CIFR and TIFR adaptation policies and MRC and SC diversity techniques is performed. The main contribution are closed-form expressions derived for the proposed adaptation policies and diversity techniques. Based on them, numerically obtained results are graphically presented in order to show the effects of various system parameters. Since *κμ* model as general physical fading model includes Rayleigh, Rician, and Nakagami-*m*  fading models, as special cases, the generality and applicability of this analysis are more than obvious. Nonlinear fading scenario is discussed in the similar manner, as an analytical

κ = 1 μ = 1 8 9 10 κ = 2 μ = 2 15 15 16

κ = 1 μ = 1 19 21 24 κ = 2 μ = 2 23 26 28

 OPRA adaptation policy ORA adaptation policy CIFR adaptation policy TIFR adaptation policy

> = 10 dB

> = 10 dB

= 15 dB

= 15 dB

. As it is shown

0

Expression (1.38) 6th significant digit

Expression (1.39) 6th significant digit

Weibull fading.

**7. Conclusion**

1

2

3

MRC reception L=2

branch diversity case, converge for any value of the parameters *κ*, *μ* and

the specified significant digit for some values of system parameters.

= 2.5

4

Capacity per unit bandwith <C>/B [Bits/Sec/Hz]

5

6

7


*2.4.2 Material use reduction*

The maintenance operation must seek for minimizing the need for raw materials. This can be achieved without compromising the quality of maintenance if certain measures are taken collaboratively. Some of these measured are explained in the following.

• Using recycled materials. Using recycled, reused, and renewable materials reduces the demand for raw resources and contributes to preserving them for future generations. Maintenance operations that involve reconstruction of segments have high potentials to reuse certain elements of the existing part that is demolished. This requires an assessment of the opportunities to reuse and planning it before the demolition starts. Additionally, repair policies instead of replacement, particularly for equipment pieces, can significantly contribute to saving the embodied energy that has been used to manufacture, ship, and install the existing equipment. However, in all cases, the quality and performance criteria must not be compromised.