Cut Location Optimization Controller for Earthmoving Equipment
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Solution Overview
Problem
Current systems for optimizing material movement operations in machines like dozers and wheel loaders lack an efficient method to determine the lowest cost cut locations and paths, which affects the overall cost-effectiveness of material moving processes.
Innovation Solution
A system and method that utilize a position sensor and controller to determine the actual profile of the work surface, calculate target profiles based on cut locations, loading profiles, and slot parameters, and select the lowest cost target profile to define the lowest cost cut location or set of implement paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional planning systems are used to determine machine paths, then the system complexity is low, but the cost-effectiveness of material movement operations is insufficient
Solution Approach 1:
The system performs preliminary calculations to determine optimal cut locations and target profiles before actual material movement operations begin. The controller pre-processes work surface profile data, evaluates multiple potential paths, and identifies the lowest cost option in advance, allowing the machine to follow pre-optimized trajectories without real-time computational overhead.
Solution Approach 2:
The system creates a digital representation (copy) of the work surface profile using position sensor data, then performs virtual simulations of different cut locations and paths on this digital model. This allows cost evaluation of multiple scenarios without physically executing them, enabling optimal path selection before actual operations.
2Ease of manufacture
If multiple target profiles are evaluated to find the lowest cost path, then the cost-effectiveness improves, but the computational time and processing complexity increase
Solution Approach 1:
The evaluation process is segmented into discrete steps: (1) receiving work surface profile data, (2) generating multiple candidate target profiles based on different cut locations, (3) calculating costs for each profile separately, (4) comparing results, and (5) selecting the optimal path. This segmentation allows systematic evaluation without overwhelming computational burden.
Solution Approach 2:
The system varies key parameters such as cut location positions and target profile geometries across multiple evaluation iterations. By systematically changing these parameters and calculating corresponding costs, the system identifies the parameter combination that yields the lowest cost path without requiring exhaustive search of all possible configurations.
3Productivity
If autonomous operation is implemented to improve productivity, then consistent productivity is achieved, but the system complexity and initial cost increase
Solution Approach 1:
The machine performs self-guidance by using its own position sensor data to determine work surface profiles and calculate optimal paths. The controller automatically processes sensor inputs, evaluates multiple paths, and generates control commands without external intervention, enabling autonomous operation with consistent productivity.
Solution Approach 2:
The system continuously receives feedback from position sensors regarding the actual work surface profile and machine location. This feedback is fed back into the path optimization algorithm, allowing the system to adjust and refine path selections based on real conditions while maintaining autonomous operation.
Data Source
AI summary
A system for determining a cut location at a work surface includes a position sensor and a controller. The controller stores a final design plane of the work surface and determines an actual profile of the work surface. A plurality of target profiles extending along a path are determined, each corresponding to a cut location. The target profiles are based at least in part upon the cut location, a loading profile, slot parameters, and the actual profile of the work surface. The controller is further configured to determine a lowest cost target profile and the lowest cost target profile defines an optimized cut location. A method is also provided.