Optimal any-angle path planning in static and dynamic environments

Abstract:Any-angle path planning extends traditional graph-based path planning by allowing movement between any pair of vertices, rather than being restricted by predefined edges. It can find straighter and shorter paths in continuous space with graphs, making it particularly suitable for navigation in open areas. Many any-angle path-planning algorithms have been proposed, but only a few can guarantee optimal solutions. To address this challenge, this article focuses on optimal any-angle path planning on grids and introduces two general techniques that accelerate computation while preserving optimality in both static and dynamic environments: 1) elliptical forward expansion, which leverages ellipse-based neighborhoods to restrict the search space, and 2) field of view, which replaces traditional line-of-sight methods to speed up visibility checks. To integrate these two techniques, inverted and forward scanning are introduced. Inverted scanning establishes visual connections from open nodes, whereas forward scanning initiates scans from closed nodes. Building on the proposed techniques, Zeta* and Zeta*-SIPP are developed for static and dynamic environments respectively. Zeta*, when combined with forward scanning, is similar to the state-of-the-art algorithm Anya and attains comparable performance. Unlike Anya, Zeta* can be readily extended to other settings. For example, Zeta*-SIPP, which extends Zeta* by integrating Safe Interval Path Planning (SIPP), can find time-optimal any-angle paths in dynamic environments. Benchmarking results show that Zeta*-SIPP achieves more than an order-of-magnitude speedup over the state-of-the-art optimal planner TO-AA-SIPP when using either scanning method. Overall, this research identifies the key requirements for achieving optimal any-angle path planning and introduces a unified approach suitable for different environments.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2607.00065 [cs.RO]

(or arXiv:2607.00065v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.00065 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1016/j.artint.2026.104642 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.