Occupancy Grid Free Space Detection in Cluttered Robot Environments
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Solution Overview
Problem
Conventional robotic path planning systems in cluttered environments face inaccuracies in free space detection due to the use of occupancy grid maps, which incorrectly mark unit geometric grids as occupied even if only partially covered by obstacles, leading to missed free space for robotic movement.
Innovation Solution
A processor-implemented method that generates an occupancy grid map with bounding boxes around obstacles, merges unit grids into unified segments, identifies their centers as seeds, and performs convex expansion to accurately identify free space by expanding regions until they touch segment edges or obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If unit geometric grids are marked as occupied when vertices of obstacles are present, then obstacle detection is simplified, but free space detection accuracy deteriorates
Solution Approach 1:
The environment is divided into unit geometric grids, and obstacle bounding boxes are segmented to identify which grids are actually occupied. This segmentation allows precise marking of only those grids that are truly occupied by obstacles, rather than using a simplified vertex-based approach that marks too many grids as occupied.
Solution Approach 2:
Before performing path planning, the system pre-processes the occupancy grid map by accurately determining which unit grids are occupied by obstacles using bounding box intersection analysis. This preliminary accurate marking of occupied grids ensures that subsequent path planning algorithms work with correct free space information, avoiding the need for complex real-time corrections.
2Reliability
If unit geometric grids partially occupied by obstacles are marked as occupied, then obstacle coverage is ensured, but available free space for robotic movement is reduced
Solution Approach 1:
The system applies different marking rules to different regions of the occupancy grid based on local occupancy conditions. Unit grids are marked as occupied only when they are actually intersected by obstacle bounding boxes, rather than applying a uniform marking rule. This local quality approach ensures that partially occupied grids are correctly identified, maximizing the available free space while maintaining reliable obstacle detection.
3Ease of manufacture
If conventional occupancy grid marking methods are used, then implementation is simple, but incorrect free space data is produced
Solution Approach 1:
The system replaces the conventional simple vertex-based marking mechanism with a more sophisticated bounding box intersection analysis mechanism. Instead of merely checking if vertices fall within grids, the system calculates the actual intersection between obstacle bounding boxes and unit grids, substituting a simple mechanical rule with a more accurate geometric analysis to preserve free space information.
Data Source
AI summary
Robots are used extensively in different applications so as to perform specific tasks. However, the robots are required to move around in a location where they are present, so as to perform the tasks. For path planning, free space identification is performed by the robots during which obstacles are detected and free space is identified. However, the existing systems for path planning struggle to identify free space in cluttered environments. The disclosure herein generally relates to robotic path planning, and, more particularly, to a method and system for free space detection in a cluttered environment for robotic path planning. The system inscribes obstacles in bounding boxes and all unit grids inscribed by the bounding boxes are considered as occupied. Further, by seeding the occupancy grid map, the system identifies unified segments and corresponding seeds. Further a convex expansion is executed in the occupancy grid map to detect the free space.