Vehicle Controller Using Crop-Row Matching for Autonomous Navigation
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Solution Overview
Problem
Existing vehicle control systems for agricultural and industrial applications lack the ability to autonomously navigate and control implements in complex environments, relying heavily on manual operation and limited sensor data.
Innovation Solution
The implementation of a vehicle controller system that integrates motion sensors, actuators, and a processing apparatus to autonomously navigate vehicles and control implements. This system uses map data structures, path data structures, and sensor data to determine the vehicle's location and control the implement's operations based on predefined waypoints and real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle control systems are implemented, then navigation precision and operational accuracy are improved, but system complexity and cost increase
Solution Approach 1:
The autonomous vehicle control system is divided into separate functional modules: motion sensor module for detecting vehicle movement, actuator module for controlling implements, and processing apparatus for integrating data and making control decisions. This segmentation allows each module to be optimized independently while maintaining overall system precision without excessive complexity.
Solution Approach 2:
The processing apparatus serves multiple functions simultaneously: it accesses map data structures, processes motion sensor data, determines vehicle location, controls actuators for navigation, and adjusts implement operations. This multi-functionality reduces the need for separate dedicated systems, thereby improving precision without proportionally increasing system complexity.
2Speed
If real-time sensor data processing is implemented, then operational responsiveness is improved, but computational load and energy consumption increase
Solution Approach 1:
The system pre-loads and caches map data structures and path information into the processing apparatus before autonomous operation begins. This preliminary action allows the system to process real-time sensor data with minimal computational delay, improving operational responsiveness without the energy cost of processing large datasets in real-time.
Solution Approach 2:
The processing apparatus focuses computational resources on processing motion sensor data and control decisions relevant to the vehicle's current location and immediate operational context, rather than processing all possible sensor data uniformly. This localized processing approach improves responsiveness while reducing overall energy consumption.
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.