3D Terrain Mapping for Predictive Agricultural Vehicle Control
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Solution Overview
Problem
Existing vehicle control systems lack the ability to generate and utilize accurate three-dimensional terrain maps, particularly in agricultural settings, leading to inefficiencies and potential damage when navigating uneven or rolling terrain.
Innovation Solution
A system that generates three-dimensional terrain maps using sensors and GNSS data, incorporating features like vegetation and obstacles, and adjusts vehicle paths in real-time to optimize steering and implement control, utilizing machine learning for continuous map updates and sensor fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicle control systems navigate terrain without three-dimensional terrain maps, then the system complexity is reduced, but steering performance and vehicle control accuracy deteriorate
Solution Approach 1:
The system generates three-dimensional terrain maps in advance by fusing sensor data (LIDAR, cameras, GNSS) before the vehicle reaches the terrain. This preliminary mapping allows the control system to anticipate terrain features and plan optimal paths ahead of time, improving steering performance without requiring complex real-time processing during vehicle operation.
Solution Approach 2:
The patent introduces an intermediary terrain map representation that mediates between raw sensor data and vehicle control decisions. The three-dimensional terrain map serves as an intermediate structure that stores processed terrain information, allowing the control system to query pre-processed terrain data rather than processing raw sensor streams in real-time, thus reducing computational complexity while maintaining steering accuracy.
2Measurement precision
If three-dimensional terrain maps are generated using sensor fusion and machine learning, then terrain mapping accuracy is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The terrain mapping system is segmented into multiple independent modules: LIDAR processing module, camera processing module, GNSS integration module, and machine learning classification module. Each module processes specific sensor data independently and contributes to the overall three-dimensional terrain map, allowing for modular complexity management while achieving high mapping accuracy through coordinated operation of specialized components.
3Measurement precision
If real-time path adjustments are made based on three-dimensional terrain maps, then vehicle control accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary path planning by analyzing the three-dimensional terrain map before the vehicle reaches each terrain section. Optimal paths are pre-calculated based on terrain features, vehicle constraints, and mission objectives. During actual vehicle operation, the system only needs to follow the pre-planned path and make minor adjustments, significantly reducing real-time computational requirements while maintaining high control 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
Improves steering performance and reduces vehicle strain by anticipating terrain changes, allowing for precise path planning and implement adjustments, enhancing safety and efficiency in agricultural operations.
Implementation Method 1
data from a sensor system including a LIDAR sensor
Implementation Method 2
A terrain mapping system receives, from a global navigation satellite system (GNSS) receiver, position data associated with a vehicle
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Embodiments of the present disclosure relate generally to generating and utilizing three-dimensional terrain maps for vehicular control. Other embodiments may be described and/or claimed.