Driver-Support Mapping for Mixed Material-Transport Fleets
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Solution Overview
Problem
Human-operated material-transport vehicles in industrial settings lack advanced automation features like navigation in tight spaces, payload manipulation, and collision avoidance, and upgrading entire fleets is costly, necessitating integration with newer driverless systems.
Innovation Solution
A driver-support system equipped with sensors and a processor for navigation, path planning, collision avoidance, and communication with a fleet-management system, enhancing the capabilities of human-operated vehicles while allowing coexistence with driverless vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If driverless material-transport vehicles are used to increase automation, then the extent of automation is improved, but the device complexity increases and cost increases
Solution Approach 1:
The system divides the fleet into autonomous vehicles and human-operated vehicles, each serving different functions. The autonomous vehicles handle standardized transport tasks while human-operated vehicles handle complex tasks requiring judgment, creating a segmented division of labor that balances automation with human capability without requiring complete fleet replacement.
Solution Approach 2:
The fleet-management system serves as a universal platform that coordinates both autonomous and human-operated vehicles, enabling the mixed fleet to function as an integrated system. This multi-functionality allows the facility to leverage both automation and human operation without requiring separate management systems.
2Reliability
If driverless material-transport vehicles are deployed to improve safety and navigation, then collision avoidance and tight-space navigation are improved, but the device complexity and cost increase
Solution Approach 1:
The system segments navigation capabilities by equipping only autonomous vehicles with advanced sensors and path-planning systems, while human-operated vehicles maintain simpler systems. This segmentation provides advanced safety and navigation where automation is present without unnecessarily complicating human-operated vehicles.
Solution Approach 2:
The fleet-management system acts as an intermediary that receives data from both autonomous and human-operated vehicles, processes information about facility conditions, and provides guidance to human operators. This mediator enables advanced navigation and collision avoidance capabilities across the entire fleet without requiring every vehicle to have complex autonomous systems.
3Device complexity
If traditional human-operated vehicles are used to maintain simplicity and lower cost, then device complexity is reduced, but productivity and operational efficiency decrease
Solution Approach 1:
The fleet-management system implements continuous feedback loops that monitor the locations, tasks, and statuses of all vehicles in real-time. This feedback enables dynamic task assignment and route optimization, allowing human-operated vehicles to operate more efficiently by receiving real-time guidance and coordinating with autonomous vehicles, thereby improving overall productivity without increasing vehicle complexity.
4Adaptability or versatility
If a mixed fleet of autonomous and human-operated vehicles is maintained to balance cost and capability, then adaptability is improved, but the difficulty of detecting and measuring fleet status increases
Solution Approach 1:
The fleet-management system serves as a universal monitoring platform that handles both autonomous and human-operated vehicles through a unified interface. It collects data from autonomous vehicles via direct communication and from human-operated vehicles through operator input devices, providing a comprehensive view of the entire mixed fleet's status without requiring separate monitoring systems.
Data Source
AI summary
There is provided a driver-support system for use with a human-operated material-transport vehicle, and methods for using the same. The system has at least one sensor, a human-vehicle interface, and a transceiver for communicating with a fleet-management system. The system also has a processor that is configured to provide a mapping application and a localization application based on information received from the sensor. The mapping application and localization application may be provided in a single localization-and-mapping (“SLAM”) application, which may obtain input from the sensor, for example, when the sensor is an optical sensor such as a LiDAR or video camera.