Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids
This addresses scalability issues in navigation for robotics applications, offering an incremental improvement over existing hierarchical and search-based methods.
The paper tackles the problem of efficient path planning in large-scale 3D environments by developing a method that combines any-angle planning with multi-resolution grids, achieving optimality and completeness while improving computational speed, as shown in experiments outperforming sampling-based methods.
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.