Autonomous Work Vehicle Route Registration With AI Task Points
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Solution Overview
Problem
Conventional systems for controlling autonomous vehicles require extensive setup and calibration time, and the routing process is cumbersome, limiting their efficiency and flexibility.
Innovation Solution
A method for registering routes that allows autonomous vehicles to perform various tasks by selecting and editing task points, including geographical locations, with options for stopping or passing through these points, and using artificial intelligence for route registration and object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems are used for controlling autonomous vehicles, then the vehicles can perform tasks, but extensive setup and calibration time is required
Solution Approach 1:
The system enables autonomous vehicles to register their own routes and perform tasks independently. The vehicle captures images, detects objects, determines task points, and registers routes without requiring extensive manual setup or calibration by operators, thereby reducing setup time while maintaining ease of operation
Solution Approach 2:
The system performs preliminary actions by automatically capturing images and detecting objects before the actual task execution. This preliminary processing enables the vehicle to pre-determine task points and register routes in advance, reducing the time needed for setup and calibration while maintaining operational simplicity
2Productivity
If conventional routing systems are used, then vehicles can follow registered routes, but the routing process is cumbersome and requires vehicle-specific registration
Solution Approach 1:
The system creates a universal routing mechanism where routes registered by one vehicle can be used by multiple vehicles for different tasks. The route registration system is not vehicle-specific but rather creates a shared database of routes that can be universally applied, thereby improving routing efficiency and reducing programming complexity
Solution Approach 2:
The system allows copying of routes and task points between different vehicles. Instead of programming each vehicle separately, the system enables copying of registered routes and adapting them to different vehicles, which simplifies the programming process and improves routing efficiency
3Adaptability or versatility
If autonomous vehicles are used, then tasks can be performed automatically, but the system lacks flexibility in task selection at route points
Solution Approach 1:
The system introduces dynamic task selection capabilities where the autonomous vehicle can adapt its behavior at task points based on real-time conditions. The vehicle can dynamically choose to perform a task, skip it, or modify the task based on current situational requirements, thereby enhancing flexibility while maintaining autonomous operation
Solution Approach 2:
The system allows changing parameters of task execution at route points. The vehicle can modify task parameters such as whether to stop, what task to perform, or how to execute the task based on detected conditions, enabling flexible adaptation while preserving the autonomous operation framework
Data Source
AI summary
A method includes selecting a registered route of a work vehicle system, the registered route including one or more task points each of which including a geographical location, and selecting a particular agricultural task, from among a plurality of agricultural tasks, that a work vehicle can perform at the one or more task points when the work vehicle follows the registered route.