3D Robotic Tool Cell Mapping for Faster Obstacle-Aware Path Planning
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Solution Overview
Problem
Existing robotic systems face challenges in performing relatively straightforward tasks such as obstacle avoidance, path generation, and adapting to differences between actual objects and CAD models due to processor-intensive algorithms and the complexity of processing point clouds.
Innovation Solution
A robotic tool system that utilizes a scanner to capture spatial data, allocates it to a cellular space, determines characteristics of cells based on adjacent data, and configures tools to operate on objects efficiently, using cellular space processing to simplify obstacle detection and path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If edge detection algorithms are used to detect edges of items and obstacles from point cloud data, then obstacle avoidance capability is improved, but processing time and computational load increase significantly
Solution Approach 1:
The patent segments the continuous point cloud data into discrete cellular space units. Each cell is independently processed and characterized, transforming the complex continuous space analysis into simpler discrete cell evaluation. This segmentation enables parallel processing of multiple cells simultaneously, reducing overall processing time while maintaining edge detection capability through neighborhood pattern comparison.
Solution Approach 2:
The patent introduces cellular space as an intermediary representation between the raw point cloud data and the edge detection process. Instead of directly analyzing point cloud edges, the system first converts spatial data into a cellular grid structure, then performs simplified neighborhood comparisons on this intermediate representation, significantly reducing computational complexity.
2Ease of operation
If path algorithms are used to calculate optimum paths while avoiding obstacles, then navigation capability is improved, but computational load and processing time increase
Solution Approach 1:
The patent performs preliminary processing by converting spatial data into cellular space representation and pre-calculating neighborhood patterns for all cells before path planning begins. This preliminary structuring of the environment data enables faster query operations during actual pathfinding, reducing real-time computational energy requirements while maintaining navigation accuracy.
3Measurement precision
If point cloud data is processed directly to generate spatial information, then measurement precision is maintained, but device complexity and processing requirements increase
Solution Approach 1:
The patent changes the parameter representation of spatial data by transforming continuous point cloud coordinates into discrete cellular space indices. This parameter transformation simplifies the data structure from unstructured point clouds to organized grid cells with defined neighborhood relationships, reducing processing complexity while preserving essential spatial information through cell-centered and neighborhood-based analysis.
Data Source
AI summary
3D modelling and robotic tool systems and methods are provided that enable more efficient spatial processing of an object and its surroundings. The robotic tool system comprises: a scanner, configured to capture spatial data of an object and its surroundings; a processor, configured to: allocate the spatial data to cells of a cellular space; determine one or more characteristics of a cell according to data of adjacent cells; and configure a robotic tool to operate on the object at least in part according to the one or more characteristics.