Bradley D., Russell D.W. (eds.) Mechatronics in Action: Case Studies in Mechatronics

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106 D. Toal, E. Omerdic, J. Riordan, and S. Nolan

Other system properties are highlighted in the following:

System Integration

• Check connection and make system integration before mission execution.

• Detect and resolve problems in advance.

• Find optimal positions for onboard equipment.

• Make fast connection with ship and ROV resources and save costly ship time.

Planning

• Build complex underwater scenarios using an expandable database of objects

(ROV and ship models), structures and custom components (moorings, buoys,

etc.).

• Prepare mission plan, including routes, trajectories and way points.

• Generate marketing “proof of concept” visualisations.

Simulation

• Simulate run-time behaviour in normal and critical situations under

disturbances (waves, currents, umbilical effect) using full 6 DOF real-time

simulators.

• Develop and test advanced fault-tolerant control system with autotuning

features.

• Use hardware-in-the-loop to evaluate the performance of embedded controllers.

• Simulate system response to different faults (thruster faults, leakage in the wet

bottle).

Training

• Provide real-feel training without exposing personnel and equipment to hazards

while simultaneously saving expensive ship-based training.

• Train pilots to control the vehicle in normal and harsh conditions, including

strong currents, waves, thruster faults and system errors with no risk of system

damage or loss.

• Provide interaction with dynamic objects using standard input interfaces.

Fault-tolerant Control

• Provide optimal and robust vehicle control in the fault-free case, which

minimises control energy cost function.

• In faulty cases, detect, isolate and accommodate faults by distributing control

energy among operable thrusters and continue missions with minimum loss of

performance and manoeuvrability.

Multi-mode Operations Marine Robotic Vehicle – a Mechatronics Case Study 107

• Using a set of aiding tools, allow operators to be more concentrated on other

tasks.

Enhanced Operator Interface and Auto-enhanced Survey Execution

• Expand existing operator display with real-time 3D VR visualisation.

• Create virtual view points and view the VR scene from different angles.

• Eliminate decision uncertainties due to bad visibility and harsh weather

conditions.

• Log run-time data for later replay and analysis.

• Synchronise sonars and ship auto-tracking based on real-time captured digital

terrain.

Offline Analysis

• Replay the mission looking at the scenario from any angle or viewpoint.

• Use advanced features of real-time simulators and statistical tools to calibrate

instruments and improve accuracy of sonar images.

• Determine factors that need to be improved to make future missions even

better.

The MPPT Ring thus has three innovative components which can function in a

stand-alone mode and/or be used in a fully integrated system. These are:

1. real-time simulator for high-resolution sonar mapping of survey scale

environments;

2. integrated ship and ROV operations assessment, planning, and execution tool;

3. offshore operations planning and real-time operation control tool.

7.2.2 The Virtual Underwater Laboratory

These developments are supported by the implementation of the Virtual

Underwater Laboratory or VUL [7]. This, a crucial component of the MPPT Ring,

is a mixed hardware/software augmented reality marine operations support tool

designed to resolve the weak points associated with traditional survey mobilisation

and operations strategies and to simplify overall system integration.

The VUL uses an open architecture, providing a framework for researchers to

develop, implement and test advanced control algorithms in a safe, simulated

environment before tests are performed in a real-world environment. The signal-

level compatibility between the simulated and real-world environment provides

the opportunity for rapid control prototyping and hardware-in-the-loop

development techniques to be used in system design.

108 D. Toal, E. Omerdic, J. Riordan, and S. Nolan

7.2.3 Architecture and Implementation

Interconnection between the VUL and existing ship and ROV resources, real-

world environment is shown in Figure 7.2. In the simulated environment, real-

world components from the ship (GPS1, GAPS) and the ROV (PHINS, external

sensors, power lines and leak detectors) are replaced with hardware/ software

simulators, as shown in Figure 7.3. More details about the roles, input/output

interface and function of the main hardware VUL components can be found in

Omerdic et al. [7].

