Autonomous Weed Targeting with Machine-Learning Offset Compensation

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Solution Overview

Problem

Autonomous systems struggle to accurately identify and manipulate objects in unpredictable agricultural environments, leading to inefficiencies and environmental impacts from traditional weed control methods like manual labor and chemical herbicides.

Innovation Solution

An autonomous weed eradication system using a prediction system and targeting system to generate a speculative position prediction by applying an offset learned via a machine learning model, allowing for accurate targeting and manipulation of objects like weeds, reducing latency and environmental impact.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional weed control methods (manual labor or chemical herbicides) are used, then weed elimination is achieved, but labor efficiency decreases and environmental harm increases

Engineering Contradiction:

Improveweed elimination efficiencyVSAvoidenvironmental harm from chemical herbicides

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The patent replaces chemical herbicide systems with a mechanical/physical system consisting of computer vision (cameras), machine learning prediction models, and precision actuators that deliver mechanical disturbances (water jets, air blasts, or physical impacts) to selectively eliminate weeds without chemical contaminants

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The autonomous vehicle performs weed elimination independently using onboard sensors and prediction systems to identify targets and execute elimination actions without human intervention, thereby increasing productivity while eliminating the need for manual labor

Inventive Principle:

Principle #25Self-service

2Speed

If autonomous systems use prediction systems to reduce latency in targeting, then response speed improves, but positioning accuracy may deteriorate

Engineering Contradiction:

Improvetargeting response speedVSAvoidobject position accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The prediction system performs preliminary calculations to forecast future weed positions based on current motion data and historical trajectories. By predicting where weeds will be when the elimination action occurs, the system compensates for latency in detection and actuation, maintaining both speed and accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously compares predicted positions with actual detected positions, using the differences (errors) to refine and update prediction models in real-time. This feedback loop ensures that prediction accuracy improves over time while maintaining fast response speeds

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250239060A1Targeting and manipulating objects of interest

Publication Date: 2025.07.24 CARBON AUTONOMOUS ROBOTIC SYST INC

AI summary

Systems and methods for reducing latency of targeting and manipulating of an object of interest is provided. The method includes receiving an image of the object of interest, generating a predicted location of the object of interest based on the received image, applying an offset learned by a machine learning model, the offset representing a difference between a prediction system and a targeting system, causing an adjustment to an implement, and targeting the object of interest with the adjusted implement after the offset is applied.