Direction Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic
🤖 AI Summary
In high-dimensional optimal path planning, designing heuristic functions that simultaneously ensure accuracy and computational efficiency remains challenging. To address this, we propose the Directional Information Tree (DIT*) algorithm. Our method introduces a directional filtering mechanism grounded in generalized vector modeling and directional similarity indexing, enabling fast and accurate estimation of edge directional costs. Within a sampling-based framework, DIT* tightly couples directional similarity metrics, direction-aware nearest-neighbor search, and directional-cost heuristics to significantly improve sample quality and exploration guidance. Experimental results demonstrate that DIT* achieves faster convergence than state-of-the-art single-query planners (e.g., EIT*) in ℝ⁴–ℝ¹⁶ configuration spaces. Furthermore, its effectiveness and robustness are validated across diverse real-world robotic tasks, including manipulation and mobile robot navigation.
Application Category
📝 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
Problem
Research questions and friction points this paper is trying to address.
Optimizing path planning via direction heuristics
Improving computational efficiency in high-dimensional spaces
Accelerating convergence for sampling-based planners
Innovation
Methods, ideas, or system contributions that make the work stand out.
Direction filter for optimal path planning
Direction cost heuristic guides exploration
Sampling-based planner with goal bias
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