Vehicle Implement Control Using Sensor Fusion Plant Detection
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Solution Overview
Problem
Current autonomous vehicle systems for agricultural and industrial applications lack efficient methods for precise navigation and operation of implements, such as sprayers and tillers, due to imprecise localization and inadequate real-time adjustment of implement control based on environmental conditions.
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 environmental conditions, allowing for the generation of path data structures and real-time adjustment of implement control data to perform operations like spraying or tillage with precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle systems use basic sensors and simple navigation methods, then the system complexity is low, but the navigation precision and implement control accuracy are insufficient
Solution Approach 1:
The patent combines multiple sensor types (motion sensors, image sensors, distance sensors) and multiple data sources (map data structures, path data structures, sensor data) into an integrated localization and control system. This merging allows the system to achieve high measurement precision through sensor fusion and multi-source data correlation, while the modular architecture manages the inherent complexity through systematic integration.
Solution Approach 2:
The processing apparatus serves multiple functions: it processes data from various sensor types, performs localization, generates motion plans, controls vehicle actuators, and adjusts implement operations. This multi-functionality consolidates what would otherwise require separate systems, achieving high precision without proportionally increasing overall system complexity.
2Manufacturing precision
If the system uses detailed map data structures and path data structures with implement control data, then the navigation and implement control precision improve, but the data processing complexity increases
Solution Approach 1:
The system pre-generates map data structures and path data structures with embedded implement control data before field operations. This preliminary action organizes complex data relationships in advance, allowing the processing apparatus to efficiently retrieve and execute pre-planned control sequences during actual operations, thereby achieving high implement precision without real-time processing bottlenecks.
Solution Approach 2:
The patent introduces structured data intermediaries (map data structures, path data structures, waypoint records) that mediate between high-level navigation goals and low-level implement control. These data structures organize complex information hierarchically, with waypoints serving as intermediaries that link vehicle position to implement operation parameters, simplifying the control logic while maintaining precision.
3Productivity
If the system processes motion sensor data in real-time to determine vehicle location and adjust implement control, then the operational efficiency improves, but the computational load and processing complexity increase
Solution Approach 1:
The processing apparatus continuously processes motion sensor data, image sensor data, and distance sensor data in real-time throughout vehicle operation. This continuous processing enables dynamic localization updates and real-time implement adjustments, maintaining high operational efficiency. The system manages computational load through optimized data flow and prioritized processing of critical parameters.
Solution Approach 2:
The system implements closed-loop feedback by continuously comparing actual vehicle position (determined from motion sensor data) against the planned path, and by monitoring implement performance. This feedback drives real-time corrections to both vehicle navigation and implement control, achieving high operational efficiency through adaptive control while managing complexity through feedback-based decision making.
4Reliability
If the system integrates multiple sensor types and data sources for comprehensive environmental awareness, then the navigation reliability improves, but the system complexity increases
Solution Approach 1:
The patent merges data from motion sensors, image sensors, and distance sensors into a unified localization and navigation framework. This sensor fusion approach improves navigation reliability by cross-validating measurements and compensating for individual sensor limitations. The modular data processing architecture manages integration complexity through standardized interfaces and hierarchical data organization.
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 accurate navigation and operation of agricultural and industrial vehicles by determining the vehicle's location and adjusting implement control based on environmental data, improving efficiency and precision in tasks like spraying and tillage.
Implementation Method 1
one or more motion sensors configured to detect motion of a vehicle
Implementation Method 2
one or more image sensors connected to the vehicle... receive image data, captured using the one or more image sensors, depicting one or more plants
Implementation Method 3
normalized difference vegetation index camera connected to the vehicle... receive normalized difference vegetation index data, captured using the normalized difference vegetation index camera
Implementation Method 4
distance sensor connected to the vehicle... access current point cloud data captured using the distance sensor
Data Source
AI summary
Systems and methods for vehicle controllers for agricultural and industrial applications are described. For example, a method includes receiving image data, captured using one or more image sensors connected to a vehicle, depicting one or more plants in a vicinity of the vehicle; detecting the one or more plants based on the image data; responsive to detecting the one or more plants, adjusting implement control data; and controlling, based on the adjusted implement control data, an implement connected to the vehicle to perform an operation on the one or more plants.