  Autonomous exploration in dynamic environments necessitates a planner that
can proactively respond to changes and make efficient and safe decisions for
robots. Although plenty of sampling-based works have shown success in exploring
static environments, their inherent sampling randomness and limited utilization
of previous samples often result in sub-optimal exploration efficiency.
Additionally, most of these methods struggle with efficient replanning and
collision avoidance in dynamic settings. To overcome these limitations, we
propose the Heuristic-based Incremental Probabilistic Roadmap Exploration
(HIRE) planner for UAVs exploring dynamic environments. The proposed planner
adopts an incremental sampling strategy based on the probabilistic roadmap
constructed by heuristic sampling toward the unexplored region next to the free
space, defined as the heuristic frontier regions. The heuristic frontier
regions are detected by applying a lightweight vision-based method to the
different levels of the occupancy map. Moreover, our dynamic module ensures
that the planner dynamically updates roadmap information based on the
environment changes and avoids dynamic obstacles. Simulation and physical
experiments prove that our planner can efficiently and safely explore dynamic
environments.
