Row-Based World Model for Low-Drift AV Mission Planning
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Solution Overview
Problem
Current Simultaneous Localization and Mapping (SLAM) techniques are computationally intensive and prone to exponential sensor error drift, making them challenging to implement in agricultural environments with limited technical sophistication and infrastructure.
Innovation Solution
A row-based world model is generated using a cloud component that provides a simplified frame of reference for autonomous vehicles, allowing for semantic user instructions and reducing the complexity of navigation by associating each row with a unique frame of reference, including distance and plant information, enabling efficient mission planning and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SLAM techniques are used for autonomous vehicle navigation, then localization and mapping capabilities are improved, but computational intensity and sensor error drift increase
Solution Approach 1:
The patent segments the agricultural environment into distinct row structures, creating a simplified frame of reference system where each row is independently mapped and localized. This segmentation reduces the overall computational complexity by breaking down the complex field into manageable, structured units rather than processing the entire environment as a single complex space.
Solution Approach 2:
The patent changes the parameter representation from continuous 3D spatial coordinates used in traditional SLAM to discrete row-based indices and positions. This parameter transformation simplifies the mathematical computations required for localization and mapping, reducing computational intensity while maintaining adequate precision for agricultural navigation tasks.
2Measurement precision
If SLAM techniques are used for autonomous vehicle navigation, then localization and mapping capabilities are improved, but sensor error drift increases
Solution Approach 1:
The patent implements feedback mechanisms where the autonomous vehicle continuously monitors its position relative to known row structures and uses this information to correct accumulated errors. By repeatedly referencing the structured row-based frame of reference, the system can detect and compensate for sensor drift, maintaining long-term localization accuracy without the exponential error accumulation characteristic of traditional SLAM.
3Loss of information
If traditional world models are used for autonomous vehicle navigation, then comprehensive environmental representation is achieved, but ease of operation decreases
Solution Approach 1:
The patent extracts the essential navigational information from the complex agricultural environment by focusing specifically on row structures. Rather than attempting to represent all environmental features, the system extracts and utilizes only the critical row-based framework needed for navigation, simplifying both the world model and operational complexity while retaining sufficient information for effective autonomous operation.
Data Source
AI summary
A system, method, and autonomous vehicle (AV) that executes an AV mission plan for a field having plants that follow a row are described. The system includes a cloud component that generates a row-based world model with row-based frames of reference. A semantic user instruction associated with the AV mission plan is received. The semantic user instruction is associated with the row-based world model and generates the AV mission plan. The AV receives the AV mission plan from the cloud component. The AV executes the AV mission plan and completes the AV mission plan. The AV then uploads the AV information gathered from the AV mission plan to the cloud component. The cloud component geocodes the location of each feature with the row-based world model so that the feature includes at least one row number and at least one distance associated with the row number.