Robotic Surface Processing via Segmented Property Classification
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Solution Overview
Problem
Existing methods for processing surfaces with robotic vehicles, such as lawn mowers, are inefficient due to random movement strategies that result in prolonged processing times and increased costs associated with high-precision localization systems.
Innovation Solution
A method that divides the surface into individual segments based on property classes, using random divisions to generate segments that can be processed easily, with predefined or random movement paths depending on the segment type, and employing sensors to detect boundaries and obstacles for optimal navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a random movement strategy is used for processing the surface, then the robotic vehicle can navigate without complex localization systems, but the processing time increases significantly and surfaces are processed multiple times
Solution Approach 1:
The surface is divided into multiple segments with different property classes (A, B, C) based on processing efficiency characteristics. The controller generates a processing strategy that sequences these segments to optimize overall processing time. This segmentation allows the system to use simple random navigation while still achieving efficient processing by strategically ordering segment visits.
Solution Approach 2:
The controller pre-calculates the processing strategy by determining the optimal sequence of segments before the robotic vehicle begins processing. This preliminary action includes classifying segments by property class and arranging them in a sequence that minimizes total processing time, allowing the vehicle to execute simple random movements within each segment while maintaining high overall productivity.
2Productivity
If the surface is divided into many small segments, then the processing strategy can be optimized, but the calculation time and computational resources increase
Solution Approach 1:
The controller classifies segments into discrete property classes (A, B, C) based on processing efficiency parameters. By transforming continuous surface characteristics into discrete categories, the system reduces computational complexity while still enabling effective optimization of the processing sequence. This parameter change allows efficient calculation of optimal strategies without requiring excessive computational resources.
3Measurement precision
If high-precision localization systems are used, then the robotic vehicle can navigate accurately, but the costs increase significantly
Solution Approach 1:
The system replaces expensive high-precision localization systems with a combination of simple sensors and intelligent software processing. The robotic vehicle uses basic sensors to detect boundaries and obstacles, while the controller compensates for lower navigation precision by calculating optimal processing sequences that account for the vehicle's actual position and movement capabilities. This approach achieves effective processing without the high costs associated with precision localization hardware.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for quicker and more effective surface processing, reducing time and costs by optimizing the processing strategy based on segment characteristics and using affordable navigation means.
Implementation Method 1
the sensors comprising at least one infrared sensor which detects the intensity of infrared radiation reflected from the subsoil
Data Source
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AI summary
The invention relates to a method for processing a surface (100) by means of a robotic vehicle (10), wherein the robotic vehicle (10) has a control system (15) in which data concerning the outline of the surface (100) to be processed are stored, wherein locating means (20) are present, which determine the position of the robotic vehicle (10), in particular in relation to the surface (100) to be processed, and wherein the method comprises the following steps: dividing the surface (100) to be processed into individual segments (61a to 64a; 61b to 64b); classifying each individual segment (61a to 64a; 61b to 64b) into a property class (A, B); and moving to and processing each individual segment (61a to 64a; 61b to 64b) in succession, each individual segment (61a to 64a; 61b to 64b) being processed with a processing strategy corresponding to its property class (A, B).