Autonomous Navigation Research and Development
Location: Kewdale, WA
Duration: 5 months
Proposed start date: April 2019
The work in this project involves the methods by which an agent (a robot or vehicle) autonomously explores and maps a previously un-mapped area, so that the vehicle may then automatically complete journeys in that area. There are a number of ways that this may be achieved, with some of the methods manually intensive and not commercially viable. Other methods, such as Simultaneous Localisation and Mapping (SLAM) lets you place an autonomous vehicle in an unknown area and then navigate the area, map the area and maintain its own localisation within the area it is mapping. SLAM is the computational problem of generating a map of the agent’s local environment, while simultaneously tracking its position and orientation.
The aim is that there is no human assistance in this process or any prior knowledge from the autonomous vehicle. The inputs available to the vehicle include odometry, laser ranging data, GPS location (if available), visible imagery, with the available inputs then being fused and provided to the algorithms to solve the problem.
Research to be Conducted
RCT want to explore the use of techniques such as SLAM to map regions both in underground and above ground mines, thus improving and enhancing its current automation technology. As an intern working on this project you will be embedded with the engineering teams in the RCT offices and will be able to contribute to the development of the RCT automation products.
We are looking for a PhD student with the following:
- A degree in Engineering and/or Mathematics and Statistics
- Demonstrated ability to assess and solve complex problems and systems
- Ability to visually present results
- Programming skill in a programming language
- Proven report writing skills
- Demonstrated ability in mathematics and algorithm development
- An understanding of industrial/mining vehicle operations
Outcomes of this project will include an initial understanding of the chosen approach’s capabilities when deployed in the various scenarios where autonomous navigation is required. A set of KPIs will need to be developed and presented as part of the project output. Suggestions as to the performance against the KPIs, and methods to improve the results will be expected. The performance parameters will cover both hardware and software aspects of the project.
The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner. The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
27 March 2019
APR – 0879