Hybrid Path Planning for Sparse Road Network Navigation
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Solution Overview
Problem
Current navigation technologies relying on vector pathfinding algorithms suffer from poor accuracy in regions with sparse road network data, leading to inaccurate path planning.
Innovation Solution
A hybrid navigation method combining vector and raster pathfinding algorithms, where vector algorithms are used for backbone paths and raster algorithms are used for intermediate paths to enhance precision, especially in regions with sparse road networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vector pathfinding algorithm is used for navigation, then planning efficiency is improved, but path planning accuracy deteriorates in regions with sparse road network data
Solution Approach 1:
The patent segments the path planning process into two distinct parts: a backbone path planned using vector pathfinding algorithm for efficiency, and intermediate paths planned using raster pathfinding algorithm for accuracy. This segmentation allows each algorithm to operate in its optimal domain, resolving the contradiction between efficiency and accuracy.
Solution Approach 2:
The patent merges vector pathfinding and raster pathfinding algorithms into a hybrid navigation system. The vector algorithm handles the backbone path (major routes) while the raster algorithm handles intermediate paths (local navigation), combining the strengths of both algorithms to achieve both efficiency and accuracy simultaneously.
2Speed
If vector pathfinding algorithm is used for path planning, then computation speed is improved, but accuracy in avoiding obstacles deteriorates
Solution Approach 1:
The patent segments obstacle avoidance into two stages: the backbone path is computed quickly using vector pathfinding, while only the critical intermediate segments near obstacles are recomputed using raster pathfinding. This segmentation maintains high computation speed for overall path planning while ensuring reliable obstacle avoidance where needed.
Solution Approach 2:
The patent applies different algorithmic qualities to different parts of the path: vector pathfinding is used for the bulk of the path (where speed is critical), while raster pathfinding is applied locally to intermediate paths (where accuracy is critical). This local quality approach optimizes both computation speed and obstacle avoidance reliability.
3Adaptability or versatility
If road network data is used for navigation, then navigation coverage is improved, but accuracy in regions with sparse data deteriorates
Solution Approach 1:
The patent introduces raster pathfinding as an intermediary algorithm that bridges the gap between vector pathfinding and actual navigation in sparse regions. When road network data is insufficient, the raster algorithm acts as a mediator to plan accurate intermediate paths based on environmental sensor data, ensuring both coverage and precision.
Solution Approach 2:
The patent changes the underlying data representation parameters from vector-based road network graphs to raster-based grid maps for intermediate path planning. This parameter change enables the system to operate accurately in regions where vector road network data is sparse, maintaining both navigation coverage and path accuracy.
Data Source
AI summary
A navigation method, device, and system are provided in this application. The method includes: obtaining a first target location and a second target location (101), where the first target location is a location closest to a start location on K road segments, the second target location is a location closest to an end location on the K road segments, and K is a positive integer; obtaining a first path from the first target location to the second target location based on a vector pathfinding algorithm and the K road segments (102); obtaining a second path from the start location to the first target location and a third path from the second target location to the end location (103); and determining a fourth path from the start location to the end location (104).