Autonomous Rovers

Space mining will likely utilise a fleet of heterogeneous robots that must collaborate to accomplish the goal. Thus, apart from meeting the challenges above, an autonomous rover must also manoeuvre and interact with other robots without causing damage. This argues for a high degree of intelligence on each agent and a robust multi-robot coordination system to ensure long-term operation.

At SSL, we are researching machine learning and robotic perception to address some of the key challenges towards autonomous robots for collaborative space mining: lack of satellite positioning systems, navigation in hazardous terrain, and the need for delicate robot interactions. Many of these challenges are encapsulated in the NASA Space Robotics Challenge Phase 2 (SRCP2), in which participants developed software to enable a fleet of heterogeneous rovers to autonomously and collaboratively find and extract resources on a simulated lunar environment.

Our extensively employ machine-learning based robotic perception to accomplish accurate localisation, semantic mapping of the lunar terrain, and object detection to facilitate accurate close range manoeuvring between rovers. The following media summarises the capabilities developed by the Adelaide team.

The solution developed by Team University of Adelaide won 3rd place and an Innovation Award in the NASA SRCP2 competition.

• Teams develop code to coordinate robots (NASA)

• University of Adelaide students win $100,000 from NASA (The Australian)

• University of Adelaide team wins third prize in NASA robotics challenge (Engineers Australia)

• Students honoured in NASA robotics challenge (University of Adelaide)