Monocular Vision Shake-Point Localization for Autonomous Harvesting
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Solution Overview
Problem
Orchard harvesting technologies face challenges such as irregular tree spacing, varying tree sizes, complex canopy structures, low connectivity, and dusty environments, which hinder the integration of advanced automation technologies, leading to inefficient and labor-intensive harvesting methods with high fruit and tree damage.
Innovation Solution
An autonomous agricultural vehicle equipped with a chassis, identification system, shaker system, and control system that captures images, identifies shake points on tree trunks, positions itself for harvesting, and shakes the trees to dislodge fruit, using advanced sensing and decision-making capabilities to adapt to orchard conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labor and basic mechanical equipment are used for tree shaking, then the system is simple and easy to operate, but harvesting efficiency is low and labor intensity is high
Solution Approach 1:
The patent replaces manual mechanical tree shaking with an automated robotic system equipped with image sensors, processors, and controlled actuators. The system uses visual recognition to identify trees and shake points, then automatically positions and actuates the shaking mechanism, eliminating manual labor while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The autonomous harvester system performs self-navigation and self-operation by using onboard image sensors to detect trees and shake points, processing visual data to determine optimal shaking positions, and automatically executing the shaking action without external human intervention. The system serves itself by making real-time decisions based on environmental perception.
2Object-affected harmful factors
If basic mechanical tree shakers are used, then the equipment structure is simple, but fruit and tree damage occurs due to lack of precision
Solution Approach 1:
The patent replaces imprecise mechanical positioning with an automated vision-based localization system. Image sensors capture visual data of trees and their shake points, processors analyze the images to precisely determine shake point locations, and actuators execute precise movements to target these identified points, thereby minimizing damage to both fruit and trees through accurate positioning.
3Productivity
If advanced automation technologies are integrated into orchard harvesting, then harvesting efficiency improves, but the system becomes vulnerable to environmental factors like dust and low connectivity
Solution Approach 1:
The patent employs protective enclosures and filtering systems around image sensors and electronic components to shield them from dust and debris in the orchard environment. The system includes protective housings that allow optical access while preventing direct contact with particulate matter, maintaining sensor functionality and system reliability despite challenging environmental conditions.
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
Enhances harvesting efficiency, reduces labor dependency, and minimizes fruit and tree damage by implementing precise fruit harvesting and navigation in orchards with advanced sensory and navigational technology.
Implementation Method 1
a vibration generation system configured to generate vibrational energy that the shaker imparts to the tree
Data Source
AI summary
An autonomous harvesting machine for orchard operating environments is described. The autonomous harvesting machine uses machine vision techniques to identify and triangulate features in the operating environment using a stream of monocular images. For instance, the harvesting machine identifies and localizes a shake point of a tree by projecting virtual rays from the pose of the identification system to the identified emergence point feature. To harvest the fruit of trees in the orchard, the harvesting machine shakes the tree at the identified shake point. Additionally, the harvesting machine autonomously navigates through the orchard using a combination high resolution spatial information based on localized features and low resolution spatial information from accessed satellite images.