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 it 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 frontier-based planner and receding horizon next-best-view planner in terms of exploration time, path length, and computational time.
翻译:自主勘探要求机器人生成信息丰富的轨迹。虽然基于取样的方法在无人驾驶航空器的探索中效率很高,但其中许多方法没有有效地利用以往规划迭代的抽样信息,导致重复计算和延长勘探时间。此外,很少有人明确显示其在动态环境中的勘探能力,即使它们可以实时运行。为了克服这些限制,我们提议建立一个新的动态勘探规划师(DEP),利用增量抽样和概率路线图(PRM)探索未知的环境。在我们取样战略中,节点是逐步增加的,在探索区域平均分布,产生最佳观点。为了进一步缩短勘探时间并确保安全,我们的规划师优化了本地路径,并根据Euclidean 签名远程功能(ESDF)地图加以完善。与此同时,由于多管规划员,PRM允许拟议规划员快速寻找替代路径,以避免安全勘探的动态障碍。模拟实验表明,我们的方法安全地探索动态环境,超越了基于前沿规划师的动态环境,并超越了下一个最佳时间行距,探索路径和后视距的路径。