Heterogeneous Sensor Data Fusion for Autonomous Mining Equipment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The integration of new technologies in mine exploitation, such as autonomous drills and trucks, poses challenges in effectively managing and optimizing the overall process, including the integration with existing resources and tools, leading to inefficiencies and increased costs.
Innovation Solution
A system and method for generating models of the environment using pre-extraction, in-ground, and post-extraction data from heterogeneous sensors, combined with equipment data, to create integrated models that facilitate better control and management of equipment units, enhancing resource extraction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If new technologies such as autonomous drills and trucks are introduced to enhance specific procedures, then efficiency and cost-effectiveness of individual tasks are improved, but integration complexity with existing resources and tools increases
Solution Approach 1:
The patent merges data from heterogeneous sensors (seismic, electromagnetic, gravitational, etc.) and multiple equipment units into a unified environmental model. This integration allows autonomous drills, trucks, and existing tools to operate from a common information base, improving overall system coordination while maintaining individual equipment efficiency.
Solution Approach 2:
The environmental model serves multiple functions simultaneously: it guides autonomous equipment operation, optimizes extraction procedures, monitors safety conditions, and coordinates between different equipment units. This multi-functional approach reduces integration complexity by providing a single versatile system rather than multiple specialized integration layers.
2Measurement precision
If data from multiple heterogeneous sensors is collected to create comprehensive environmental models, then accuracy of resource extraction is improved, but data processing complexity and time consumption increase
Solution Approach 1:
The system performs preliminary data fusion by creating and updating environmental models in advance of extraction operations. Sensors continuously collect data that is processed into predictive models of resource location, geological conditions, and equipment positioning, allowing equipment to operate with pre-processd information rather than real-time processing delays.
Solution Approach 2:
The environmental model acts as an intermediary between raw sensor data and equipment control decisions. Instead of directly processing all sensor inputs for each control decision, the system uses the environmental model as a intermediate representation that simplifies and accelerates the decision-making process for autonomous equipment.
3Productivity
If autonomous equipment units are deployed to reduce human resources, then labor costs are reduced, but system coordination and control complexity increase
Solution Approach 1:
The system implements continuous feedback loops where autonomous equipment units report their status, position, and operational data to the central environmental model, which in turn provides updated guidance and coordination information. This feedback mechanism enables automatic system coordination without requiring complex manual control protocols.
Solution Approach 2:
Autonomous equipment units are equipped with onboard sensors and processing capabilities that allow them to independently interpret environmental models and adjust their operations. This self-service capability reduces the coordination burden on central control systems, as each unit can autonomously respond to environmental changes while maintaining overall system harmony.
Data Source
AI summary
A system and method are described for generating a model of an environment in which a plurality of equipment units are deployed for the extraction of at least one resource from the environment. The system comprises a pre-extraction modeling unit configured to receive data from a first plurality of heterogeneous sensors in the environment and to fuse the data into a pre-extraction model. An equipment modeling unit is configured to receive equipment data relating to the plurality of equipment units and to combine the equipment data into an equipment model. A post-extraction modeling unit is configured to receive data from a second plurality of sensors and to fuse the data into a post-extraction model. Information from the pre-extraction model, the equipment model and/or the post-extraction model is communicable to the equipment units for use in controlling operation of the equipment units in the environment.