Autonomous Work Region Boundaries for Geolocation Dead Zones
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Solution Overview
Problem
Existing autonomous work vehicles face challenges in efficiently defining and navigating work regions, particularly in areas with dead zones where wireless geolocation services are unavailable, requiring tedious user input and potentially inaccurate digital map definitions.
Innovation Solution
The method involves defining a work region with boundaries and dead zones, generating a traversal pattern that encounters dead zones, and using a local navigation mode within these zones that does not rely on wireless geolocation services, such as visual navigation with stored 3D point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital map definitions are used to define work regions, then boundary definition can be performed, but user input is tedious and accuracy is poor in dead zones
Solution Approach 1:
The autonomous machine performs self-training by autonomously navigating the work region and collecting sensor data to define boundaries and dead zones without requiring extensive user input or training. The system learns and stores this information for future autonomous operations.
Solution Approach 2:
The system performs preliminary autonomous training runs to map the work region, identify dead zones where wireless geolocation is unavailable, and store this information before actual work operations begin. This preliminary action eliminates the need for tedious user input during operational phases.
2Measurement precision
If wireless geolocation service is used for navigation, then navigation accuracy is improved, but service is unavailable in dead zones
Solution Approach 1:
The system implements different navigation strategies for different regions: wireless geolocation service is used in areas with good signal availability, while local sensor-based navigation (cameras, LIDAR, odometry) is used in dead zones. The system automatically detects and adapts to the local environment's signal conditions.
Solution Approach 2:
The system uses sensor data (cameras, LIDAR, odometry sensors) as an intermediary navigation method when wireless geolocation service is unavailable in dead zones. This intermediary approach maintains navigation capability without relying on the primary wireless service.
3Ease of operation
If user guidance is provided for dead zone traversal, then navigation through dead zones is improved, but resource consumption increases
Solution Approach 1:
The system performs preliminary training runs to learn and store the locations of dead zones and the optimal paths through them. During actual work operations, the system simply executes the pre-planned paths through dead zones using stored information, avoiding real-time computational complexity and reducing resource consumption.
Data Source
AI summary
A boundary of a work region is defined in which an autonomous machine is to operate. A dead zone is defined in the work region wherein a loss of a wireless geolocation service is known or predicted. A traversal pattern within the boundary is autogenerated. The autonomous machine executes the traversal pattern to perform work using the wireless geolocation service to navigate outside of the dead zone. When encountering the dead zone before, during, or after executing the traversal pattern, the machine prioritizes a localization input that does not rely on the wireless geolocation service to perform the work in the dead zone.