  This paper extends the family of gap-based local planners to unknown dynamic
environments through generating provable collision-free properties for
hierarchical navigation systems. Existing perception-informed local planners
that operate in dynamic environments rely on emergent or empirical robustness
for collision avoidance as opposed to providing formal guarantees for safety.
In addition to this, the obstacle tracking that is performed in these existent
planners is often achieved with respect to a global inertial frame, subjecting
such tracking estimates to transformation errors from odometry drift. The
proposed local planner, called dynamic gap, shifts the tracking paradigm to
modeling how the free space, represented as gaps, evolves over time. Gap
crossing and closing conditions are developed to aid in determining the
feasibility of passage through gaps, and Bezier curves are used to define a
safe navigable gap that encapsulates both local environment dynamics and
ego-robot reachability. Artificial Harmonic Potential Field (AHPF) methods that
guarantee collision-free convergence to the goal are then leveraged to generate
safe local trajectories. Monte Carlo benchmarking experiments are run in
structured simulation worlds with dynamic agents to showcase the benefits that
such formal safety guarantees provide.
