Autonomous exploration is a crucial aspect of robotics that has numerous applications. Most of the existing methods greedily choose goals that maximize immediate reward. This strategy is computationally efficient but insufficient for overall exploration efficiency. In recent years, some state-of-the-art methods are proposed, which generate a global coverage path and significantly improve overall exploration efficiency. However, global optimization produces high computational overhead, leading to low-frequency planner updates and inconsistent planning motion. In this work, we propose a novel method to support fast UAV exploration in large-scale and cluttered 3-D environments. We introduce a computationally low-cost viewpoints generation method using novel occlusion-free spheres. Additionally, we combine greedy strategy with global optimization, which considers both computational and exploration efficiency. We benchmark our method against state-of-the-art methods to showcase its superiority in terms of exploration efficiency and computational time. We conduct various real-world experiments to demonstrate the excellent performance of our method in large-scale and cluttered environments.
翻译:----
自主探测是机器人技术的重要方面,具有众多应用。目前,大多数现有方法会贪心地选择能够最大化即时奖励的目标。这种策略在计算效率方面非常高,但对于整体探测效率来说不足够。近年来,一些最先进的方法被提出,这些方法可以生成全局覆盖路径并显著提高整体探测效率。然而,全局优化会产生高计算开销,导致规划器更新的低频率和不一致的规划动作。在本文中,我们提出了一种新方法,以支持大规模和杂乱三维环境中的快速无人机探测。我们介绍了一种计算低成本的视点生成方法,使用新颖的无遮挡球体。此外,我们将贪心策略与全局优化相结合,同时考虑计算和探测效率。我们将我们的方法与最先进的方法进行了基准测试,以展示其在探测效率和计算时间方面的优越性。我们进行了各种实际环境的实验,以展示我们的方法在大规模和密集环境中的出色性能。