Forklift Task Classification for Adaptive Mast and Speed Control
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Solution Overview
Problem
Material handling vehicles lack the ability to autonomously recognize and adapt their behavior to specific tasks, leading to inefficiencies and potential operator errors in industrial settings.
Innovation Solution
A vehicle classification system that uses on-board sensors and a vehicle controller to monitor and identify patterns in data, classify tasks such as loading or unloading trailers, and modify operational parameters like mast height or speed to assist operators or enhance autonomous operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If material handling vehicles use fixed operational parameters, then device complexity is reduced, but adaptability to different tasks deteriorates
Solution Approach 1:
The system dynamically adjusts operational parameters based on detected task patterns. The vehicle transitions from static fixed parameters to dynamic adaptive parameters by monitoring sensor data, identifying task types (loading, unloading, transport), and modifying mast height, speed, and other operational characteristics in real-time to match the detected task requirements
Solution Approach 2:
The vehicle autonomously performs task classification and self-adjustment without external intervention. The onboard sensor system and controller automatically detect operational patterns, classify the current task, and modify operational parameters independently, enabling the system to serve itself in adapting to different working conditions
2Productivity
If material handling vehicles autonomously recognize and adapt to tasks, then operational efficiency is improved, but device complexity increases
Solution Approach 1:
The system implements a closed-loop feedback mechanism where onboard sensors continuously monitor vehicle operations, the controller analyzes the sensor data to detect task patterns, and the system adjusts operational parameters based on this feedback. This continuous monitoring-analyzing-adjusting cycle enables autonomous task recognition and adaptation while improving operational efficiency
Solution Approach 2:
The patent replaces manual task recognition and parameter adjustment with an automated electronic system. Instead of operators manually assessing tasks and adjusting vehicle parameters, the system uses sensor data analysis and electronic control to automatically detect task types and modify operational characteristics, substituting mechanical/manual processes with electronic automation
3Manufacturing precision
If the vehicle modifies operational parameters dynamically, then task completion accuracy is improved, but loss of time in parameter adjustment occurs
Solution Approach 1:
The system performs preliminary task classification by analyzing sensor data patterns before executing parameter adjustments. By detecting task types (loading, unloading, transport) in advance through pattern recognition, the system prepares appropriate operational parameters beforehand, enabling smooth transitions without time-consuming adjustments during task execution
Data Source
AI summary
Systems and methods for material handling vehicle task classification are provided. A method for task characterization on a material handling vehicle comprises monitoring data communicated between one or more on-board sensors and a vehicle controller on the material handling vehicle. The method further comprises identifying a repletion or pattern in the monitored data, and determining that the repetition or pattern in the monitored data is a vehicle task. The method also comprises modifying an operational parameter of the material handling vehicle based on the determined vehicle task.