Speaker: Bart Nabbe, Robotics Institute, School of Computer Science, Carnegie Mellon
Abstract:
The mobility sensors (LADAR, stereo, etc.) on a typical mobile robot vehicle can only acquire data up to a distance of a few tens of meters. Therefore a navigation system has no knowledge about the world beyond this sensing horizon. As a result, path planners that rely only on this knowledge to compute paths are unable to anticipate obstacles sufficiently early and have no choice but to resort to an inefficient behavior of local obstacle contour tracing.
To alleviate this problem, we present an opportunistic navigation and view planning strategy that incorporates look-ahead sensing of possible obstacle configurations. This planning strategy is based on a what-if analysis of hypothetical future configurations of the environment. Candidate vantage positions are evaluated based on their ability of observing anticipated obstacles. These vantage positions identified by this forward-simulation framework are used by the planner as intermediate waypoints. The validity of the strategy is supported by results from simulations as well as field experiments with a real robotic platform. These results also show that opportunistically significant reduction in path length can be achieved by using this framework.
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