Work Machine Control Using Capability-Based Assignment Likelihood
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Solution Overview
Problem
Existing work machine control systems often fail to respond efficiently to unexpected conditions on a worksite, leading to inefficient operation due to their reliance on a priori data and rigid assignment planning, which lacks adaptability to dynamic work environments.
Innovation Solution
A control system for mobile work machines that dynamically senses work location conditions and adjusts machine settings or assignment criteria based on the machine's capabilities, using a likelihood metric to determine the feasibility of meeting assignment criteria, allowing for real-time adjustments and improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a priori data and rigid assignment planning are used, then system complexity is reduced, but adaptability to dynamic work environments deteriorates
Solution Approach 1:
The control system transitions from static a priori planning to dynamic real-time control by continuously sensing worksite conditions and adjusting machine settings on-the-fly. The system evaluates likelihood metrics dynamically and modifies assignment criteria during operation, making the control approach adaptive to changing environmental conditions while maintaining manageable complexity through structured decision frameworks.
Solution Approach 2:
The system changes operational parameters by adjusting machine settings based on real-time worksite conditions. Instead of fixed parameters from a priori planning, the control system modifies parameters dynamically by evaluating likelihood metrics and selecting optimal settings from available machine capabilities, enabling adaptability without requiring complete system redesign.
2Adaptability or versatility
If real-time sensing and dynamic adjustment are implemented, then adaptability to unexpected conditions is improved, but device complexity increases
Solution Approach 1:
The control system implements feedback by continuously sensing worksite conditions and using this information to adjust machine settings in real-time. The system evaluates likelihood metrics based on sensed conditions and feeds this information back into the control decision-making process, enabling adaptive response to unexpected conditions while maintaining structured complexity through defined feedback loops and decision criteria.
Solution Approach 2:
The system performs self-service by autonomously evaluating its own capabilities against worksite conditions and automatically adjusting settings without external intervention. The control system independently determines whether assignment criteria can be met based on current machine capabilities and environmental conditions, making self-directed decisions to optimize performance while managing its own operational complexity.
3Productivity
If likelihood metric evaluation is performed, then productivity through efficient operation is improved, but loss of time for evaluation and comparison is incurred
Solution Approach 1:
The system performs preliminary action by pre-evaluating machine capabilities and storing available settings before actual work execution. The likelihood metric evaluation uses pre-characterized machine capabilities and predefined assignment criteria, allowing rapid assessment during operation without extensive real-time computation, thus improving productivity while minimizing evaluation time.
Data Source
AI summary
A method of controlling a work machine on a worksite includes receiving an indication of a work machine assignment having a worksite location and corresponding assignment criteria associated with completion of the work machine assignment, receiving a set of worksite conditions at the location, and identifying a set of machine capabilities, each machine capability corresponding to operation of a controllable subsystem on the work machine. The method includes generating a likelihood metric indicative of a likelihood that the assignment criteria will be met based on the set of worksite conditions and the set of machine capabilities, comparing the likelihood metric to a threshold, and generating a control signal that controls the work machine based on the comparison.