Vehicle Path Generation for Obstacle-Adaptive Factory Routing
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Solution Overview
Problem
Existing vehicle systems in manufacturing environments face challenges in efficiently navigating complex production systems with varying obstacles and load changes, particularly in supporting and transporting large or heavy products, without manual intervention.
Innovation Solution
A vehicle system equipped with a pathing generation system that retrieves a production system floorplan, receives input on current position, locations, and obstacles, and generates optimal routes for the vehicle using processors and memory devices, allowing for autonomous navigation and load handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for vehicle navigation in production systems, then the vehicle can handle complex obstacles and load changes, but productivity decreases and manual labor increases
Solution Approach 1:
The vehicle system performs self-navigation by automatically retrieving floorplan data, determining its current position, identifying obstacles and load changes, and generating optimal routes without human intervention. The system serves itself by integrating sensors, processors, and communication interfaces that enable autonomous decision-making and path planning throughout the production environment.
2Extent of automation
If autonomous navigation is implemented without manual intervention, then productivity increases, but the system must handle complex obstacles and load changes autonomously
Solution Approach 1:
The navigation system is designed as a universal multi-functional platform that can handle various production environments, obstacle types, and load configurations through a single integrated architecture. The system retrieves floorplan data, processes sensor information, generates routes, and adapts to different scenarios using common hardware and software components, eliminating the need for multiple specialized systems.
Solution Approach 2:
The autonomous navigation system is divided into functional modules: floorplan data retrieval, current position determination, obstacle detection, load change identification, route generation, and execution control. Each module performs a specific function and communicates with others through standardized interfaces, making the complex system manageable, maintainable, and scalable.
3Measurement precision
If the vehicle system uses detailed floorplan data and multiple inputs for route generation, then navigation accuracy improves, but information processing time and computational load increase
Solution Approach 1:
The system retrieves and processes floorplan data in advance before the vehicle needs to navigate. By having the production system floorplan预先 available in the vehicle's memory or accessible through communication interfaces, the system eliminates the need for real-time environmental scanning and mapping, significantly reducing computational time during actual navigation while maintaining high position accuracy.
Data Source
AI summary
A vehicle system includes a vehicle, one or more input devices, and one or more memory devices storing instructions thereon. When executed by one or more processors, the instructions cause the one or more processors to retrieve, from the one or more memory devices, a floorplan of a production system, receive a current position of the vehicle, and receive, from the one or more input devices, one or more inputs. The one or more inputs include one or more of one or more locations in the production system, a footprint of the vehicle, or one or more obstacles. The instructions also cause the one or more processors to generate a route for the vehicle from the current position of the vehicle to the one or more locations based on the one or more inputs.