Rowan Pivetta

Image Rowan Pivetta


Date commenced:

12 January 2015

Full name:         

Rowan Pivetta

Study/Department Area:

Robotics Engineering

Profile Type:

PhD Candidate


Bachelor of Robotics Engineering at Flinders University


Rowan Pivetta is a PhD student at the Centre for Maritime Engineering, Control and Imaging (CMECI) at Flinders University (South Australia). Rowan’s current research is in coverage path planning for autonomous robots working in enclosed and confined spaces. His keen interest in robotics and electronics enabled him to receive B.E with First Class Honours in Robotics Engineering from Flinders University in 2013. He likes the challenges of robot design and autonomy and enjoys seeing the positive impact robotic systems are having in the home and industry.



Research Project/s summary/description

With corrosion being the major cause of marine structural failures, considerable time and effort is required to inspect and clean ballast tanks. To ensure corrosion doesn’t settle, periodic inspections are required to asses any potential damage. Currently, inspections are undertaken manually and require workers to perform inspection inside these enclosed and confined tanks. Whilst workers wear protective equipment, this environment poses a great health risk and alternative solutions are being explored to remove unnecessary risks.

This project aims to determine if it is possible to develop an autonomous robot to perform a full tank inspection therefore eliminating the need for a human to access these confined spaces. Developing inspection tank robots is an active field of research and as it currently stands, there is not one solution that can ensure 100% coverage of a ballast tank. Ballast tanks are considered to be a complex environment for a robot to operate in. Facing the same challenges as a human worker, robots have to traverse irregularly shaped terrain, manoeuvre in and around internal structures that limit mobility and achieve coverage in many hard to reach places.  To achieve this in a robotic platform requires a lot of forward planning.

My research within this project focuses on the development of 3D coverage path plans that can achieve full coverage for autonomous robots working in enclosed and confined spaces. Coverage path planning (CPP) is a field of research that looks into generating paths that allow robots to autonomously transverse or sense all points in an area or a volume. CPP has been fundamental to a number of household, industrial and military robotic applications such as vacuum cleaners, underwater surveying, autonomous spray painting, UAV surveillance, demining and farming.

This research seeks to increase autonomous capability by developing an architecture that will allow robots to generate compressive offline 3D coverage plans that can be incrementally replanned online within these complex environments. This study will looks into the integration of current state-of-the-art 2D and 3D CPP techniques to find a solution that addresses the gaps that currently makes it difficult to produce coverage plans for inspection robots operating in enclosed and confined environments.


PhD Thesis Title

(if applicable)

3D Coverage Path Planning for Autonomous Robots Performing Inspection Tasks within Enclosed and Complex Environments

Research Supervisors:

Assoc Professor Karl Sammut, Dr Andrew Lammas, Dr Youhong Tang

Associated Researchers:

Mr Andrew Short, Dr. Andrew Lammas

Research Interests:

Autonomous systems

Coverage planning

Assistive robotics

Teaching Interests / Subjects:

Tutor - Engineering Design (1st Year)

Demonstrator - Sensors and Actuators (2nd Year)

Demonstrator – Advanced Control Systems (4th Year)