All simulators are synchronised with real-time. Full 6 DOF vessel dynamic

models are implemented, including thruster DC-motor dynamics with non-

linearities such as saturation, slew-rate limiter, friction, non-linear propeller load

and so forth. Different components of ship and ROV simulators are simulated as

parallel loops executed with different speeds depending on the dynamics of

components. Payload imaging sonars are simulated remotely on a dedicated PC.

The inputs to and outputs from the virtual instruments (simulators) are compatible

with corresponding real-world instruments at the signal level. In this way, all

communication delays and latencies are present during the control design stage

which then provides a framework to support the design of robust control systems

in a realistic environment.

All software is implemented in LabVIEW, Matlab and Visual Studio C++.

Data (outputs of individual components) are bundled into clusters and transmitted

using network-published shared variables based on the National Instruments

Publish-Subscribe Protocol (NI-PSP). The NI-PSP protocol uses less network

bandwidth and is more efficient than TCP/IP for the given requirements of the NI-

PSP protocol. However, unlike the User Datagram Protocol (UDP), the NI-PSP

protocol guarantees delivery by implementing an additional layer of functionality

on top of the raw UDP structures.

A simulation block diagram including ROV dynamics and kinematics is given

in Figure 7.4. If desired, system states can be “contaminated” with sensor noise.

Hence, in the simulated environment, two types of navigation data are available:

noise-free and noisy data (states). However, only noisy data are available in the

real-world environment. Real-time access to noise-free and noisy data in

simulation mode provides tools to investigate the influence of sensor error

measurements on the performance of the overall control system and to assess the

quality of side-scan and multibeam imagery [8, 9].

Multi-mode Operations Marine Robotic Vehicle – a Mechatronics Case Study 109

Real-Time

Visualisation

Navigation PC

(Multi-beam)

Real-Time

Virtual Reality

Visualisation

Control PC

(Visualisation)

Hand Control Unit

(ROV pilot)

PC104 PHINS

Compact RIO

Compact

Vision

GPS1

GAPS

DVL

Depth Sensor

USBL

Leak detectors

To actuators

To cameras & lights

To power switches

Water

line

Ethernet

Fig. 7.2 VUL in real-world environment

Real-Time

Visualisation

Navigation PC

(Side-scan

Simulator)

Real-Time

Virtual Reality

Visualisation

Control PC

(Visualisation

& Mission

Control)

Hand Control Unit

(ROV pilot)

PC104

Ship Simulator

(Dynamic Model, Propulsion

System, Control Allocation,

Control System)

Compact RIO

Compact

Vision

Leak detectors

Simulator

To actuators

To cameras & lights

To power switches

Ethernet

GAPS

Disturbances

(Waves, Currents,

etc.)

GPS1

ROV Simulator

(Dynamic Model, Propulsion

System)

Cameras & Lights

Simulator

Power Switch

Simulator

PHINS

Simulator PC

Fig. 7.3 VUL in simulated environment

110 D. Toal, E. Omerdic, J. Riordan, and S. Nolan

Disturbances

(Waves, Currents)

Propulsion

System

Serial

Interface/Protocol

Selector

ROV Dynamic

Model

Sensor Noise Model

(Linear & Angular

Velocities)

Sensor Noise Model

(Position &

Orientation)

n τη

η*

ν*

ROV Simulator (Simulator PC)

Fig. 7.4 ROV dynamics and kinematics simulation loop

Table 7.1 Implemented serial output protocols

ASCII output protocols Binary output protocols

Gyrocompass DORADO OUT

Gyrocompass II Simrad EM

HYDROGRAPHY Navigation Short

INDYN Control

OCTANS Standard

PHINS Standard

POSIDINIA 6000

A serial output interface is used to transform noise-free and noisy states into

INS-compatible messages which are sent through available communications ports.

Currently, seven ASCII and four binary output protocols have been implemented,

as indicated in Table 7.1.

