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Abstract : |
Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, we have developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect obstacles and recommend steering commands to ensure robot safety. These obstacles are passed to the global system which stores them in a map of the environment. With each addition to the map, the global system uses an incremental path planning algorithm to optimally replan the global path and recommend steering commands to reach the goal. An arbiter combines the steering recommendations to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment. Autonomous outdoor navigators have a number of uses, including planetary exploration, construction site work, mining,, |
