Forklift Adaptive Acceleration Using Manual Drive Feedback
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Solution Overview
Problem
Existing materials handling vehicle systems lack the ability to accurately adapt and control acceleration during semi-automated driving operations, leading to potential instability and inefficiency, especially when handling varying loads and conditions.
Innovation Solution
The system monitors and calculates adaptive drive parameters, such as acceleration, during manual operations and uses this data to control semi-automated driving operations, ensuring that the vehicle's acceleration is adjusted based on recent driving behavior and load conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle operates in semi-automated mode with fixed acceleration parameters, then the control system is simple, but the vehicle stability and efficiency deteriorate when handling varying loads and conditions
Solution Approach 1:
The controller monitors vehicle drive parameters (acceleration, speed) during manual operations and uses this feedback to automatically adjust acceleration parameters during semi-automated operations. This closed-loop feedback mechanism enables the system to adapt to varying load conditions and maintain vehicle stability without requiring complex manual intervention.
Solution Approach 2:
The system performs preliminary monitoring and data collection during manual operations before transitioning to semi-automated mode. By pre-processing and storing drive parameter data during manual operation, the system prepares the necessary information in advance to enable stable and efficient automated operation when needed.
2Measurement precision
If the system monitors multiple drive parameters during manual operation, then the control precision improves, but the data processing complexity increases
Solution Approach 1:
The controller segments the monitoring process by focusing on key drive parameters (acceleration, speed) during manual operations. By dividing the complex operational data into distinct, manageable parameter categories, the system achieves precise measurement without overwhelming data processing complexity.
Solution Approach 2:
The system dynamically adjusts which drive parameters are monitored and their weighting based on operational context. During manual operation, the controller selectively monitors parameters that are most relevant to the current driving conditions, optimizing measurement precision while minimizing unnecessary data processing.
3Productivity
If the vehicle uses adaptive acceleration control during semi-automated operations, then the efficiency improves, but the control algorithm complexity increases
Solution Approach 1:
The control system implements dynamic acceleration adjustment by continuously adapting acceleration parameters based on monitored drive data from manual operations. This dynamic approach allows the vehicle to optimize its acceleration profile for varying load conditions and terrain, significantly improving operational efficiency without requiring overly complex algorithms.
Data Source
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AI summary
A method for operating a materials handling vehicle (10) is provided comprising: monitoring, by a controller (103), a first vehicle drive parameter corresponding to a first direction of travel of the vehicle during a first manual operation of the vehicle by an operator and concurrently monitoring, by the controller, a second vehicle drive parameter corresponding to a second direction different from the first direction of travel during the first manual operation of the vehicle by an operator. The controller receives, after the first manual operation of the vehicle, a request to implement a first semi-automated driving operation. Based on the first and second monitored vehicle drive parameters during the first manual operation, the controller controls implementation of the first semi-automated driving operation.