3D Terrain Path Planning With Goal-Biased Random Trees
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current path planning algorithms for unmanned vehicles, such as the RRT algorithm, are primarily based on two-dimensional obstacle spaces and struggle to effectively plan safe and collision-free paths in complex three-dimensional terrains, particularly in scenarios requiring asymptotically optimal routes like military reconnaissance.
Innovation Solution
A method and system that utilize a goal bias strategy and multi-sampling strategy to generate random nodes, incorporate elevation detection, and perform path search cut-off detection to build a path cut-off random tree, optimizing the RRT algorithm for three-dimensional terrain path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the traditional RRT algorithm is used for path planning, then the algorithm structure remains simple and search capability is maintained, but the algorithm cannot effectively handle three-dimensional terrain and produces incomplete path planning results
Solution Approach 1:
The patent extends the traditional two-dimensional RRT algorithm to three-dimensional terrain by introducing elevation values as a third dimension. The random node generation now considers 3D coordinates (x, y, z) where z represents elevation, and the nearest node search incorporates 3D distance calculation. This dimensional extension enables the algorithm to handle complex three-dimensional terrain while maintaining the core RRT framework.
Solution Approach 2:
The patent modifies the node generation parameters by introducing elevation thresholds and 3D coordinate systems. The distance calculation parameter is changed from 2D Euclidean distance to 3D distance incorporating elevation differences. These parameter changes enable the algorithm to adapt to three-dimensional terrain without fundamentally altering the algorithm structure.
2Reliability
If path planning is performed without elevation detection, then the planning speed is fast and the process is simple, but the planned path may not be safe or collision-free in complex terrain
Solution Approach 1:
The patent performs preliminary elevation detection during the node generation and expansion phases, before final path construction. By checking elevation thresholds and 3D distance constraints in advance, the algorithm ensures that only valid nodes are added to the tree, guaranteeing path safety without requiring post-processing validation.
Solution Approach 2:
The patent implements feedback mechanisms through elevation detection and nearest node validation. Each generated node is evaluated against elevation constraints and 3D distance criteria, with invalid nodes rejected and regenerated. This feedback loop ensures path safety while maintaining efficiency through targeted validation rather than exhaustive checking.
3Loss of time
If the RRT algorithm operates without goal bias strategy, then the search is uniformly random, but the convergence to target point is slow and planning time is extended
Solution Approach 1:
The patent applies local quality by implementing goal bias in the random node generation process. When a node is selected for expansion, there is a biased probability that the target point itself is chosen as the random node, creating a local search tendency toward the goal. This maintains overall random exploration while locally directing search effort toward the target, reducing planning time without sacrificing the probabilistic completeness of RRT.
Data Source
AI summary
The present disclosure provides a method and system for path planning of an unmanned vehicle in a three-dimensional terrain and relates to the technical field of path planning. The method includes building a random tree with an initial point of a to-be-planned path as a node; generating a random node based on a goal bias strategy and a multi-sampling strategy; determining a node in the random tree and with a minimum two-dimensional distance from the random node as a nearest node; determining a direction from the nearest node to the random node as an extension direction; determining a point corresponding to a preset step length as a to-be-determined node in the extension direction with the nearest node as a starting point; and updating the random tree or determining a path cut-off random tree through elevation detection and path search cut-off detection, so as to determine the to-be-planned path.