Autonomous Vehicle Controller for Crop-Row Localization
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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 in complex environments, relying on manual control and limited sensor data for task execution.
Innovation Solution
A system comprising motion sensors, actuators, and a processing apparatus that accesses map data and path structures to autonomously control vehicle movement and implement operations, utilizing sensors like lidar and image sensors for localization and dynamic adjustment of implement control based on environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle systems use manual control and limited sensor data for task execution, then device complexity is reduced, but navigation precision and operation accuracy deteriorate
Solution Approach 1:
The system segments the control architecture into modular components: motion sensors (lidar, image sensors, IMU) for environmental perception, navigation module for path planning, and implement control module for task execution. This segmentation allows each module to specialize in specific functions, improving navigation precision through dedicated sensor arrays while managing system complexity through modular design with defined interfaces between components.
Solution Approach 2:
The system performs preliminary actions by pre-mapping the operational environment using motion sensors before task execution. The navigation module pre-calculates optimal paths and the implement control module pre-configures task parameters based on environmental data. This preliminary processing enables precise navigation and operation by preparing control strategies in advance, reducing real-time computational complexity.
2Manufacturing precision
If the system integrates multiple sensors and actuators for autonomous control, then task execution accuracy improves, but device complexity increases
Solution Approach 1:
The system merges multiple sensors (lidar, image sensors, IMU) into an integrated perception system and multiple actuators into a unified control system. The processing apparatus consolidates data from all sensors to create a comprehensive environmental model, then coordinates all actuators through a single control interface. This merging improves operation accuracy by providing complete environmental awareness and coordinated actuator control, while managing complexity through centralized processing logic.
Solution Approach 2:
The system implements feedback loops where motion sensors continuously monitor environmental changes and vehicle position, actuators report their operational status, and the processing apparatus adjusts control commands in real-time. This feedback mechanism ensures high operation accuracy by continuously comparing actual system state with desired state and making corrective adjustments, while managing complexity through established feedback control algorithms.
3Productivity
If the vehicle autonomously navigates and controls implements using map data and path structures, then productivity increases, but loss of information increases due to complex data processing requirements
Solution Approach 1:
The system performs preliminary action by pre-processing environmental data into structured map representations before autonomous navigation begins. Path structures are pre-calculated based on map data and task requirements, with key waypoints and navigation parameters stored in advance. This preliminary data preparation enables efficient real-time navigation by reducing complex environmental data to essential navigation instructions, minimizing information loss during processing.
Solution Approach 2:
The system creates simplified copies of the environmental map in multiple formats optimized for different processing needs. The original detailed map is copied into navigation-ready path structures with extracted key features, and further copied into real-time navigation instructions for the control system. These progressive copies reduce information complexity at each stage while preserving essential navigation and task execution data, enabling high productivity with minimal information loss.
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 precise autonomous control of vehicles and implements in agricultural and industrial settings, improving efficiency and accuracy by integrating sensor data for real-time navigation and operation adjustments, enhancing task execution in complex environments.
Implementation Method 1
one or more motion sensors configured to detect motion of a vehicle
Implementation Method 2
utilizing sensors like lidar and image sensors for localization
Data Source
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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.