Walnut Picking Robot Navigation With Multi-Sensor Fusion Mapping
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Solution Overview
Problem
Existing automated agricultural picking technologies face inefficiencies and low accuracy due to reliance on two-dimensional images, limited single-tree operation capabilities, and high initial and maintenance costs associated with guide line installations.
Innovation Solution
An automated walnut picking method utilizing multi-sensor fusion technology, combining two-dimensional laser sensors, Simultaneous Localization and Mapping (SLAM), and image recognition to create offline maps, predict walnut maturity, and guide robots for precise trunk location and walnut collection without guide lines, leveraging 2D and 3D sensors for accurate positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If two-dimensional image sensors are used for fruit positioning, then the device complexity is reduced, but the positioning accuracy deteriorates
Solution Approach 1:
The patent combines two-dimensional image sensors with three-dimensional depth sensors to create a multi-sensor fusion system. The 2D image sensor captures color and texture information while the 3D depth sensor provides distance and spatial information, merging their data to achieve accurate three-dimensional positioning of fruits without excessive system complexity
Solution Approach 2:
The patent transitions from two-dimensional image-only sensing to three-dimensional positioning by introducing depth information through time-of-flight sensors and stereo vision. This dimensional enhancement allows the system to accurately locate fruits in 3D space, solving the positioning accuracy problem while maintaining reasonable device complexity
2Device complexity
If single fruit tree operation is implemented, then the device complexity is reduced, but the productivity deteriorates
Solution Approach 1:
The patent designs a picking robot with universal capabilities that can operate on multiple fruit trees simultaneously or sequentially. The robot equipped with multi-sensor fusion and 3D positioning can identify and pick fruits across different trees within its workspace, enhancing productivity while maintaining manageable system complexity through modular design
3Measurement precision
If guide lines are installed for vehicle positioning, then the positioning accuracy is improved, but the ease of manufacture and maintenance deteriorates
Solution Approach 1:
The patent implements Simultaneous Localization and Mapping (SLAM) technology that enables the guide vehicle to automatically build maps and locate itself without external guide lines. The system uses laser sensors and image sensors to autonomously navigate and position, eliminating the need for manual guide line installation and reducing maintenance requirements
Solution Approach 2:
The patent replaces the mechanical guide line system with an optical and computational approach using laser sensors, image sensors, and SLAM algorithms. This substitution eliminates physical guide lines entirely, improving ease of manufacture and maintenance while maintaining or enhancing positioning accuracy through software-based navigation
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 significantly enhances picking accuracy and efficiency by enabling multi-dimensional judgment and automatic navigation, reducing maintenance needs, and ensuring high automation and operation efficiency without initial guide line installations.
Implementation Method 1
collecting laser data of the entire park through a two-dimensional laser sensor arranged on the guide vehicle
Implementation Method 2
obtaining depth information of a corresponding trunk recognized by the image sensor through a three-dimensional depth sensor
Data Source
AI summary
Disclosed is an automated walnut picking and collection method based on multi-sensor fusion technology, including: operation 1.1: when a guide vehicle for automated picking and collection is started, performing path planning for the guide vehicle; operation 1.2: remotely controlling the guide vehicle to move in a park according to a first predetermined rule, and collecting laser data of the entire park; operation 1.3: constructing a two-dimensional offline map; operation 1.4: marking a picking road point on the two-dimensional offline map; operation 2.1: performing system initialization; operation 2.2: obtaining a queue to be collected; operation 2.3: determining and sending, by the automated picking system, a picking task; operation 2.4: arriving, by the picking robot, at picking target points in sequence; operation 2.5: completing a walnut shaking and falling operation; and operation 2.6: collecting shaken walnuts. The provided method can obtain high-precision fruit coordinates and complete autonomous harvesting precisely and efficiently.