Vision-Based Autonomous Navigation Without Boundary Wires
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Solution Overview
Problem
Autonomous grounds maintenance machines face challenges in navigating within predefined boundaries without relying on costly and cumbersome boundary wires, especially due to limited computing resources and battery life.
Innovation Solution
The implementation of a method for autonomous machine navigation that utilizes a vision system and non-vision-based sensors to define and maintain boundaries, allowing the machine to correct its position and orientation, and involves a training mode to generate a three-dimensional point cloud for navigation, enabling efficient operation within a work region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If boundary wires are used to define work region boundaries, then navigation reliability is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent replaces the mechanical boundary wire system with a vision-based navigation system. The autonomous machine uses cameras and image processing algorithms to detect and track boundary features (such as fences, hedges, or marked lines) in the environment, substituting the physical wire boundary with optical sensing and computational vision to achieve boundary definition and navigation.
Solution Approach 2:
The vision system creates a digital representation or map of the work region boundaries based on visual data. The machine captures images of boundary features, processes them to identify boundary locations and orientations, and uses this copied spatial information to navigate without physical wires, effectively replacing the physical boundary wire with a digital boundary model.
2Measurement precision
If sophisticated navigation systems are implemented, then navigation precision is improved, but computing resource consumption increases
Solution Approach 1:
The vision system operates periodically rather than continuously. The machine captures images at specific intervals or at key navigation decision points, processes these images to update its position and boundary information, and then navigates using the accumulated data. This periodic operation reduces computational load and energy consumption compared to continuous high-frequency processing, while maintaining adequate navigation precision.
Solution Approach 2:
The system performs preliminary image capture and processing during periods when the machine is stationary or moving slowly, such as during charging cycles or between work tasks. By pre-processing visual data and building boundary models in advance, the system reduces the computational burden during active work periods, thereby conserving battery life while maintaining navigation precision.
Data Source
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AI summary
Autonomous machine navigation techniques may generate a three-dimensional point cloud that represents at least a work region based on feature data and matching data. Pose data associated with points of the three-dimensional point cloud may be generated that represents poses of an autonomous machine. A boundary may be determined using the pose data for subsequent navigation of the autonomous machine in the work region. Non-vision-based sensor data may be used to determine a pose. The pose may be updated based on the vision-based pose data. The autonomous machine may be navigated within the boundary of the work region based on the updated pose. The three-dimensional point cloud may be generated based on data captured during a touring phase. Boundaries may be generated based on data captured during a mapping phase.