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.
Major difficulties in the robotic fish research are caused by the water media since water is an incompressible fluid with high density and rather difficult environments for mobile robots, including waterproofing issues, great viscous or friction drag and water pressure drag, etc. This project is to focus on three main challenge issues as follows.
Challenge 1: Swimming mechanism and mathematical models.
The fish's swimming mechanism and mathematical models are not very mature. In 1930's, Gray made an assumption about the swimming mechanism. He estimated the power requirements for a cruising dolphin, assuming that its drag can be approximated by that of a rigid model and considering turbulent flow. The calculations indicated that the power required exceeded the estimates of muscle power output by a factor of seven, thus the "Gray Paradox". Later, the reversed Karman vortex-street was observed. To explain it, many researchers proposed their own theories such as "vortex peg" mechanism, undulating pump mechanism and vorticity control mechanism. These theories explained the problem how the fish obtains its energy to move forward in some extent. But the exact theory did not appear and some new observation was in contradiction to the traditional assumption. There is a need to re-examine existing data and to focus on new theoretical development. The mathematical models to describe the kinematics of fish are based on many assumptions, including the resistive hydrodynamic models, 2D waving plate theory, and later wake theories of oscillating foil propulsion. These theories provide great help for the design of artificial propulsion systems and the robotic fish body. For the static water environment or quasi-steady fluid flow, current wake theories work well. But as far as the unsteady water is concerned the above theories will be reformulated to derive dynamic models of the oscillating foils.
Challenge 2: Motion control methods.
The second challenge is the motion control methods for the robotic fish. There are three main motions for robotic fish: cruising, manoeuvring and hovering. Cruising is referred to the swimming in constant speeds. Manoeuvring is to accelerate, decelerate, change direction, turn and swim up-down, etc. Hovering is to stop or stabilize at some position in water. Early robotic fish research has focused on the cruising efficiency, i.e. propulsion efficiency and fluid flow effects. For example, the approach carried out in the MIT and Draper Laboratories projects applied a parameterised kinematic model to a robotic fish. The parameters were determined by extensive experimental trials and worked well. However, the accuracy and the robustness remain a challenge issue when the robotic fish track an unknown trajectory. Some researches focused on the manoeuvring and hovering and detailed hydrodynamic interaction models of a robotic fish were proposed. Nonlinear control method and fuzzy pectoral fins control method were developed based on the quasi-steady fluid flow but the practical performance is not very satisfying. There is little research on the unsteady flow situation.
Challenge 3: Mechanical structure and sensors.
In general, the selection of mechanical structure, sensors and navigation technique are important factors in the design of a robotic fish. Firstly the mechanical structure of a robotic fish is diversified according to the different biological kind of robotic fish. For example, if the robotic fish mimics an eel, its body may have more joints than the robotic fish that mimics a tuna. There is non-uniform basic principle even for the same biological kind of robotic fish due to the immature mechanism of fish swimming. Secondly, most of previous research groups did not use navigation sensor apart from traditional internal sensors such as the six-axis Inertial Measurement Unit in the VCUUV project at MIT. The sensors required by the autonomous navigation of a robotic fish include video cameras (image sensors), hydrophones, infrared sensors and ultrasonic sensors. Due to the waterproof requirement, limited space in a robotic fish and other special features of water, most of navigation sensors used in air would not work well in water. Thirdly, some researchers focus on the bio mimetic fish skin to protect inside circuits and to provide free undulation capabilities. The artificial muscle or other rubber materials belongs to this kind. Other researchers develop the new material that could act as bio mimetic actuators to create undulation movement and provide forward energy for a robotic fish, such as SMA (shape memory alloy), IPMC (ionic polymer-metal composites) and ICPF (ionic conducting polymer film), etc. In summary, it remains a big challenge to realize fully autonomous navigation on robotic fish, where this project aims to make a break through.
As one of the project partners in this consortium, the UESSEX team 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 fully operate to monitor pollution in ports as specified in EU Directive 2005/35. More specifically, we will
- Develop a real-time navigation and control system for a team of 3 robotic fish;
- Deploy a multi-sensor platform in each robotic fish for navigation, communication and pollution monitoring; and
- Create cooperative strategies for the robotic fish team to build a 3D pollutants map