Distributed Knowledge Base for Vehicle Localization
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Solution Overview
Problem
Current autonomous and semi-autonomous vehicle systems face challenges in accurately navigating and managing work sites due to unreliable obstacle detection and localization, especially in environments with heavy foliage or obstructions that interfere with GPS signals, leading to errors in positioning and navigation.
Innovation Solution
A distributed knowledge base system within the vehicle that combines a fixed and learned knowledge base, utilizing a machine controller to integrate sensor data from various sources, including GPS, cameras, and lidar, to dynamically adjust navigation and obstacle avoidance strategies, allowing for both autonomous and operator-assisted control modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based localization is used for vehicle navigation, then positioning capability is provided, but accuracy deteriorates in environments with heavy foliage or obstructions that interfere with GPS signals
Solution Approach 1:
The patent combines GPS localization with visual localization using cameras and lidar to create a hybrid positioning system. The sensor system integrates multiple data sources including GPS receivers, cameras, and lidars to compensate for GPS signal interference in environments with heavy foliage, thereby maintaining positioning accuracy and navigation reliability.
Solution Approach 2:
The patent introduces visual features and landmarks as intermediary elements for localization. When GPS signals are blocked, the system uses cameras to detect visual features and lidars to scan for landmarks, serving as intermediary reference points that enable accurate positioning without direct GPS dependency.
2Measurement precision
If multiple sensor data sources are integrated for improved navigation accuracy, then positioning precision is enhanced, but system complexity increases
Solution Approach 1:
The patent segments the localization system into distinct functional modules: GPS receivers for satellite-based positioning, cameras for visual feature detection, lidars for landmark scanning, and a sensor system for data integration. Each module operates semi-independently, allowing the complex functionality to be managed through modular architecture while maintaining high navigation accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and reliability of vehicle navigation and obstacle avoidance in diverse environments by integrating multiple sensor data sources and adaptive learning, ensuring safe and efficient operation of vehicles in complex work sites.
Implementation Method 1
GPS, cameras, and lidar
Implementation Method 2
lidar
Data Source
Figure 1
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Figure 3
AI summary
A vehicle is comprised of a machine controller, a steering system, a propulsion system, a braking system, a sensor system, and a knowledge base used by the machine controller. The machine controller identifies a dynamic condition and sends commands to the steering system, the propulsion system, and the braking system to move the vehicle.