Towards Real-Time Sensor-Based Path Planning in Highly Dynamic Environments

This paper presents work on sensor-based motion planning in initially unknown dynamic environments. Motion detection and probabilistic motion modeling are combined with a smooth navigation function to perform on-line path planning and replanning in cluttered dynamic environments such as public exhibitions. The SLIP algorithm, an extension of Iterative Closest Point, combines motion detection from a mobile platform with position estimation. This information is then processed using probabilistic motion prediction to yield a co-occurrence risk that unifies dynamic and static elements. The risk is translated into traversal costs for an E* path planner. It produces smooth paths that trade off collision risk versus detours.


Published in:
Autonomous Navigation in Dynamic Environments
Year:
2006
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Springer Tracts on Advanced Robotics
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 Record created 2006-12-07, last modified 2018-01-27

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