Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids
#path planning #robotics #multi-resolution grids #any-angle planning #navigation #Euclidean shortest paths #scalability #RSS 2025
📌 Key Takeaways
- Researchers developed a novel path planning method combining any-angle optimality with multi-resolution efficiency
- The approach overcomes scalability issues common to search-based methods like A* in large environments
- Extensive experiments demonstrate superior solution quality and speed compared to existing methods
- The framework has been open-sourced to benefit the broader robotics community
📖 Full Retelling
Researchers Victor Reijgwart, Cesar Cadena, Roland Siegwart, and Lionel Ott from an unspecified institution published a groundbreaking paper titled 'Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids' on February 24, 2026, addressing critical challenges in robotic navigation through complex environments. The paper introduces a novel approach that overcomes significant limitations in current path planning methods by combining the optimality properties of any-angle planners with computational efficiency through multi-resolution representations. Traditional path planning methods such as sampling, trajectory optimization, and search-based algorithms like A* struggle with scalability in large-scale, high-resolution maps or fail to exploit the explicit connectivity information available in hierarchical mapping approaches. The researchers' extensive experiments on both real and synthetic environments demonstrate that their proposed method not only maintains solution quality but also outperforms even sampling-based methods in terms of speed, making it particularly valuable for applications requiring Euclidean shortest paths as the foundation of navigation systems. The framework has been open-sourced to encourage further development within the robotics and planning community.
🏷️ Themes
Robotics, Path Planning, Multi-Resolution Mapping
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Original Source
--> Computer Science > Robotics arXiv:2602.21174 [Submitted on 24 Feb 2026] Title: Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids Authors: Victor Reijgwart , Cesar Cadena , Roland Siegwart , Lionel Ott View a PDF of the paper titled Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids, by Victor Reijgwart and 2 other authors View PDF HTML Abstract: 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. Comments: 12 pages, 9 figures, 4 tables, accepted to RSS 2025, code is open-source: this https URL Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.21174 [cs.RO] (or arXiv:2602.21174v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2602.21174 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference:...
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