Overview
Anytime D* is a incremental search method for dynamic pathfinding. It works by refining a repairable path while gradually tightening the heuristic bound.
SpecializedSource checked
Anytime D*
Anytime D* addresses dynamic pathfinding by refining a repairable path while gradually tightening the heuristic bound.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Pathfinding
- Difficulty
- Advanced
- Family
- Incremental Search
- Tier
- Specialized
- Status
- Verified
- Review
- Source Checked
- Sources
- 2
- Formulas
- 0
- Relationships
- 1
- Code example
- Not ready
- Visualization
- Ready
- Aliases
- Anytime D-star
Core Concepts
dynamic pathfindingincremental searchpathfinding
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(1)
Average
O(n)
Worst
O(n)
Space
O(1)
When to Use
- Use Anytime D* when solving dynamic pathfinding and you want to benefit from refining a repairable path while gradually tightening the heuristic bound.
- Choose it when O(n) average/worst-case time and O(1) memory match your workload profile.
- Prefer it over Visibility Graph Roadmap or 0-1 BFS when the problem structure favors dynamic pathfinding.
When Not to Use
- Avoid when path costs or obstacles change in ways the method cannot update efficiently.
- Avoid when approximate paths are unacceptable and the method does not guarantee optimality.
What It Does
- Computes dynamic pathfinding within Incremental Search workloads (O(n) time, O(1) space).
- Works by refining a repairable path while gradually tightening the heuristic bound.
- Maintains key invariants across dynamic pathfinding, incremental search, pathfinding.
Use Cases
- Production and research workloads requiring dynamic pathfinding via refining a repairable path while gradually tightening the heuristic bound.
- Incremental Search pipelines where O(n) time and O(1) space fit system constraints.
- Engineering comparisons against Visibility Graph Roadmap and 0-1 BFS when tuning dynamic pathfinding.
Inputs
- Incremental Search input instance for dynamic pathfinding
- Configuration parameters controlling dynamic pathfinding
Outputs
- Computed dynamic pathfinding solution produced by Anytime D*
- Diagnostic state over dynamic pathfinding and incremental search
Pseudocode
- 1.Initialize the search space, start/goal states, and cost/priority tables for Anytime D*.
- 2.Insert the initial state into the open frontier or roadmap with its baseline cost and heuristic estimate.
- 3.Expand or sample candidate transitions by refining a repairable path while gradually tightening the heuristic bound.
- 4.Update path costs, parent pointers, or conflict constraints whenever an improved trajectory is found.
- 5.Reconstruct and return the collision-free path from start to goal once the termination criterion holds.
Canonical Example
- Input
- Incremental Search input instance for dynamic pathfinding configured for a representative dynamic pathfinding test case
- Output
- Computed dynamic pathfinding solution produced by Anytime D* satisfying Anytime D* invariants
- Explanation
- Anytime D* processes the input by refining a repairable path while gradually tightening the heuristic bound, tracking dynamic pathfinding and incremental search to produce the verified dynamic pathfinding result.
Implementation Notes
- Separate search state from world state so changes can be tested.
- Use small maps with known optimal answers before large scenarios.
- Track whether returned paths are optimal, bounded, or heuristic.
Common Pitfalls
- Using an inadmissible heuristic where optimality is expected.
- Ignoring dynamic obstacles, time-dependent costs, or changing edge weights.
- Testing only open grids and missing narrow-passage cases.
Interactive Mechanism Walkthrough
2D Grid Wavefront & TrajectoryShow refining a repairable path while gradually tightening the heuristic bound on a small dynamic pathfinding example.
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Step 1 of 5: Initialize the search space, start/goal states, and cost/priority tables for Anytime D*.
pathfinding-mechanismNotes
Anytime D* operates in O(n) time and O(1) space within the Incremental Search family. Often compared with Visibility Graph Roadmap and 0-1 BFS.
Sources (2)
What each reference supports for this entry.
Artificial Intelligence: A Modern Approach
BookAI search reference supporting Anytime D* heuristic evaluation, frontier ordering, replanning behavior, or completeness tradeoffs.
Open sourceD* Lite
PaperReference for incremental D*-style replanning behavior and rhs-value updates.
Open sourceExplore Next
Good next jumps from the relationship graph, ranked for useful discovery.
Relationships
View full relationship pageConceptual neighborD* Lite
Anytime D* extends D*-style replanning with bounded-suboptimal anytime behavior.
Tags
PathfindingGraph