Predictive Harvesting Logistics for Synchronized Material Transfer
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Solution Overview
Problem
Agricultural harvesting operations face inefficiencies due to misalignment between the arrival times of harvesters and receiving machines, leading to increased fuel consumption, machine wear, and poor operational quality, as well as interruptions in harvesting due to unsynchronized material transfer.
Innovation Solution
An agricultural harvesting system that generates predictive maps using in-situ data and historical or predicted data to optimize the synchronization of harvester and receiving machine operations, including yield, vegetative index, topographic, soil property, and crop state maps, to control the speed and path planning of both machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester and receiving machine operate independently without synchronization, then operational flexibility is maintained, but material transfer interruptions occur and fuel consumption increases
Solution Approach 1:
The system performs preliminary actions by predicting future positions and arrival times of both harvester and receiving machine before the material transfer event. The predictive model calculates expected locations based on current speeds and paths, allowing the receiving machine to be positioned in advance and the harvester to be guided to the optimal transfer point, thereby preventing interruptions and reducing fuel waste from waiting.
Solution Approach 2:
The system implements feedback by continuously monitoring actual positions of the harvester and receiving machine, comparing them against predicted positions, and adjusting speeds and paths in real-time. This closed-loop control ensures synchronization is maintained despite variations in terrain, machine performance, or environmental conditions, optimizing both productivity and energy efficiency.
2Ease of operation
If the harvester and receiving machine operate independently, then operational simplicity is maintained, but machine wear increases due to unsynchronized operations
Solution Approach 1:
The system enables self-service by allowing the harvester and receiving machine to automatically adjust their own operations based on predictive models. Each machine receives guidance signals that autonomously adjust speed and path to achieve synchronization, eliminating the need for complex manual coordination while reducing wear from idle waiting and repeated positioning adjustments.
3Reliability
If real-time control of harvester and receiving machine is implemented, then material transfer synchronization is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary predictive model that acts as a mediator between the harvester and receiving machine control systems. This model processes inputs from both machines, calculates optimal synchronization parameters, and outputs guidance signals, thereby achieving reliable material transfer synchronization without requiring direct complex real-time control loops between the machines themselves.
Data Source
AI summary
An agricultural harvesting system obtains a yield map that maps yield values to different geographic locations in a worksite and a speed map that maps agricultural harvester speed values to different geographic locations in the worksite. The agricultural harvesting system identifies a geographic location in the worksite at which the agricultural harvester will be full, at least to a threshold level, based on the yield map; identifies a geographic location in the worksite at which a material transfer operation is to start based on the geographic location at which the agricultural harvester will be full, at least to the threshold level; and identifies a time at which the agricultural harvester will arrive at the material transfer location, based on the speed map. The agricultural harvesting system can control one or more of the agricultural harvester and a receiving machine.