Vehicle Controller Localization Using Crop-Row Map Matching
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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 for tasks like spraying or tillage, due to imprecise motion sensing and lack of real-time environmental data integration.
Innovation Solution
The implementation of a vehicle controller system that includes motion sensors, image sensors, and distance sensors to determine the vehicle's location and adjust implement operations based on real-time data from maps and environmental conditions, allowing for precise navigation and operation by controlling actuators to move the vehicle and perform tasks such as spraying or tillage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle systems use traditional motion sensors for navigation, then the system structure is simple, but the localization precision and navigation accuracy are insufficient
Solution Approach 1:
The patent combines multiple sensing systems (motion sensors, image sensors, distance sensors) and integrates them with map data structures to achieve precise localization. The processing apparatus fuses data from these diverse sources to determine vehicle position and control implements, resolving the contradiction by merging simple components into a complex integrated system that delivers high measurement precision.
Solution Approach 2:
The patent introduces map data structures as an intermediary between the sensors and the control system. The map represents physical objects and serves as a reference framework that mediates the conversion of raw sensor data into meaningful localization information, enabling precise positioning without requiring direct complex processing of all sensor inputs.
2Measurement precision
If the vehicle controller integrates real-time environmental data and map data for precise navigation, then the navigation accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent pre-processes environmental data during a demonstration run to create map data structures before actual autonomous operation. This preliminary action stores processed information about physical objects and their locations, so that during real-time navigation, the system only needs to query and compare against the pre-built map, significantly reducing processing time while maintaining high navigation accuracy.
Solution Approach 2:
The map data structure focuses on storing only the relevant local features and physical objects necessary for navigation, rather than processing all environmental data in real-time. This selective local quality approach allows the system to achieve high navigation accuracy by comparing sensor data against specific pre-identified features in the map, reducing overall computational load.
3Manufacturing precision
If the system uses multiple sensors and actuators for precise implement control, then the operation precision improves, but the device complexity increases
Solution Approach 1:
The processing apparatus serves multiple functions: it processes data from motion sensors, image sensors, and distance sensors; it localizes the vehicle; it controls the vehicle's motion; and it controls the implement operations. This multi-functional approach consolidates what could be separate complex systems into a single universal processing unit, achieving high implement control precision without proportionally increasing overall system complexity.
Solution Approach 2:
The system uses motion sensors to continuously monitor vehicle motion and provides feedback to the processing apparatus, which adjusts actuator commands in real-time. This feedback loop enables precise implement control by constantly comparing actual vehicle position and motion against desired trajectories, allowing the system to achieve high precision through iterative correction rather than overly complex open-loop control mechanisms.
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.