Soil Image CNN Control for Robust Wire-Free Machine Guidance
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Solution Overview
Problem
Current autonomous soil working machines, such as lawn mowers and harvesters, require external infrastructure like wires or GPS systems for navigation, which are complex, costly, and limited in portability, and existing image processing methods are not robust enough to handle environmental perturbations like shadows, lighting variations, and obstacles.
Innovation Solution
A method using a trained convolutional neural network for image processing that generates a synthetic descriptor of the soil, allowing the machine to autonomously navigate and perform tasks without external infrastructure, by learning soil characteristics and being robust to perturbations through hierarchical representation and convolutional operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external infrastructure like GPS systems or wires is used for navigation, then navigation accuracy and control are improved, but device complexity and cost increase
Solution Approach 1:
The system uses the soil working means itself to capture images and process them autonomously. The machine serves its own navigation needs by using its working equipment (camera, processor) to detect soil characteristics and determine its position and trajectory without external infrastructure.
Solution Approach 2:
The patent replaces mechanical/GPS-based navigation systems with an optical and computational system. Instead of using satellites or physical wires, the system uses image capture and digital processing to achieve navigation and area confinement.
2Ease of manufacture
If traditional image processing methods are used, then implementation simplicity is improved, but robustness to environmental perturbations deteriorates
Solution Approach 1:
The system pre-processes images by converting them to grayscale and applying histogram equalization before feature extraction. These preliminary processing steps enhance the quality of input data and improve the system's ability to handle varying lighting conditions and environmental perturbations.
Solution Approach 2:
The patent introduces intermediate processing steps (grayscale conversion, histogram equalization) as mediators between the raw image capture and the final soil classification. These intermediary operations enhance the robustness of the system by normalizing input data before analysis.
3Reliability
If sophisticated image processing is used to handle perturbations, then reliability is improved, but computational complexity increases
Solution Approach 1:
The image processing is divided into distinct sequential stages: preprocessing (grayscale conversion, histogram equalization), feature extraction (edge detection, contour identification), and classification (soil type determination). This segmentation allows each stage to be optimized independently while maintaining overall system reliability.
Solution Approach 2:
The system applies selective processing operations based on the specific requirements of each processing stage. Not all operations are applied to all images - only the necessary preprocessing and analysis steps are performed, avoiding unnecessary computational overhead while maintaining robustness.
4Adaptability or versatility
If autonomous operation without external infrastructure is implemented, then portability is improved, but navigation reliability deteriorates
Solution Approach 1:
The soil working means is designed to perform multiple functions: soil processing and autonomous navigation. The same equipment (camera, processor) used for soil analysis is also used for navigation and position determination, eliminating the need for separate navigation infrastructure and improving portability.
Solution Approach 2:
The machine determines its own position and navigation trajectory using its own imaging and processing capabilities. It serves its own navigation needs without relying on external GPS infrastructure, achieving both portability and reliable autonomous operation.
Data Source
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AI summary
The invention relates to a method (100) for controlling a soil working means, based on an image processing. Such a soil working means comprises a locomotion member (201) and a working member (202). The method comprises the steps of: acquiring (101) at least one digital image of the soil by means of digital image acquisition means (203) installed on the working means; processing (102), by means of an electronic processing unit (204), the at least one digital image acquired by performing at least one convolution operation on the digital image by means of a trained neural network (300; 400; 500); obtaining (103), by means of the electronic processing unit, at least one synthetic soil descriptor based on such a processing; - generating (104), by means of the electronic processing unit, at least one control signal of the locomotion member or of the working member based on the synthetic soil descriptor.