Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connectivity information. Yet widely used path planning methods such as sampling and trajectory optimization do not exploit this explicit connectivity information, and search-based methods such as A* suffer from scalability issues in large-scale high-resolution maps. In many applications, Euclidean shortest paths form the underpinning of the navigation system. For such applications, any-angle planning methods, which find optimal paths by connecting corners of obstacles with straight-line segments, provide a simple and efficient solution. In this paper, we present a method that has the optimality and completeness properties of any-angle planners while overcoming computational tractability issues common to search-based methods by exploiting multi-resolution representations. Extensive experiments on real and synthetic environments demonstrate the proposed approach's solution quality and speed, outperforming even sampling-based methods. The framework is open-sourced to allow the robotics and planning community to build on our research.

翻译：分层多分辨率体素映射方法被广泛用于表示大型复杂环境，因其能高效捕捉环境的占用与连通性信息。然而，广泛使用的路径规划方法（如采样法和轨迹优化法）并未利用这种显式的连通性信息，而基于搜索的方法（如A*算法）在大规模高分辨率地图中存在可扩展性问题。在许多应用中，欧几里得最短路径构成导航系统的基础。对于此类应用，任意角度规划方法通过连接障碍物顶点与直线段来寻找最优路径，提供了一种简单高效的解决方案。本文提出一种方法，该方法既具备任意角度规划器的最优性与完备性，又通过利用多分辨率表示克服了基于搜索方法常见的计算可处理性问题。在真实与合成环境中的大量实验表明，所提方法在求解质量与速度上均表现出色，甚至优于基于采样的方法。本框架已开源，以便机器人学与规划领域的研究者能在本研究基础上继续推进。