Space Robotics and Rovers

We are affiliated with the Andy Thomas Centre for Space Resources.

Autonomous Rovers

In-Situ Resource Utilisation (ISRU) refers to mining resources on other planetary bodies (primarily the Moon and Mars) and using them to support longer and deeper space missions. The difficulties of building a large-scale human presence in space and the lack of real-time interplanetary communication means that space mining will have to depend on robots with a high level of autonomy. However, the lack of infrastructure means that autonomous rovers will need to contend with hazardous terrain, lack of accurate positioning systems, limited power supply, and many other difficulties. Space robotics has been identified by NASA as a Centennial Challenge.

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.

NASA Space Robotics Challenge

At The University of Adelaide, 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. Much of our efforts have been conducted under 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.

Simulated lunar environment provided by NASA as part of SRCP2. Two lunar landers (a processing plant and a recharge station) were provided along with 6 rovers.
Close-up of available rover types (excavator, hauler, scout).

Robotic Perception for Autonomous Rovers

The solution developed by Team University of Adelaide extensively employed 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.

Object detection on the lunar surface.

Results and Media Coverage

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

Team Co-Leaders Ravi Hammond (3rd from left) and James Bockman (4th from left) receiving a mock cheque from NASA (for award of US$75,000) from DVCR Prof Anton Middelberg (1st from left), SA Chief Entrepreneur Andrew Nunn (2nd from left) and ECMS Dean Prof Katrina Falkner (5th from left).

Relevant Publications

  • Ragav Sachdeva, Ravi Hammond, James Bockman, Alec Arthur, Brandon Smart, Dustin Craggs, Anh-Dzung Doan, Thomas Rowntree, Elijah Schutz, Adrian Orenstein, Andy Yu, Tat-Jun Chin and Ian Reid. Robotic Vision for Space Mining. 2021.