Potential Gap: Using Reactive Policies to Guarantee Safe Navigation

This paper considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module for hierarchical navigation systems that has provable collision-free properties. Given that APF theory applies to idealized robot models, the pro...

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Bibliographic Details
Published in:arXiv.org 2021-03
Main Authors: Xu, Ruoyang, Feng, Shiyu, Vela, Patricio A
Format: Article
Language:English
Subjects:
Algorithms
Collision avoidance
Collision dynamics
Navigation systems
Potential fields
Properties (attributes)
Robots
Robustness
Unknown environments
Online Access:Get full text
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Summary:This paper considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module for hierarchical navigation systems that has provable collision-free properties. Given that APF theory applies to idealized robot models, the provable properties are lost when applied to more realistic models. We describe a set of algorithm modifications that correct for these errors and enhance robustness to non-ideal models. Central to the construction of the local planner is the use of sensory-derived local free-space models that detect gaps and use them for the synthesis of the APF. Modifications are given for a nonholonomic robot model. Integration of the local planner, called potential gap, into a hierarchical navigation system provides the local goals and trajectories needed for collision-free navigation through unknown environments. Monte Carlo experiments in benchmark worlds confirm the asserted safety and robustness properties by testing under various robot models.
ISSN:2331-8422