Vision-Based MHV Control for Discharge Speed and Position
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Solution Overview
Problem
Existing road-construction machinery operations require skilled operators to monitor and adjust machinery functions and site conditions, including alignment, speed, and unauthorized personnel/objects, which can be distracting and unsafe.
Innovation Solution
A system using machine learning (ML) to analyze visual data from cameras mounted on material-handling vehicles (MHVs) to automatically adjust operational conditions and detect objects/entities, allowing for safe and efficient operation without operator distraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera-based monitoring systems are used to allow operators to remotely view the discharge area, then operator safety is improved by removing laborers from dangerous positions, but operator workload increases as they must simultaneously operate the machine and monitor the video stream
Solution Approach 1:
The system enables automatic monitoring and adjustment of discharge area conditions through computer vision and machine learning algorithms. The processor independently analyzes visual data, detects objects and conditions, and generates adjustment commands without requiring operator attention, allowing the system to monitor itself while the operator focuses on machine operation
Solution Approach 2:
The patent replaces manual visual monitoring by operators with an automated computer vision system. Cameras capture visual data that is processed by machine learning algorithms to detect objects, determine their nature (debris, personnel, equipment), and trigger appropriate responses, substituting human observation with automated optical detection and analysis
2Ease of operation
If operators manually monitor and adjust machinery functions and site conditions, then operational control is maintained, but operator distraction increases and safety decreases
Solution Approach 1:
The system continuously captures visual data from cameras, processes it through machine learning algorithms, and provides real-time feedback about discharge area conditions. The processor generates adjustment commands based on detected objects and conditions, creating a closed-loop feedback system that automatically informs and adjusts operations without requiring continuous operator attention
Solution Approach 2:
The automated vision system performs monitoring and adjustment functions independently, serving itself to detect conditions and generate commands without operator intervention. The system self-manages the monitoring task, allowing operators to focus on machine operation while maintaining safety through automated surveillance
3Adaptability or versatility
If skilled operators are required to simultaneously operate machinery and monitor video streams for adjustments, then operational flexibility is maintained, but system complexity increases and safety is compromised
Solution Approach 1:
The system automates the monitoring and adjustment functions through self-service capabilities. The computer vision system independently performs object detection, classification, and command generation, eliminating the need for operators to divide attention between machine operation and video monitoring. This reduces operational complexity while preserving flexibility through automated decision-making
Data Source
AI summary
A system for modifying an operational condition, i.e., the discharge speed or position, of a material-handling vehicle (MHV). The system includes a computer utilizing a machine learning engine to analyze visual data, a vision system comprising at least one camera mounted to the MHV for generating said visual data, and a controller operatively connected to the MHV. The vision system generates visual data by imaging an observed area proximate the MHV. That data is transmitted to the computer which, using a processor, analyzes the data to determine if a current operational condition of the MHV is outside of a tolerance band. If it is outside of the band, the computer system generates operational commands to bring the MHV back within the tolerance band and transmits said commands to the controller.