Optimal Any-Angle Pathfinding In Practice

  title={Optimal Any-Angle Pathfinding In Practice},
  author={Daniel Damir Harabor and Alban Grastien and Dindar {\"O}z and Vural Aksakalli},
  journal={J. Artif. Intell. Res.},
Any-angle pathfinding is a fundamental problem in robotics and computer games. [] Key Result In a range of empirical comparisons we show that Anya is competitive with several recent (sub-optimal) online and pre-processing based techniques and is up to an order of magnitude faster than the most common benchmark algorithm, a grid-based implementation of A.

Fast and Almost Optimal Any-Angle Pathfinding Using the 2k Neighborhoods

It is shown that a well-known pre-processing technique, namely subgoal graphs, originally proposed for (non any-angle) 8-connected grids, can be straightforwardly adapted to 2k neighborhoods, a family of neighborhoods that allow an increasing number of movements as k is increased, which yields a pathfinder that computes 2k-optimal paths very quickly.

CWave: High-performance single-source any-angle path planning on a grid

  • D. SinyukovT. Padır
  • Computer Science
    2017 IEEE International Conference on Robotics and Automation (ICRA)
  • 2017
The key idea of the presented algorithm is that it does not represent the grid as a graph and uses discrete geometric primitives to define the wave front and the performance of the algorithm on three maps is demonstrated to be significantly faster than that of Theta∗, Lazy Theta ∗ and Field A∗ adapted for single-source planning.

Optimal Any-Angle Pathfinding on a Sphere

An any-angle pathfinding algorithm for calculating the shortest path between point pairs over the surface of a sphere that preserves all primary benefits of Anya in Euclidean geometry and always returns an optimal path on a sphere and does so entirely on-line, without any preprocessing or large memory overheads.

CWave: Theory and Practice of a Fast Single-source Any-angle Path Planning Algorithm

The paper discusses foundations and experimental validation of CWave, and presents future work to address the limitations of the current implementations and obtain further performance enhancements.

Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles

This work presents two algorithms, grounded in the same idea, that can obtain provably optimal solutions to the considered problem and conducts a thorough empirical evaluation showing that the latter algorithm might be as fast as the previously-known greedy non-optimal solver while providing solutions of better quality.

A Suboptimality Bound for 2k Grid Path Planning

A suboptimality bound is derived, as a function of k, that generalizes previously known bounds for the 4and 8connected grids and strongly suggests that vertices need to be placed in corners in order to obtain near-optimal solutions.

Grid Pathfinding on the 2 k Neighborhoods

Three contributions that enable the construction of effective grid path planners for extended 2 k -neighborhoods are described that are competitive with the "any-angle" path planner Theta$^*$ both in terms of solution quality and runtime.

Any Angle Path Finding in Stochastic Obstacle Scenes

This work presents Any-Angle (ANYA) path finding in discretized stochastic obstacle scenes using the exact algorithm AO* with caching (CAO*), which is already shown to outperform shortest path algorithms by investigating the interval sets.

Euclidean Pathfinding with Compressed Path Databases

This work considers optimal and anytime algorithms for the Euclidean Shortest Path Problem (ESPP) in two dimensions and shows that the auxiliary data structures required by the new method are cheap to build and store and faster than a range of recent ESPP planners.



An Optimal Any-Angle Pathfinding Algorithm

Anya is described: a new optimal any-angle pathfinding algorithm which searches over sets of states represented as intervals which always returns an optimal path.

Any-Angle Path Planning

Three new any-angle find-path algorithms are introduced that propagate information along graph edges without constraining paths to be formed by graph edges, which can be used to quickly find paths that are shorter than the paths found by traditional edge-constrained find- Path algorithms.

Lazy Theta*: Any-Angle Path Planning and Path Length Analysis in 3D

Lazy Theta*, a variant of Theta* which uses lazy evaluation to perform only one line-of-sight check per expanded vertex (but with slightly more expanded vertices), is introduced.

Theta*: Any-Angle Path Planning on Grids

This work presents Theta*, a variant of A*, that propagates informati on along grid edges without constraining the paths to grid edges, and shows experimentally that Theta* finds shorter and more realistic looking paths than either of these existing techniques.

Speeding-Up Any-Angle Path-Planning on Grids

This paper takes advantage of the similarities between Subgoal Graphs and visibility graphs to show that Subgoalgraphs can be used, with small modifications, to quickly find “any-angle” paths, thus extending their applicability.

Block A*: Database-Driven Search with Applications in Any-Angle Path-Planning

A new type of database, the Local Distance Database (LDDB), that contains distances between boundary points of a local neighborhood that calculates the optimal path between start and goal locations given the local distances stored in the LDDB is introduced.

Near Optimal Hierarchical Path-Finding

HPA* (Hierarchical Path-Finding A*), a hierarchical approach for reducing problem complexity in path-finding on grid-based maps, which abstracts a map into linked local clusters and works very well in domains with a dynamically changing environment.

An Optimal Algorithm for Euclidean Shortest Paths in the Plane

The algorithm is based on an efficient implementation of wavefront propagation among polygonal obstacles, and it actually computes a planar map encoding shortest paths from a fixed source point to all other points of the plane; the map can be used to answer single-source shortest path queries in O(log n) time.

Field D*: An Interpolation-Based Path Planner and Replanner

This approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and to produce paths with a range of continuous headings, which is particularly well suited to planning low-cost trajectories for mobile robots.

Accelerated A * Trajectory Planning : Grid-based Path Planning Comparison

The Accelerated A* algorithm is a high performance pathplanning algorithm designed to be used within a multi-agent planning framework solving a UAV collision avoidance problem and reduces memory requirements which makes it usable for large-scale worlds where the original A* is not usable.