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.

翻译：动态环境中的自主探索需要一种能够主动响应变化、为机器人做出高效且安全决策的规划器。尽管大量基于采样的方法在静态环境探索中已取得成功，但其固有的采样随机性以及对先前样本的有限利用往往导致探索效率次优。此外，大多数此类方法在动态场景中难以实现高效重规划和碰撞规避。为解决上述局限性，我们提出面向无人机动态环境探索的启发式增量概率路线图探索（HIRE）规划器。该规划器采用基于概率路线图的增量采样策略，通过向自由空间邻近的未探索区域（定义为启发式前沿区域）进行启发式采样来构建路线图。通过将轻量化视觉方法应用于占据地图的不同层级，即可检测到启发式前沿区域。此外，我们的动态模块确保规划器能根据环境变化动态更新路线图信息，并规避动态障碍物。仿真与物理实验证明，该规划器可高效、安全地探索动态环境。