7.2.4 Imaging Sonar Simulator

While mathematical models to simulate the pertinent physical processes governing

subsea acoustic imaging have existed for decades, to date, no instance of a PC

based real-time sonar simulator has appeared commercially [10]. This is a direct

result of the limited computational capacity of PC technology combined with both

the massive scale of the oceanographic databases required to initialise and

evaluate meaningful mission scenarios and the numerically intensive nature of

acoustic simulation models. Capable of processing only a minute fraction of the

acoustic transmission-reception cycle in real-time, an hour of simulation time may

typically translate to one minute of real-time using current state-of-the-art acoustic

Multi-mode Operations Marine Robotic Vehicle – a Mechatronics Case Study 111

simulation technology [10]. Consequently, existing sonar simulation models

operate in simple idealised environments and do not address the complexity of the

environmental model or they operate offline.

Both instances represent significant drawbacks for the end user as the key issue

with marine systems modelling and simulation is the capability to rapidly and

exhaustively test the effectiveness of proposed methodologies in a realistic model

of the underwater operating environment. For cost-effective sonar research,

education, and offshore training applications, the ideal solution is a single

integrated model with the requisite resolution and fidelity to accurately perform

real-world representative simulation in real-time on commercial-off-the-shelf PC

technology. Synthetic side-scan sonar imagery generated by simulator is shown in

Figure 7.5.

Fig. 7.5 Synthetic side-scan sonar imagery generated by simulator

7.2.5 Laboratory Configuration

The laboratory interconnection of the VUL components is shown in Figure 7.6.

Two VUL applications run in parallel on the control PC. The first is the Graphical

User Interface (GUI) of Figure 7.7 for an ROV pilot using a joystick, keyboard

and mouse for interaction with the GUI. The second is the Virtual Reality display

of Figure 7.8, used to visualise a VR scene

in real-time, using measurements from

virtual sensors (simulation environment) or real sensors (real-world environment).

Figure 7.9 displays a part of the main screen of the ROV simulator. A part of the

PC104 display is shown in Figure 7.10. This display is not available in the real-

world environment. However, the PC104 can be accessed and remotely

administered from the Control PC through the network using the remote

administrator.

Ship, ROV, ocean surface, seabed and so forth

112 D. Toal, E. Omerdic, J. Riordan, and S. Nolan

Fig. 7.6 VUL – Overall lab setup Fig. 7.7 VUL – GUI (Control PC)

Fig. 7.8 VUL – Virtual Reality display Fig. 7.9 VUL – ROV simulator (Control PC)

(Control PC)

Fig. 7.10 VUL – PC104 shared data

7.3 University of Limerick (UL) Thrusted Pontoon/ROV

7.3.1 Base Vehicle

The Thrusted Pontoon/ROV is a multi-mode of operation vehicle. It can be

operated on the surface (Figure 7.11) as a survey platform either towed (by an

extra towing cable) or thrusted by 4 horizontal thrusters to allow surge, sway and

Multi-mode Operations Marine Robotic Vehicle – a Mechatronics Case Study 113

yaw which is useful in confined spaces or near hazards where a boat and tow

cannot operate. It can also be operated as an ROV (Figure 7.12). In these various

modes of operation, it is used in conjunction with an umbilical and associated

winch; the umbilical carrying vehicle power, control and data from sensors and

instruments.

(a) (b)

Fig. 7.11 Model (a) and real vehicle (b) with surface operation buoyancy configuration –

positively buoyant

(a) (b)

Fig. 7.12 Model (a) and real vehicle (b) with ROV operation buoyancy configuration – neutrally

or marginally positive buoyancy

In the surface-tow or surface thrusted modes of operation, overall vehicle

buoyancy is maintained strongly positive by 8 buoyancy modules mounted on the

pontoon upper frame. While in surface-tow mode, a “Quick Release” arrangement

allows the two top most buoyancy modules to be detached from the vehicle,

reducing overall vehicle buoyancy to neutral or slightly positive. The vehicle can

then be operated in ROV mode for submerged survey with control in six degrees

of freedom (surge, sway, yaw, heave, pitch and roll). The syntactic foam

buoyancy modules are depth rated to 1,000m, while all the other components

114 D. Toal, E. Omerdic, J. Riordan, and S. Nolan

integrated on the vehicle including payload sensors are depth rated beyond

2,000m.

