4.6 Article

Potential Gap: A Gap-Informed Reactive Policy for Safe Hierarchical Navigation

Journal

IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 6, Issue 4, Pages 8325-8332

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3104623

Keywords

Collision avoidance; vision-based navigation; reactive and sensor-based planning

Categories

Funding

  1. NSF [1849333]
  2. Direct For Computer & Info Scie & Enginr
  3. Div Of Information & Intelligent Systems [1849333] Funding Source: National Science Foundation

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This study proposes a local planning module called "potential gap" that integrates gap-based local navigation methods with artificial potential field (APF) methods for hierarchical navigation systems. By using sensory-derived local free-space models to detect gaps and synthesize the APF, collision-free trajectories can be achieved. Algorithm modifications are introduced to correct errors and enhance robustness for non-ideal models, particularly nonholonomic robot models. Integration of the potential gap local planner into hierarchical navigation systems provides local goals and trajectories for collision-free navigation through unknown environments, as confirmed by Monte Carlo experiments in benchmark worlds.
This letter considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module, called potential gap, for hierarchical navigation systems. 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. Trajectories derived from the APF are provably collision-free for idealized robot models. The provable property is lost when applied to more realistic models. A set of algorithm modifications correct for these errors and enhance robustness to non-ideal models, in particular a nonholonomic robot model. Integration of the potential gap local planner 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.

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