Autonomous Work Machine Route Switching for Complex Work Areas
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Solution Overview
Problem
Autonomous work machines face challenges in setting efficient and accurate routes in work areas with complex shapes and obstacles, leading to reduced work quality and increased processing loads on learning systems.
Innovation Solution
An information processing system that employs a dual route setting method using a neural network and a neural-network extended classifier system to set routes, switching between methods based on work quality and obstacle detection, ensuring high-quality route setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex learning method is used to set routes in intricate work areas, then route setting accuracy is improved, but processing load increases
Solution Approach 1:
The system dynamically switches between the first route setting method (neural network) and the second route setting method (simplified algorithm) based on the complexity of the work area. For simple rectangular areas, the simplified second method is used to reduce processing load. For complex intricate areas, the first method is activated to maintain high route setting accuracy. This dynamic adaptation resolves the contradiction between accuracy and processing load.
Solution Approach 2:
The system changes the parameter of route setting method selection based on work area characteristics. When the work area is determined to be simple (rectangular), the system switches to the second method with lower processing requirements. When the work area is complex (intricate shapes), the system switches to the first method with higher processing capability. This parameter change allows the system to optimize between accuracy and processing load for different scenarios.
2Speed
If a simplified route setting method is used to reduce processing load, then processing speed is improved, but route setting accuracy decreases
Solution Approach 1:
The system dynamically selects the appropriate route setting method based on work area complexity. For simple rectangular work areas, the simplified second method is used, providing fast processing speed. For complex intricate work areas, the system switches to the more accurate first method. This dynamic selection ensures that processing speed is optimized when possible, while accuracy is maintained when required by the complexity of the task.
Solution Approach 2:
The system changes the route setting method parameter based on the characteristics of the work area. When the work area is rectangular and simple, the second method is selected for speed. When the work area becomes complex or intricate, the system switches to the first method to ensure accuracy. This parameter change strategy allows the system to achieve high processing speed for simple tasks while maintaining accuracy for complex tasks.
3Device complexity
If a single route setting method is used for all work areas, then system complexity is reduced, but work quality varies across different area types
Solution Approach 1:
The system dynamically adapts its route setting method based on the type of work area encountered. For rectangular areas, the simplified second method is used. For intricate or complex areas, the system switches to the first method. This dynamic adaptation ensures consistent high work quality across different area types, while the system maintains relatively low complexity by using a simplified method whenever possible and only activating the more complex first method when necessary.
Solution Approach 2:
The system changes the route setting method parameter based on work area classification. When the work area is classified as rectangular, the second method is applied. When classified as intricate or complex, the first method is applied. This parameter change ensures that work quality remains consistent and high across different area types, while the system complexity is kept manageable by using the simpler second method for the majority of simple rectangular areas.
Data Source
AI summary
An information processing system is provided which can increase the possibility that a path along which work quality is high is set in a work area. In an acquisition step of the information processing system, boundary information is acquired, the boundary information indicating the boundary of the work area targeted by a work machine that can travel autonomously. In a setting step, a path of the work machine is set in order to minimize unreached areas from the work area on the basis of the acquired boundary information, and when the work quality in the case of using a path set by a first method does not meet a prescribed criterion, a path is set by a second method.