Vehicle Controller Navigation by Crop Row Matching and Point Clouds
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Solution Overview
Problem
Current autonomous vehicle systems lack efficient methods for precise navigation and operation in agricultural and industrial environments, particularly in accurately locating vehicles within mapped areas and controlling implements based on real-time environmental data.
Innovation Solution
The implementation of systems that include motion sensors, image sensors, and distance sensors connected to processing apparatuses, which access map data structures and path data structures to control vehicle movement and implement operations, allowing for dynamic adjustments based on sensor data and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle systems use sensors and map data structures to navigate agricultural and industrial environments, then navigation precision and operation accuracy are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the operational area into a map data structure with discrete locations and waypoints. The vehicle's navigation is divided into sequential path following steps, with implement operations triggered at specific waypoints. This segmentation allows precise location tracking while managing system complexity through structured data organization.
Solution Approach 2:
The patent introduces a processing apparatus as an intermediary between the sensors and the vehicle control systems. This intermediary component receives sensor data, processes it against the map data structure, and generates control commands, thereby managing the complexity of integrating multiple sensors and control functions.
2Manufacturing precision
If the system uses motion sensors and path data structures to control vehicle movement to waypoints, then vehicle positioning accuracy and implement operation timing are improved, but data processing time and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-defining the map data structure with all possible locations, waypoints, and associated implement control data before vehicle operation. This allows the processing apparatus to simply match current sensor data against the pre-established structure, reducing real-time computational load while maintaining precise operation timing.
3Productivity
If the system integrates multiple sensors and actuators for autonomous vehicle and implement control, then operational automation and productivity are improved, but device complexity and control difficulty increase
Solution Approach 1:
The patent merges the control of the vehicle and the implement into a single integrated system controlled by one processing apparatus. The map data structure contains both vehicle navigation waypoints and implement control data, allowing coordinated control of multiple actuators through a unified control logic, thereby improving automation while managing control complexity.
Data Source
AI summary
Systems and methods for vehicle controllers for agricultural and industrial applications are described. For example, a method includes accessing a map data structure storing a map representing locations of physical objects in a geographic area; accessing current point cloud data captured using a distance sensor connected to a vehicle; detecting a crop row based on the current point cloud data; matching the detected crop row with a crop row represented in the map; determining an estimate of a current location of the vehicle based on a current position in relation to the detected crop row; and controlling one or more actuators to cause the vehicle to move from the current location of the vehicle to a target location.