Recent progress in local and global traversability for planetary rovers

TLDR

Autonomous planetary rovers must operate efficiently due to size, power, and computing constraints in vast unknown environments. This paper extends prior systems to enable autonomous navigation in such domains. The rover uses binocular stereo vision and a voting‑based module system to sense cluttered environments and select the next action. We show that the rover can efficiently avoid obstacles and plan paths with globally intelligent, single‑processor decision making, achieving 100 m travel at 15 cm/s in outdoor tests.

Abstract

Autonomous planetary rovers operating in vast unknown environments must operate efficiently because of size, power and computing limitations. Recently, we have developed a rover capable of efficient obstacle avoidance and path planning. The rover uses binocular stereo vision to sense potentially cluttered outdoor environments. Navigation is performed by a combination of several modules that each "vote" for the next best action for the robot to execute. The key distinction of our system is that it produces globally intelligent behavior with a small computational resource - all processing and decision making are done on a single processor. These algorithms have been tested on our outdoor prototype rover, Bullwinkle, and have recently driven the rover 100 m at a speed of 15 cm/sec. In this paper we report on the extension on the systems that we have previously developed that were necessary to achieve autonomous navigation in this domain.