Cavendish518/LE-Nav
This work investigates automatic hyperparameter tuning for planners such as DWA and TEB, and our navigation framework LE-Nav can be used to adjust hyperparameters of any optimization-based planner.
This framework helps service robots navigate complex, human-filled spaces more effectively. It takes information about the robot's environment, like images and sensor data, and automatically adjusts the robot's movement settings to navigate like an expert. This is ideal for roboticists and automation engineers deploying robots in dynamic, unpredictable indoor environments.
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Use this if your service robots struggle with unpredictable environments and you need them to adapt their navigation behavior on the fly without constant manual tuning.
Not ideal if your robots operate in static, highly structured environments where fixed navigation parameters are consistently effective.
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35
Forks
2
Language
Python
License
MIT
Category
Last pushed
Sep 09, 2025
Commits (30d)
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