论文标题
通过增量采样和概率路线图，对动态环境的自动无人机探索
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap
论文作者
论文摘要
自主探索要求机器人迭代地产生信息轨迹。尽管基于采样的方法在无人机勘探方面具有高效，但其中许多方法并未有效利用先前计划迭代中的采样信息，从而导致冗余计算和更长的勘探时间。而且，即使他们可以实时运行，很少有人在动态环境中明确显示了他们的探索能力。为了克服这些局限性，我们提出了一种新型的动态探索计划者（DEP），用于使用增量采样和概率路线图（PRM）探索未知环境。在我们的采样策略中，逐步添加节点，并在探索区域中均匀分布，从而产生最佳观点。为了进一步缩短探索时间并确保安全性，我们的计划者在本地优化了路径，并根据欧几里得签名的距离函数（ESDF）地图对路径进行完善。同时，作为多Query计划者，PRM允许拟议的计划者快速搜索替代路径，以避免动态障碍以进行安全探索。仿真实验表明，我们的方法可以安全地探索动态环境，并在探索时间，路径长度和计算时间方面优于基准计划者。
Autonomous exploration requires robots to generate informative trajectories iteratively. Although sampling-based methods are highly efficient in unmanned aerial vehicle exploration, many of these methods do not effectively utilize the sampled information from the previous planning iterations, leading to redundant computation and longer exploration time. Also, few have explicitly shown their exploration ability in dynamic environments even though they can run real-time. To overcome these limitations, we propose a novel dynamic exploration planner (DEP) for exploring unknown environments using incremental sampling and Probabilistic Roadmap (PRM). In our sampling strategy, nodes are added incrementally and distributed evenly in the explored region, yielding the best viewpoints. To further shortening exploration time and ensuring safety, our planner optimizes paths locally and refine them based on the Euclidean Signed Distance Function (ESDF) map. Meanwhile, as the multi-query planner, PRM allows the proposed planner to quickly search alternative paths to avoid dynamic obstacles for safe exploration. Simulation experiments show that our method safely explores dynamic environments and outperforms the benchmark planners in terms of exploration time, path length, and computational time.
