Implementation and Management Approach

The main objective of the SHOAL project is to design and develop three fully functional robotic fish equipped with chemical sensors and a scalable communications infrastructure.

1. The robot fish will detect pollution with on-board electrochemical sensors.

2. By using underwater communications technology, the fish will communicate its findings to the other fish and to the hub located on shore.

3. Swarm intelligence will allow the localisation of the pollution source.

4. Results will be transferred to the Port Authority for action.

One of the project partners in this consortium, the University of Essex, has successfully built the advanced robotic fish which swam autonomously at London Aquarium for nearly two years. The major achievements include novel hybrid control architecture, a 3D fish simulator, fish swimming patterns, simple fish behaviours and layered learning of individual robotic fish. Based on the existing success, the team will work on a new generation of robotic fish that can operate autonomously in a port to search and monitor harmful contaminants, other pollutants and leaks in vessels cooperatively.

Artificial Intelligence and Swarm intelligence

BMT is in charge of creating intelligence for the fish. This will involve both an intelligence for each individual fish and the development of a swarm AI. The fish will have to be able to manage multiple problems; avoiding obstacles, knowing where to monitor pollution, finding the source of a pollution, maintaining communication distance from the other fish, recharging themselves at the charging station and many more. Each individual robotic fish will have an array of sensors and external information that will allow it to navigate the environment

Robotic Design

In nature, fish have astonishing swimming ability after thousands years evolution. The observation of real fish shows that this kind of propulsion is more noiseless, effective, and manoeuvrable than propeller-based propulsion, which has inspired the researchers to build robotic fish that can interact with the aquatic environment efficiently. Instead of the conventional propellers used in ships or underwater vehicles, the undulation movement provides the main energy of robotic fish. The major applications of robotic fish are in the marine & military fields such as detecting leakage of oil pipelines, monitoring water quality monitoring, mine countermeasures, etc.

Chemical Analysis

The established methods for the detection of pollutants in waters are based on sampling and analysis of discrete water samples. The analysis is performed in laboratories located remotely away form the sampling sites and frequently the chemical analysis is carried out with expensive apparatus, such as bench-top spectrometers and chromatographs. The issue with these methods is that, although they function well and provide reliable chemical data, they are laboratory-based, personnel-dependent, time-consuming and expensive. However an emerging research trend in the last 1-2 decades has been the growing interest in research in chemical sensors and biosensors which can meet the monitoring needs of those interest in polluted water analysis.

Communications

Until now underwater communications (UComms) have been mainly focused on point to point links in open sea between an underwater vehicle or seabed sensors and the support ship. Theoretically radio waves are usable in water, however they are of little use for this purpose because of their great attenuation at short distances. Optical solutions such as green-blue laser suffers from scattering and needs a high precision in pointing. The acoustic communication appears as the preferable solution; however the channel is far from ideal, especially in the very shallow water conditions of a port. It has a very limited bandwidth, and causes severe signal dispersion both in time and frequency domain.

The SHOAL project will develop an Underwater Mobile Ad-hoc Network (UMANet) in harsh environmental conditions.

Another important key point is the impact of such communications on the existing environmental constituents and especially real fishes which use the port as their life area. Special care will be taken to protect this unstable balance in selecting appropriate frequency range, reducing acoustic level and minimising data exchange.

Hydrodynamics

The fluid dynamics part of this project will cover two main components: computational fluid dynamics (CFD) and hydrodynamic testing.

Computational Fluid Dynamics: This component will integrate the hydrodynamic and motion control components. The hydrodynamic component will simulate fish movement while motion control will realise fish motion from one location to another. Pollutant spread in the water will also be modelled. The task involves following computational works:

• Modelling pollutant spread due to current effect.
• Modelling pollutant spread due to diffusion
• Modelling pollutant spread due to sea wave
• Modelling pollutant spread due to passing ships

Hydrodynamic Testing: The hydrodynamic testing programme will provide benchmarking data for the CFD study, and will also provide an understanding of the relationship between the manoeuvring behaviour of the fish with the swimming strategy employed (i.e. the amplitudes, frequencies and phases of the motions of the various sections). This will form the basis of a simplified engineering model of the motion control which will be used as a screening tool to identify key cases for more detailed analysis. Through tank (port, harbour) modelling of pollutant spread, results will give clear insight into how pollutant is spreading in the different environment or conditions. Based on these information, the SHOAL system can be designed with more accuracy and efficiency.

Technology solution

Robotic Design

Simulation: The hydrodynamic component will simulate fish movement while motion control will realize fish motion from one location to another. Pollutant spread in the water will also be modelled. The task involves following computational works:

• Modelling pollutant spread due to current effect.
• Modelling pollutant spread due to diffusion
• Modelling pollutant spread due to sea wave
• Modelling pollutant spread due to passing ships

Chemical sensing: Electrochemical Sensors for

• Phenol derivatives;
• Heavy Metals;
• Water Quality (Dissolved O2, Conductivity, ORP).

Instrumentation:

• Hardware compatible and optimised for selected sensor system and detection principle.
• Signal processing and data interpretation algorithms included in user-friendly interface.

Communications

There are two major challenges here:

• Underwater communication

Theoretically, radio waves and optical solutions can be used for communications under waters. The acoustic communication appears as the preferable solution. The SHOAL project will develop an Underwater Mobile Ad-hoc Network in the port environmental conditions which could be seen as one of the most difficult in terms of acoustic propagation.

• 3D localisation of the robotic fish

Acoustic underwater positioning systems already exist as independent functions. Such systems have generally been single role, application specific. The target would be to integrate this function within the communications. The hub will be used as positioning reference for the swarm. In a classical navigation system the mobile has to send recurrent signal to be tracked. Applied to a swarm manoeuvring in a reduced area, this solution will generate a permanent acoustic noise which could affect other systems and disturb the environment. In the frame of this project, the positioning methodology will take advantage of navigation intelligence of the robot.

Advanced intelligence

The fish will have to be able to manage multiple problems:

• Avoiding obstacles,
• Knowing where to monitor pollution,
• Finding the source of a pollution,
• Maintaining communication distance from the other fish,

Each individual robotic fish will have an array of sensors and external information that will allow it to navigate the environment.

Current research into swarm robotics concentrates on emergent behaviour developing from biologically inspired algorithms. These can be based on movement and behaviour of insects, flocks of birds, shoals of fish or other groups. These techniques concentrate on using local information and simple rules to establish a complex group behaviour as a whole in order to achieve predetermined goals. Two examples of swarm intelligence algorithms will be utilized in SHOAL.