Work Vehicle Row-Turn Control Using Ground-Fixed LiDAR Coordinates
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Solution Overview
Problem
In environments with high-density tree or crop distributions, such as vineyards or orchards, the leaf canopies create obstacles and multiple reflectors for radio waves, hindering accurate Global Navigation Satellite System (GNSS) positioning. Additionally, seasonal changes in leaf distribution render pre-created maps ineffective for autonomous vehicle navigation.
Innovation Solution
A work vehicle equipped with exterior sensors, such as LiDAR, to output sensor data indicating the distribution of geographic features. A controller programs the vehicle to detect crop rows on opposite sides, set a coordinate system for turning travel fixed to the ground surface, and control the vehicle to travel along a path between the crop rows, adjusting for turning directions based on real-time sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS-based positioning is used for autonomous travel, then positioning accuracy is improved, but it becomes inapplicable in high-density tree environments where leaf canopies block radio waves
Solution Approach 1:
The patent introduces LiDAR sensors and SLAM technology as intermediary systems to enable positioning when GNSS is unavailable. The LiDAR sensors capture spatial information about crop rows and obstacles, while SLAM algorithms process this data to determine vehicle position and orientation, serving as a mediator between the vehicle's navigation needs and the blocked radio wave environment
Solution Approach 2:
The patent replaces the radio wave-based GNSS positioning system with a light-based LiDAR sensing system. Instead of relying on electromagnetic radio waves that are blocked by leaf canopies, the system uses laser light to actively measure distances and map the environment, substituting one physical principle for another that is more suitable for dense vegetation environments
2Productivity
If pre-created maps are used for autonomous navigation, then navigation efficiency is improved, but they become ineffective due to seasonal changes in leaf distribution
Solution Approach 1:
The patent implements dynamic map updates by continuously acquiring new spatial information using LiDAR sensors during vehicle operation. Instead of relying on static pre-created maps that become obsolete with seasonal changes, the system dynamically adapts its environmental model by continuously sensing and updating crop row positions and obstacle locations in real-time
Solution Approach 2:
The system incorporates feedback mechanisms where the vehicle continuously senses its environment using LiDAR, compares detected features with stored map data, and updates its positional information and environmental model accordingly. This closed-loop feedback ensures the navigation system remains accurate despite seasonal variations in vegetation
3Adaptability or versatility
If SLAM is used for positioning in GNSS-denied environments, then environmental adaptability is improved, but measurement precision deteriorates due to challenges in complex tree distributions
Solution Approach 1:
The patent merges multiple sensing modalities and information sources to improve SLAM positioning accuracy. It combines LiDAR spatial mapping with visual information from cameras, integrates inertial measurement unit (IMU) data for motion tracking, and fuses these with GPS data when available, creating a multi-sensor fusion system that overcomes the limitations of pure SLAM in complex environments
4Measurement precision
If automatic steering by GNSS is used, then travel precision between crop rows is improved, but it becomes inapplicable in vineyards and orchards with dense tree distributions
Solution Approach 1:
The patent uses LiDAR sensors as intermediary devices to detect crop rows and obstacles when GNSS automatic steering cannot function. The LiDAR system actively measures distances to crop rows and generates spatial maps that enable the vehicle to navigate between rows with precision comparable to GNSS-based systems, but through direct optical measurement rather than satellite positioning
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
Enables the work vehicle to perform smooth self-traveling among multiple crop rows in environments where GNSS-based positioning is difficult, maintaining accuracy and adaptability despite seasonal changes in vegetation.
Implementation Method 1
exterior sensors, such as LiDAR, to output sensor data indicating the distribution of geographic features
Data Source
AI summary
A work vehicle includes an exterior sensor to output sensor data indicating a distribution of geographic features around the work vehicle, and a controller configured or programmed to control self-traveling of the work vehicle, detect two crop rows existing on opposite sides of the work vehicle based on the sensor data, and cause the work vehicle to travel along a path between the two crop rows. During travel, if an end of at least a crop row that corresponds to a turning direction between the two crop rows is detected based on the sensor data, the controller is configured or programmed to set a coordinate system for turning travel that is fixed to a ground surface and a target point for the turning travel. The controller is configured or programmed to control the turning travel toward the target point based on the coordinate system.