Autonomous Vehicle Forest Data Collection System
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Solution Overview
Problem
Current forestry management systems are time-consuming and complex, relying on human operators for data collection, which leads to inconsistencies, inaccuracies, and inefficiencies due to the need for extensive sensor readings and sampling, often resulting in incomplete and inaccurate forest assessments.
Innovation Solution
A forestry management system utilizing autonomous vehicles to collect and analyze data, coordinating their operations to generate accurate and consistent forest state assessments, reducing the need for extensive human intervention and improving data collection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human operators collect forest information using traditional tools, then the data collection process can be performed with simple equipment, but the process becomes very time-consuming and complex requiring tens of thousands of sensor readings
Solution Approach 1:
The patent replaces manual mechanical data collection methods with autonomous vehicles equipped with sensors. These vehicles automatically traverse the forest, collect environmental data, and transmit information to the management system, eliminating the need for human operators to physically collect tens of thousands of sensor readings manually.
Solution Approach 2:
The autonomous vehicles perform self-directed data collection missions without human intervention. The forestry manager system automatically coordinates vehicle operations, assigns collection tasks, and processes the gathered information, enabling the system to serve itself in completing forest assessment operations.
2Productivity
If sampling is used to collect forest information, then the data collection process is faster, but errors occur due to lack of adequate information collection and analysis
Solution Approach 1:
The autonomous vehicles are equipped with multiple sensor types that can collect diverse forest parameters simultaneously (tree spacing, health indicators, environmental conditions). This multi-functional capability allows comprehensive data collection across the entire forest area rather than limited sampling, improving both speed and accuracy.
Solution Approach 2:
The system transitions from ground-based sampling to aerial or elevated perspective data collection using autonomous vehicles. This dimensional change enables coverage of previously inaccessible regions and provides a more comprehensive view of forest conditions, eliminating sampling errors while maintaining high productivity.
3Measurement precision
If multiple forest locations are monitored comprehensively, then the forest state assessment is more accurate, but the time and effort needed increases significantly
Solution Approach 1:
The forest area is divided into multiple zones or locations, each assigned to specific autonomous vehicles for monitoring. The forestry manager system coordinates these vehicles to systematically cover different segments simultaneously, enabling comprehensive assessment of all locations without the time penalty of sequential manual inspection.
4Ease of manufacture
If human operators interpret measurements using tools like clinometers, then the equipment is simple to use, but different operators make different interpretations leading to inconsistent results
Solution Approach 1:
The patent replaces manual interpretation of clinometer readings with automated sensor systems on autonomous vehicles. These electronic sensors objectively measure tree parameters and environmental conditions, eliminating human interpretation variability and ensuring consistent, reliable data across all measurements while maintaining ease of operation through automated processing.
Data Source
AI summary
A method and apparatus of managing a forest. A forestry management system comprises a forestry manager. The forestry manager is configured to receive information about a forest from a group of autonomous vehicles, analyze the information to generate a result about a state of the forest from the information, and coordinate operation of the group of autonomous vehicles using the result.