Subsurface Reflection Navigation for Wire-Free Autonomous Machines
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Solution Overview
Problem
Autonomous grounds maintenance machines face challenges in navigating within defined work regions without relying on costly and cumbersome boundary wires, particularly due to limitations in computing resources and battery life.
Innovation Solution
The method involves determining the current pose of the machine using non-vision-based sensors and updating it with vision-based data from ground-penetrating radar to navigate within a work region, including training phases to generate three-dimensional point clouds and define boundaries for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If boundary wires are used to define work region boundaries, then navigation accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the boundary definition function from physical boundary wires and implements it through virtual boundaries created by GPS coordinate data and machine-mounted sensors. The system determines pose relative to work region boundaries using non-vision sensors and image data processing, eliminating the need for physical wire infrastructure while maintaining navigation accuracy.
Solution Approach 2:
The patent replaces the mechanical boundary wire system with an electronic/software-based navigation system. Instead of physically detecting wires with sensors, the system uses image data from cameras, GPS positioning, and computational algorithms to define and navigate within virtual work region boundaries, reducing device complexity and cost.
2Measurement precision
If sophisticated navigation systems are implemented, then navigation accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The patent segments the navigation system into distinct functional modules: non-vision-based pose determination using sensors like GPS and inertial measurement units, vision-based pose determination using image data from cameras, and a determination module that integrates both data sources. This modular architecture allows efficient resource utilization by activating only necessary components based on operational context.
Solution Approach 2:
The patent introduces an intermediary determination module that processes and integrates data from both non-vision and vision-based pose determination systems. This intermediary layer fuses multiple data sources to achieve accurate navigation while optimizing computing resource usage by selectively processing information based on current operational needs rather than continuously running all systems at full capacity.
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 approach enables robust autonomous navigation within work regions using limited computing resources, eliminating the need for boundary wires and improving navigation accuracy and efficiency.
Implementation Method 1
determining vision-based pose data based on image data captured by a ground penetrating radar
Data Source
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AI summary
Autonomous machine navigation involves determining a current pose of an autonomous machine based on non-vision-based pose data captured by one or more non-vision-based sensors of the autonomous machine. The pose represents one or both of a position and an orientation of the autonomous machine in a work region defined by one or more boundaries. Pose data is determined based on a return signal received in response to a wireless signal transmitted to a surface or subsurface object that passively provides the return signal. The return signal is identifiable with the object. The current pose is updated based on the pose data to correct or localize the current pose and to provide an updated pose of the autonomous machine in the work region.