Selective Tile-Based Path Planning for Obstacle-Rich Areas
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Solution Overview
Problem
Existing path planning algorithms for autonomous vehicles face challenges in efficiently processing large areas with numerous obstacles, leading to latency and memory overhead issues, particularly when dealing with complex environments like those requiring high-resolution grid-based computations.
Innovation Solution
The approach involves dividing the controlled area into smaller geometric patterns, applying path finding algorithms selectively within these patterns, and combining the results with external path information to optimize memory usage and computation efficiency, allowing for real-time path generation around obstacles like no-fly zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If path finding algorithms are applied to the entire controlled area, then complete path coverage is achieved, but computational time and memory usage increase significantly
Solution Approach 1:
The controlled area is divided into multiple geometric patterns (tiles), and the path finding algorithm is applied selectively to only those patterns that contain relevant items (obstacles, no-fly zones, take-off/landing zones). This segmentation approach maintains complete path coverage while significantly reducing computational time and memory usage by avoiding processing of empty or irrelevant areas.
2Measurement precision
If high-resolution grid-based computations are used, then path planning precision is improved, but memory requirements and processing overhead increase
Solution Approach 1:
High-resolution grid-based computations are applied locally only to patterns containing relevant items, rather than uniformly across the entire controlled area. This allows precise path planning around obstacles and no-fly zones while reducing overall memory requirements by using coarser or no computation in irrelevant areas.
3Quantity of substance
If the controlled area is divided into smaller patterns, then memory usage is reduced, but the complexity of managing multiple patterns increases
Solution Approach 1:
The controlled area is divided into smaller geometric patterns that can be independently processed, reducing memory usage for each pattern. The system manages these patterns through systematic identification and selective processing based on the presence of relevant items, balancing memory efficiency with manageable complexity.
4Productivity
If path finding algorithms are applied selectively only to patterns with relevant items, then computational efficiency is improved, but the risk of missing paths in boundary areas increases
Solution Approach 1:
The controlled area is segmented into patterns, and the system identifies patterns containing relevant items through systematic scanning. By ensuring that all patterns with relevant items are processed and that boundary patterns are properly handled, the system maintains path completeness while achieving computational efficiency through selective processing.
Data Source
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AI summary
Apparatuses and methods for movement control of a device are disclosed. The apparatus comprises memory for storing information of patterns covering sectors of the area. The stored information indicates whether the patterns have at least one item relevant to movement in the area. The information has been configured based on determination of at least one pattern that has at least one item relevant to movement in the area, division of the determined at least one pattern into smaller patterns, determination of at least one of the smaller patterns with at least one item relevant to movement in the area, and repeat of the division until predefined smallest pattern size is reached. A processor is configured to determine whether a search path between a first location and a second location extends through at least one pattern having at least one item relevant to movement in the area, and to selectively use a path finding algorithm to determine a path of movement within at least one pattern with relevant items through which the search path is determined to extend.