Vehicle Controller Navigation Through Crop Row Point Cloud Matching
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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 environmental sensing, leading to suboptimal performance and resource utilization.
Innovation Solution
The implementation of a vehicle controller system that includes motion sensors, actuators, and a processing apparatus to access maps, sensor data, and implement control data, enabling precise navigation and operation by determining the vehicle's location, matching environmental features, and adjusting implement operations based on real-time sensor feedback, such as from lidar and cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional localization methods are used, then the system is simpler, but the localization precision is insufficient for precise navigation
Solution Approach 1:
The patent combines multiple localization methods (GPS, inertial sensors, and visual odometry) into a unified system that leverages the strengths of each. GPS provides global position, inertial sensors provide continuous motion data, and visual odometry provides relative position correction, together achieving high precision localization without relying on a single complex system
Solution Approach 2:
The patent introduces map data as an intermediary element that mediates between sensor inputs and vehicle control. The map provides a reference framework that allows the system to translate sensor measurements into accurate position estimates, enabling precise navigation without directly increasing sensor complexity
2Manufacturing precision
If real-time environmental sensing is enhanced, then the navigation accuracy improves, but the processing complexity increases
Solution Approach 1:
The patent performs preliminary processing of sensor data by pre-building map data structures and pre-processing point cloud information. This allows the system to have complex sensing capabilities while managing processing load through advance preparation of data structures that can be efficiently queried during navigation
Solution Approach 2:
The patent segments the processing task into distinct modules: motion sensor data processing, point cloud processing, map matching, and control signal generation. Each module handles a specific aspect of the navigation problem, reducing overall processing complexity while maintaining high navigation accuracy through specialized processing in each segment
3Measurement precision
If implement control is adjusted based on real-time sensor feedback, then the operation precision improves, but the control system complexity increases
Solution Approach 1:
The patent implements a feedback control system where implement control is continuously adjusted based on real-time sensor feedback. The system monitors the vehicle's actual position and implement status, compares it with the desired state from the path data structure, and makes real-time adjustments to maintain precise operation, resolving the contradiction between precision and complexity through closed-loop control
4Productivity
If autonomous operation is implemented, then the productivity increases, but the reliability requirements become more stringent
Solution Approach 1:
The patent prepares contingency plans and backup systems in advance to handle potential failures. This includes having redundant sensors, pre-planned alternative paths, and fail-safe mechanisms that activate automatically if primary systems fail, thereby enabling autonomous operation with high productivity while maintaining stringent reliability requirements through advance preparation
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
This solution enables accurate and efficient autonomous operation of vehicles and their implements, improving navigation, resource application, and overall agricultural or industrial task performance by integrating sensor data with map-based localization and implement control.
Implementation Method 1
one or more motion sensors configured to detect motion of a vehicle
Implementation Method 2
access current point cloud data captured using a distance sensor connected to the vehicle
Implementation Method 3
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 in the vicinity of the vehicle
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.