Autonomy algorithms developed by the University of Toronto Institute for Aerospace Studies (UTIAS) researchers could one day make cargo transport on the moon safer and more efficient for astronauts.
As part of a team led by MDA Space, Professor Tim Barfoot (UTIAS) and Alec Krawciw (UTIAS PhD student) are developing technology that will help Canada’s proposed Lunar Utility Vehicle (LUV) navigate between cargo drop-off points during future lunar missions — addressing a key transportation challenge once astronauts land on the moon.
“Lunar exploration involves a landing site and a habitat site about five kilometres apart,” says Barfoot, who is also director of the University of Toronto Robotics Institute.
“The landing site is flat for safe shuttle arrival, while the habitat needs to be shielded from radiation, typically behind rocky terrain. This creates a transportation challenge: astronauts must be able to move all cargo from the shuttle to the habitat.”
Unlike previous planetary missions where rovers explore terrain in multiple directions to collect data, the LUV will make regular round trips between fixed locations to deliver goods and equipment to astronauts. This marks the first time a space rover will be required to repeat the same path, making Barfoot’s visual teach-and-repeat (VT&R) navigation framework well-suited for the mission.
“Teach-and-repeat algorithms allow us to pilot the rover along a predetermined path by manually or physically driving it, and once it learns the path, it can automatically repeat the route as many times as you like,” says Barfoot.
“By automating this part of the mission, it saves astronauts time and energy returning to the landing site to pick up cargo, limits astronaut exposure to lunar elements and increases mission productivity.”
As part of his PhD research, Krawciw is adapting the self-driving technology for integration with the Canadian Space Agency’s (CSA) LUV test vehicle, the Lunar Exploration Light Rover (LELR).