Direction Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic

TL;DR

Direction Informed Trees (DIT*) introduces a novel sampling-based path planning algorithm that leverages directional filtering and heuristics to achieve faster convergence and more efficient exploration in high-dimensional spaces.

Contribution

The paper presents DIT*, a new planner that uses direction-based heuristics and filtering to improve convergence speed over existing methods in high-dimensional path planning.

Findings

DIT* converges faster than existing planners in high-dimensional spaces.

Effective directional filtering improves exploration efficiency.

Demonstrated success in real-world environments with complex planning tasks.

Abstract

Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Informed Trees (EIT*), employ effort heuristics to guide the search. Effective heuristics are accurate and computationally efficient, but achieving both can be challenging due to their conflicting nature. This paper proposes Direction Informed Trees (DIT*), a sampling-based planner that focuses on optimizing the search direction for each edge, resulting in goal bias during exploration. We define edges as generalized vectors and integrate similarity indexes to establish a directional filter that selects the nearest neighbors and estimates direction costs. The estimated direction cost heuristics are utilized in edge evaluation. This strategy allows the exploration to share directional information efficiently. DIT* convergence faster than existing single-query, sampling-based planners on tested problems in R^4 to R^16 and has been demonstrated in real-world environments with various planning tasks. A video showcasing our experimental results is available at: https://youtu.be/2SX6QT2NOek