Cloud Sensor Data Correlation for Robot Task Execution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud computing systems face challenges in effectively processing and synchronizing sensor data from multiple sources, such as robots, to enhance task execution and object recognition, particularly in environments where data is acquired at different times and locations.
Innovation Solution
A method is implemented in a cloud computing system to acquire and process sensor data with associated attributes like time and location, allowing for localization and synchronization of data from various sensors, enabling enhanced data processing and task execution by correlating and combining data attributes to improve object recognition and inventorying tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple sources is collected to improve object recognition and task execution, then data completeness and accuracy are improved, but data synchronization and coordination become more difficult
Solution Approach 1:
The patent introduces a cloud computing system as an intermediary that receives, processes, and synchronizes sensor data from multiple robots and sensors. The cloud system acts as a central coordinator that manages the complexity of data integration, allowing individual robots to focus on their specific tasks while the cloud handles the sophisticated coordination and correlation of data from multiple sources.
Solution Approach 2:
The system divides the data processing task into segments: individual sensors and robots collect data locally, then transmit to the cloud system which performs the complex synchronization and correlation operations. This segmentation allows each component to operate independently at its own level while contributing to the overall system goal, reducing the complexity burden on individual devices.
2Productivity
If sensor data is collected at different times and locations to provide comprehensive task execution information, then task execution effectiveness is improved, but data coordination and correlation become more challenging
Solution Approach 1:
The cloud computing system serves as an intermediary that receives sensor data with associated time and location attributes from multiple sources, then performs sophisticated coordination and correlation operations to integrate this spatiotemporally distributed data into a coherent framework that enables effective task execution.
Solution Approach 2:
The system handles multi-dimensional sensor data by incorporating time and location as additional dimensions beyond the basic sensor measurements. The cloud system correlates data across these multiple dimensions, transforming complex multi-dimensional data into actionable information for task execution.
3Adaptability or versatility
If multiple sensors and robots are coordinated to perform tasks, then task completion capability is improved, but system complexity increases
Solution Approach 1:
The cloud computing system provides universal functionality by serving multiple robots and sensors simultaneously, handling data collection, synchronization, correlation, and task coordination in a single centralized platform. This multi-functional approach allows the system to manage diverse sensors and robots without requiring separate coordination mechanisms for each.
Solution Approach 2:
The cloud system acts as an intermediary layer between individual robots/sensors and the overall task coordination, absorbing the complexity of managing multiple devices while presenting a simplified interface for task execution. Individual robots interact with the cloud system rather than directly with each other, reducing overall system complexity.
Data Source
AI summary
A method includes receiving first sensor data acquired by a first sensor in communication with a cloud computing system. The first sensor data has a first set of associated attributes including a time and a location at which the first sensor data was acquired. The method also includes receiving second sensor data acquired by a second sensor in communication with the cloud computing system. The second data has a second set of associated attributes including a time and a location at which the second sensor data was acquire. Further, the method includes generating a data processing result based at least in part on the first sensor data, the first set of associated attributes, the second sensor data, and the second set of associated attributes and instructing a robot in communication with the cloud computing system to perform a task based at least in part on the data processing result.