Robotic Vehicle Controller for Autonomous Path Following
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Solution Overview
Problem
Existing machine control systems for tasks like lawn mowing lack efficient automation, relying on human operators and struggling with precision and efficiency, especially in maintaining paths and avoiding obstacles.
Innovation Solution
A control system integrating a servo machine controller with machine vision and GPS monitoring for real-time steering and path correction, allowing machines to learn and follow paths defined by markers, and communicate with other machines for coordinated operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a robotic controller with machine vision and GPS is integrated into the machine, then automation and path-following precision are improved, but device complexity increases
Solution Approach 1:
The patent combines multiple control functions (servo controller, machine vision system, GPS monitor) into a single integrated robotic vehicle controller. This merging approach allows the system to achieve high automation by coordinating these functions through a unified control architecture, resolving the contradiction between automation extent and device complexity.
Solution Approach 2:
The controller acts as an intermediary that processes information from machine vision markers and GPS signals, then translates them into servo control commands. This intermediary role enables automated decision-making while managing the complexity of coordinating multiple subsystems through a central intelligence layer.
2Measurement precision
If machine vision and GPS monitoring are used for real-time path correction, then path-following precision is improved, but use of energy increases
Solution Approach 1:
The system continuously operates machine vision and GPS monitoring to maintain constant awareness of position relative to the learned path. This continuous measurement enables real-time correction of drift, ensuring high path-following precision while optimizing energy use by maintaining only essential monitoring functions.
Solution Approach 2:
The controller receives continuous feedback from GPS location data and machine vision marker detection, compares actual position with the learned path, and automatically adjusts steering to correct drift. This closed-loop feedback system maintains precision without requiring excessive energy by only activating corrections when deviations are detected.
3Productivity
If the controller learns and stores path data for autonomous operation, then productivity is improved, but loss of time during path learning occurs
Solution Approach 1:
The system performs path learning in advance by having the operator drive the machine along the desired path while the controller records control signals, GPS locations, and machine responses. This preliminary action creates a reusable digital model of the path, enabling subsequent autonomous operations to proceed at full productivity without repeated manual intervention.
Solution Approach 2:
The controller creates a digital copy of the physical path by recording control signals and GPS data during the learning phase. This copied path information is stored in memory and reused for autonomous navigation, eliminating the need to physically retrace the path and significantly improving operational efficiency after the initial learning time is invested.
Data Source
AI summary
A control system is provided for automatically moving a machine along a desired path. The machine uses a “learn and follow” system in which the machine is first operated in a manual mode along a desired path. The control system learns the desired path and then operates the machine in a robotic mode to follow the desired path without operator control. If the machine moves away from the desired path when in the robotic mode, the location of the machine is corrected using GPS location signals.