This research in long-range autonomous rover navigation seeks to enable efficient path planning and reliable navigation for single-command, one-kilometer autonomous rover traverse. It builds upon our previous work in global path planning
The scientific data return on Mars rover exploration missions is
fundamentally limited by the operational overhead associated with
energy collection, power management, communications and travel between
interesting sites. Time efficiency can be achieved through a
combination of detailed planning at low frequency on Earth and fast
response replanning on the rover, thereby enabling intelligent
decisions at a far greater bandwidth than possible under direct human
Using our solar-powered rover Hyperion, we have demonstrated preliminary results in long-range autonomous navigation with traverses of 300-600 meters in a single command using 30-meter resolution digital elevation models and odometric position estimation (meaning without GPS). We have achieved one instance of single-command 1-kilometer autonomous traverse in the Chilean Desert in April 2003.
We are working to enable long traverses that are reliable and efficient. By reliable, we mean that the rover minimizes the risk that it will be lost, damaged, or require remote operation. By efficient, we mean that the rover minimizes critical resources to accomplish its objectives.
Our present path planning algorithms reason about distance, time, energy, and risk, but produce routes that are optimal only in discretized planning space. We extending the algorithms to the continuous realm to find the truly optimal routes, thus improving both efficiency and reliability (if risk is minimized in the plan). At present, we produce routes based on digital elevation models. As the rover drives, it updates its map with data from onboard sensors and re-plans as needed. This makes limited use of the sensor information. We propose to update all related areas of the map so that the benefit is global, not just local. The newly planned routes are based on a more accurate map, and thus are more reliable and efficient.
Present navigation algorithms focus on the near-field, leaving a gap between the maximum range analyzed by the rover (< 10m) and the minimum resolution of orbital models (>30m resolution). This myopic view is prone to driving into super-obstacles like embankments, drainages, craters, hummocks, and dunes. We propose to extend the perception analysis to the far-field and modeling to multiple scales to enable planning for these terrain features.
For information contact: Tony Stentz or: David Wettergreen
This research is supported by NASA under contract from JPL.