Forklift Adaptive Acceleration Using Orientation-Specific Driver Data
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Solution Overview
Problem
Existing materials handling vehicles lack adaptive acceleration control, leading to suboptimal performance in semi-automated driving modes due to fixed, predefined acceleration limits that do not account for varying load stability and operator driving behavior.
Innovation Solution
A system that monitors and calculates adaptive acceleration parameters based on the operator's recent manual driving behavior, distinguishing between different vehicle orientations to optimize semi-automated driving operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed, predefined acceleration limits are used in materials handling vehicles, then the control system is simple and reliable, but the performance in semi-automated driving modes is suboptimal and does not adapt to varying load stability and operator driving behavior
Solution Approach 1:
The system performs preliminary monitoring and data collection during manual operation phases, storing acceleration data in memory before semi-automated mode begins. This allows the system to pre-analyze operator driving behavior and load characteristics, then apply this knowledge automatically when transitioning to semi-automated mode, achieving adaptability without real-time complexity
Solution Approach 2:
The system uses the operator's own manual driving behavior data to automatically configure optimal acceleration parameters for semi-automated mode. By monitoring and analyzing the operator's natural driving patterns during manual operation, the system self-adjusts acceleration limits to match the operator's style and the specific load characteristics, eliminating the need for external configuration or complex real-time adjustments
2Measurement precision
If acceleration data from all vehicle orientations is used to calculate maximum acceleration, then more data is available for calculation, but the accuracy decreases because different orientations have different load stability characteristics
Solution Approach 1:
The system segments acceleration data by vehicle orientation, creating separate datasets for different orientation ranges (e.g., 0-90 degrees, 90-180 degrees). This segmentation allows the system to calculate orientation-specific maximum acceleration values that reflect the actual load stability characteristics for each orientation, improving measurement precision by excluding irrelevant data from other orientations
Solution Approach 2:
The system applies local quality by tailoring the acceleration calculation to the specific orientation context. Instead of using a uniform approach for all orientations, the system adjusts the data selection criteria based on the current orientation, ensuring that only acceleration data from comparable orientation scenarios is used, thereby improving the local accuracy of acceleration predictions for each specific orientation
Data Source
AI summary
A method is provided for operating a materials handling vehicle comprising: monitoring, by a processor, vehicle acceleration in a direction of travel of the vehicle during a manual operation by an operator of the vehicle when the vehicle is traveling in a first vehicle orientation; collecting and storing, by the processor, data related to the monitored vehicle acceleration; receiving, by the processor, a request to implement a semi-automated driving operation; calculating, by the processor, a maximum vehicle acceleration based on acceleration data comprising the stored data, wherein the data related to the monitored vehicle acceleration used in calculating the maximum vehicle acceleration comprises only the vehicle acceleration data in the direction of travel of the vehicle collected when the vehicle is traveling in the first vehicle orientation. Based at least in part on the maximum vehicle acceleration, controlling, by the processor, implementation of the semi-automated driving operation.