Abstract
This letter considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">potential gap</i> , 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">potential gap</i> 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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