Potential Gap: Using Reactive Policies to Guarantee Safe Navigation
This work addresses safe navigation for robots in unknown environments, but it is incremental as it modifies existing methods for enhanced robustness.
The paper tackles the problem of ensuring collision-free navigation for non-ideal robot models by integrating gap-based methods with artificial potential fields, resulting in a local planner that demonstrates safety and robustness in Monte Carlo experiments.
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.