# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 5 Swarm Behaviours
### Information Consensus  
There are many decisions aMussels will have to make during their operation, such as identifying sensor failures, dividing into subgroups, and managing their energy balance (deciding when to float up for charging). Since there is a large number of agents making joint decisions, one algorithm type that has been considered for their resolution is the well-known consensus algorithms.  
Consensus algorithms in the control of multi-agent systems are particularly compelling for their simplicity and a wide range of applications. The foundation of consensus protocols in multi-agent systems lies in the field of distributed computing. In networks of agents, _consensus_ means to \"reach an agreement regarding a certain quantity of interest that depends on the state of all agents\" [27].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Simulation**} & \multicolumn{3}{c}{**Real Experiments**} \\ \cline{2-7}  & **no CA** & **CA** & \multicolumn{3}{c}{**modified CA**} & \\ \cline{2-7}  & \multicolumn{3}{c}{**3 Vehicles**} & \multicolumn{3}{c}{**4 Vehicles**} \\ \hline Shape & Triangle & Triangle & Triangle & Triangle & Triangle & Line & Triangle & Square \\ Rotation amount & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 90\({}^{\circ}\) & 90\({}^{\circ}\) \\ Stabilisation time (s) & 10.6 & 32.7 & 16.6 & 12.2 & 23.8 & 11.4 & 8.5 \\ Normalised stabilisation time (s/m) & 2.8 & 8.6 & 4.2 & 3.1 & 2.8 & 2.8 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of stabilisation times for conducted simulation and real experiments.  
Figure 25: Experimental results of rotating a four vehicle formation around its centre by 90\({}^{\circ}\).  
A consensus algorithm (protocol) is a series of rules that define information exchange between an agent and all of its neighbours in the network.  
Recently, there has been a surge of interest among scientists from different fields in problems related to consensus in networked multi-agent systems. Some of the most interesting problems include the collective behaviour of flocks and swarms [28], formation control for MRS [29], optimisation-based cooperative control [30], sensor fusion [31], synchronisation of coupled oscillators, asynchronous distributed algorithms, and many more.  
However, there have not been many advancements in the area of underwater consensus protocols. To the best of the authors' knowledge, the only underwater consensus applications include formation control of tethered and untethered underwater vehicles [32; 33; 34] and tracking of underwater targets using acoustic sensor networks (ASN) [35]. Both of those approaches have been validated only in the simulation environment, using ideal communication channels.  
In the subCULTron swarm application, the problem of operation under the conditions of poor communication is tackled. Although each aMussel has two underwater communication devices--green light and acoustic modem--only the latter and its implications on consensus algorithms is considered. Other considerations of application of consensus algorithms to the swarm have been presented in the authors' previous works [36; 37], but are beyond the scope of this paper.