Predictive Tractive Control Maps for Variable Field Conditions

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Solution Overview

Problem

Existing agricultural machinery control systems suffer from latency issues in sensor readings and machine responses, leading to suboptimal performance in variable field conditions.

Innovation Solution

A system that generates predictive maps using in-situ data and prior or predicted data to proactively control mobile agricultural machines, incorporating topographic, soil property, and prior operation maps to maintain desired performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If sensor readings and machine responses are used in real-time control, then responsiveness to field conditions is improved, but latency causes suboptimal performance in variable conditions

Engineering Contradiction:

Improveresponse speedVSAvoidcontrol latency

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

The system generates predictive maps that forecast future tractive characteristics before the machine actually encounters those conditions. By predicting soil properties, topographic features, and their impact on traction in advance, the control system can prepare optimal settings proactively rather than reactively, eliminating the performance loss caused by sensor latency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The predictive map acts as a cushion against latency by providing advance information about upcoming variations in field conditions. This allows the control system to smooth out transitions and maintain optimal performance even when sensor data arrives with delay, effectively cushioning the impact of time losses.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

2Measurement precision

If predictive maps are generated using multiple data sources, then control accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvetractive characteristic prediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system merges multiple information maps (soil properties, topography, prior operations) into a single integrated predictive map that forecasts tractive characteristics. By combining these diverse data sources through a unified predictive model, the system achieves high prediction accuracy while managing complexity through integration rather than separate processing systems.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The predictive map generator serves multiple functions: it processes soil property data, topographic data, prior operation data, and generates control recommendations. This multi-functional approach consolidates what could be separate complex systems into a single universal platform that handles all predictive mapping tasks.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentEP4260671B1Systems and methods for generating predictive tractive characterisitcs and for predictive tractive control

Publication Date: 2025.11.19 DEERE & CO

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AI summary

An information map is obtained by an agricultural system. The information map maps values of a characteristic at different geographic locations in a worksite. An in-situ sensor detects tractive characteristic values as a mobile agricultural machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive tractive characteristic values at different geographic locations in the worksite based on a relationship between values of the characteristic in the information map and tractive characteristic values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.