The vehicle is designed and constructed in modules. This is for ease of

handling in inshore surveys on small boats with small crews and each component

in this modular design (buoyancy modules, upper frame, lower tool skid, etc.) is

kept to a two man dry weight lift. The upper frame mounts eight thrusters – four

horizontal and four vertical. The lower tool skid frame caries the payload sensors.

7.3.2 High-resolution Imaging Tool Skid

The sensor systems integrated on the survey tool skid include: black and white and

colour video cameras, Reson 7125 multibeam sonar, Tritech side-scan sonar and

an ixSea fibre gyro-based navigation system with Kalman filter and aiding sensors

(RDI DVL, Digiquartz pressure depth sensor). When deployed off research

vessels in deep operations, a transponder for USBL acoustic positioning is also

integrated on the vehicle to give the best possible navigation/positioning up to

target depths of 1,000 m. The multibeam and side-scan sonar pings are controlled

centrally to avoid the instruments’ transmission reception cycles interfering with

each other [11].

7.3.3 Onboard Electronics and Computer Control

Electronics and controllers (two PC104 controllers and National Instruments

Compact RIO system) are included within wet bottles on the vehicle rather than

on the topside. This facilitates real-time control loop operation without the latency

of the communications channel/network to the surface. All parameters are

monitored topside and command level instructions communicated to the onboard

controllers.

Another advantage of this onboard controller setup is that it facilitates

autonomous vehicle development experimentation for near-intervention scenarios

without the risk of loosing expensive AUV systems. AUV development testing

can thus be performed on the vehicle while tethered and operations can be

monitored from the surface.

Electronics, controls and sensor systems are integrated across four computer

networks for reasons of separation of the time critical control functionality,

payload sensor system control and data logging and high bandwidth imaging sonar

requirements. These networks are as follows:

Net1 – Reson Multibeam Network (Bespoke). The 7125 multibeam system as

supplied calls for a dedicated network to provide deterministic latency for fusion

of sonar imagery and motion reference data.

Multi-mode Operations Marine Robotic Vehicle – a Mechatronics Case Study 115

Net2 – Control and Navigation Network (100BaseT): Devices on this network

include National Instruments Compact RIO system for low-level control and I/O

interface with devices (leak detectors, cameras and actuators), National

Instruments Compact Vision system and a PC-104 for control.

Net3 – Survey Network (1000BaseT): Devices on this network include: PC-104

for sensors (video and sonars); 8 x serial I/O channels; 4 x video channels for

ROV composite/HD cameras.

Net4 – Video Network Option: 4 x video channels for extra science payload

camera expansion.

7.3.4 Fault Tolerant Thruster Control

A hybrid control allocation approach [12] is implemented inside the Control

Allocation Express VI (Figure 7.13). In the fault-free case, optimal control

allocation is guaranteed for all possible command inputs since the hybrid approach

for control allocation finds the exact solution on the entire attainable command

set. This solution is optimal in that it minimises a control energy cost function, the

most suitable criteria for underwater applications.

In fault situations, the fault diagnosis part of the system immediately detects

and isolates any fault in a thruster using fault detection units and delivers

knowledge about faults in the form of a fault indicator vector. The fault

accommodation part of the system uses this vector to accommodate the fault and

eventually switch off the faulty thruster. At the same time, control reallocation is

performed by redistribution of control energy among remaining operable thrusters

such that the mission can be continued with a minimal loss of control

performance. All inputs to the Control Allocation Express VI are normalised to

standard intervals. Depending on user-defined settings inside the dialog box, the

outputs (actuator settings) can be compensated for non-symmetrical propeller T-

curves and adapted (scaled and/or rounded, if necessary) to meet requirements of

thruster input interfaces. Degree of usage of each thruster is controlled by sliders

HT (VT) Saturation Bounds. The position of these sliders is determined by the

ROV pilot or fault diagnosis system depending on the state of thrusters (healthy,

partial fault or total fault).

Bradley D., Russell D.W. (eds.) Mechatronics in Action: Case Studies in Mechatronics - Applications and Education

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