Off-Road Unmanned Vehicle Path Planning Using Terrain Cost Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current global path planning methods for unmanned vehicles in off-road environments fail to adequately consider terrain factors, leading to high computational complexity, poor performance, and safety issues due to lack of ground type and elevation information.
Innovation Solution
A method that utilizes satellite elevation data and remote sensing images to construct a digital elevation model, determine slope and land surface relief, and calculate traversal costs based on ground types, ultimately generating a global path using Bresenham's line and Theta* algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If A* algorithm is used for global path planning, then path finding capability is achieved, but computational complexity and memory usage increase significantly
Solution Approach 1:
The patent divides the off-road environment into discrete grid cells, where each grid contains terrain information (elevation, ground type, slope). This segmentation allows the path planning algorithm to process terrain data in manageable units rather than continuous space, reducing computational complexity while maintaining path finding reliability.
Solution Approach 2:
The patent assigns different terrain properties (elevation, ground type, slope) to each grid cell, creating local variations in terrain characteristics. This local quality approach enables the algorithm to make informed path planning decisions based on specific terrain conditions at each location, improving reliability without requiring global processing of all terrain data simultaneously.
2Device complexity
If common grid information is used without terrain factors, then path planning is simpler, but performance in off-road environments deteriorates
Solution Approach 1:
The patent performs preliminary processing of satellite remote sensing images to extract terrain information (elevation, ground type, slope) and pre-computes traversal costs for each grid cell before path planning. This preliminary action stores terrain factors in advance, allowing the path planning algorithm to use this pre-processed information without performing complex real-time terrain analysis, thus maintaining simplicity while improving off-road performance.
Solution Approach 2:
The patent introduces a terrain information layer as an intermediary between the basic grid structure and the path planning algorithm. This intermediary layer contains pre-computed terrain factors (elevation, ground type, slope, traversal cost) that mediate between simple grid representation and complex terrain analysis, enabling the algorithm to consider terrain factors without direct complex processing.
3Productivity
If terrain factors are not considered, then path planning is faster, but vehicle safety in off-road environments is compromised
Solution Approach 1:
The patent pre-computes traversal costs for each grid cell based on terrain factors (ground type, slope, elevation) before path planning. This preliminary calculation of traversal costs allows the path planning algorithm to quickly evaluate different paths by summing pre-computed costs rather than analyzing terrain factors in real-time, maintaining fast path planning speed while ensuring vehicle safety through terrain-aware path selection.
Data Source
AI summary
Provided are a method, system and device for global path planning for an unmanned vehicle in an off-road environment. The method includes: obtaining satellite elevation data and a satellite remote sensing image of a current off-road environment; constructing a digital elevation model (DEM); determining slope and land surface relief of each grid in the current off-road environment; performing gray processing on the satellite remote sensing image to obtain grayscale values of the grids; determining traversal costs of the grids corresponding to different ground types; constructing a global grid map based on the slope and the land surface relief of each grid, as well as the traversal costs corresponding to the different ground types; determining a rugged terrain potential field and path costs; and searching for paths using a Bresenham's line algorithm and Theta* algorithm based on the rugged terrain potential field and the path costs, to generate a global path.