Autonomous Forklift Navigation Without Pre-Mapped Site Surveys
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Solution Overview
Problem
Existing forklift systems require extensive site surveys and detailed maps for installation, and rely on complex IT infrastructure, leading to inefficiencies and potential errors in autonomous operation.
Innovation Solution
A stand-up counterbalanced three-wheeled forklift design with integrated sensors and cameras, coupled with a control system that uses machine learning models for navigation and pallet handling, allowing for rapid installation and efficient autonomous operation without extensive site preparation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive site surveys and detailed maps are conducted for forklift installation, then navigation accuracy is improved, but installation time and complexity increase
Solution Approach 1:
The system performs preliminary scanning of the environment to automatically generate maps and identify navigation features before forklift operation begins. This preliminary mapping action eliminates the need for extensive manual site surveys, reducing installation time while maintaining navigation accuracy through pre-processed environmental data
Solution Approach 2:
The patent replaces manual mechanical surveying methods with automated optical sensing systems (cameras, LiDAR) and computational algorithms. The mechanical process of manual measurement and mapping is substituted by electronic environment scanning and digital map generation, dramatically reducing installation time while improving precision
2Extent of automation
If complex IT infrastructure is deployed for automated forklift operation, then automation capability is improved, but system complexity and cost increase
Solution Approach 1:
The forklift system performs self-navigation, self-localization, and self-operation using onboard sensors and processing units. The system serves itself by autonomously generating maps, identifying navigation features, and executing tasks without requiring complex external IT infrastructure, thereby reducing system complexity while maintaining high automation capability
Solution Approach 2:
The onboard computer system performs multiple functions including environment scanning, map generation, feature identification, navigation calculation, and task execution. This multi-functional integration eliminates the need for separate specialized systems, reducing overall system complexity while achieving comprehensive automation
3Reliability
If pre-mapped environments are used for navigation, then navigation reliability is improved, but adaptability to new environments decreases
Solution Approach 1:
The system dynamically adapts to different environments by performing real-time environment scanning and generating new maps as needed. Rather than relying on static pre-mapped environments, the system creates and updates digital representations of the current environment, enabling reliable navigation in previously unseen locations while maintaining adaptability to changing conditions
Data Source
AI summary
A forklift autonomous operation system is disclosed. The forklift autonomous operation system includes a forklift having a load handling system, the load handling system including a mast and a plurality of forks and a camera, coupled to the load handling system, for obtaining visual input data of an environment. Further, the system includes a plurality of sensors coupled to the forklift for obtaining sensor data and a control system configured to process the visual input data and the sensor data.