Potential Gap: Using Reactive Policies to Guarantee Safe Navigation

TL;DR

This paper develops a local planning method combining gap-based navigation with artificial potential fields, ensuring collision-free movement for robots with realistic models in unknown environments.

Contribution

It introduces algorithm modifications to traditional APF methods, making them robust and provably safe for non-ideal robot models within hierarchical navigation systems.

Findings

Confirmed safety through Monte Carlo experiments

Enhanced robustness to nonholonomic robot models

Effective integration of gap-based and APF methods

Abstract

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.