Autonomous Vehicle Control Using Crop Row Matching and Map Data
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Solution Overview
Problem
Existing vehicle control systems for agricultural and industrial applications lack the capability to autonomously navigate and operate implements with precision, especially in complex environments with varying terrain and crop conditions.
Innovation Solution
The implementation of a vehicle control system that includes motion sensors, actuators, and a processing apparatus capable of accessing map data structures, path data structures, and sensor data to autonomously control vehicle movement and implement operations, such as spraying or tillage, based on predefined paths and real-time sensor feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle control systems are implemented with sensors and processing apparatus, then navigation precision and implement operation accuracy are improved, but device complexity increases
Solution Approach 1:
The autonomous vehicle control system is divided into distinct functional modules: motion sensors for detection, processing apparatus for computation, and actuators for execution. Each module operates independently but coordinates through standardized interfaces, allowing the complex system to be managed through modular components that can be developed, tested, and maintained separately while achieving high navigation precision.
Solution Approach 2:
The processing apparatus serves multiple functions simultaneously: it processes motion sensor data for localization, generates navigation paths, controls implement operations, and integrates information from multiple sensor sources. This multi-functionality reduces the need for separate dedicated systems for each task, thereby managing device complexity while maintaining comprehensive control capabilities.
2Productivity
If multiple sensors and actuators are integrated for autonomous operation, then productivity is improved, but device complexity increases
Solution Approach 1:
Multiple sensors (cameras, LIDAR, GPS) and actuators (steering, throttle, brake, implement controls) are merged into a single integrated autonomous control system managed by one processing apparatus. This consolidation allows coordinated operation of all components to achieve high productivity through autonomous navigation and implement operation, while the unified architecture manages complexity through centralized control logic and standardized communication protocols.
Solution Approach 2:
The autonomous vehicle control system operates independently without continuous human intervention. The processing apparatus autonomously processes sensor data, makes navigation decisions, controls implement operations, and adjusts to environmental conditions in real-time. This self-service capability enables continuous productive operation while reducing the need for manual monitoring and adjustment of the multiple sensors and actuators.
3Manufacturing precision
If real-time sensor data processing is implemented, then implement operation accuracy is improved, but use of energy increases
Solution Approach 1:
The processing apparatus processes sensor data at optimized intervals rather than continuously, updating navigation decisions and implement controls at frequencies sufficient for accuracy but reduced enough to conserve energy. Motion sensors are sampled at appropriate rates for the operational context, allowing the system to maintain implement operation accuracy while avoiding unnecessary computational energy consumption during steady-state operation.
Solution Approach 2:
The system pre-processes sensor data and pre-plans navigation paths before execution, preparing control commands in advance when computational demand is lower. By anticipating required actions and pre-computing responses, the system reduces real-time processing intensity during critical execution phases, thereby maintaining implement operation accuracy while managing energy consumption through proactive rather than reactive processing.
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.