Grid Map Path Planning Under Memory-Limited Navigation
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Solution Overview
Problem
Conventional path-finding systems for mobile objects in large grid maps require excessive memory and processing capacity, making it difficult for devices with limited resources to rapidly find appropriate paths without colliding with obstacles.
Innovation Solution
The system generates an approximate path on a reduced grid map, divides it into sections, and uses search algorithms like A-star or D-star to create detailed paths in each section, reducing memory requirements and processing time by leveraging reduced grid map information and adjusting obstacle sizes based on the object's dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire grid map is used for path-finding calculations, then the path accuracy is improved, but the memory capacity and processing time are excessively increased
Solution Approach 1:
The grid map is divided into multiple sections based on an approximate path. Each section contains a subset of grids that are relevant to the path-finding process. By segmenting the map, the patent reduces the amount of memory needed to store and process grid information while maintaining path accuracy within each section.
Solution Approach 2:
The patent extracts only the necessary grids for path-finding by creating a reduced map that contains only the sections relevant to the approximate path. This extraction process removes unnecessary grid data from memory, significantly reducing memory capacity requirements while preserving the essential information needed for accurate path calculation.
2Productivity
If the entire grid map is processed to find the shortest path, then the path optimality is improved, but the processing time is excessively increased
Solution Approach 1:
The path-finding process is segmented into multiple stages: first generating an approximate path using a reduced map, then dividing this path into sections, and finally calculating detailed optimal paths for each section. This segmentation allows the system to achieve path optimality through iterative refinement without processing the entire map at once, thereby reducing processing time.
Solution Approach 2:
The patent performs preliminary action by first generating an approximate path using a reduced map before calculating the detailed optimal path. This preliminary approximate path serves as a guide for subsequent detailed path calculation, allowing the system to focus computational resources on relevant sections and achieve optimality more efficiently.
3Quantity of substance
If the grid map is reduced to decrease memory usage, then the memory capacity requirement is reduced, but the path accuracy is degraded
Solution Approach 1:
The patent segments the reduced map into multiple sections and calculates detailed paths for each section individually. This segmentation allows the system to maintain path accuracy within each local section while using a reduced overall map structure, thereby preserving measurement precision without requiring full map memory capacity.
Solution Approach 2:
The patent applies local quality by calculating detailed optimal paths for each section based on local grid characteristics rather than applying a uniform reduction across the entire map. This allows the system to maintain high path accuracy in critical local areas while using a reduced representation for the overall map structure.
4Reliability
If conventional path-finding algorithms are used on large grid maps, then the path completeness is ensured, but the device complexity is excessively increased
Solution Approach 1:
The patent segments the path-finding problem into manageable sections that can be processed by devices with limited capacity. By dividing the grid map and calculating paths section by section, the system ensures complete path coverage while reducing the computational complexity required for each individual processing step.
Solution Approach 2:
The patent uses partial action by first calculating an approximate path using a reduced map, then refining this path section by section rather than calculating the complete optimal path in a single exhaustive process. This approach ensures path completeness while reducing the processing capacity required at any given moment.
Data Source
AI summary
An apparatus and method for generating a path using a limited memory size are provided. An approximate path is generated based on a reduced grid map generated by reducing an original grid map. Then, an approximate path is mapped onto the original grid map, and the mapped path is enlarged and divided into a plurality of sections based on an available memory size used for path calculation. Based on a start point and a destination point set in each of the divided sections, a detailed path in each of the sections is generated.