Robot Cloud Computing Using Slug Trails
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud computing systems lack efficient methods for robots to share and utilize environmental information and task-related data across multiple locations, leading to suboptimal task execution and resource management.
Innovation Solution
A cloud computing system that enables robots to receive instructions and share data through a network of computing resources, using 'slug trails' to cache and associate relevant information for subsequent tasks, allowing for proactive planning and improved task execution by routing requests through multiple nodes and accessing environmental data from sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots process tasks through multiple computing resources in a cloud system, then task execution efficiency and adaptability are improved, but system complexity and data management difficulty increase
Solution Approach 1:
The patent introduces slug trails as intermediary data structures that mediate between multiple computing resources and robots. Slug trails cache and associate environmental information, task parameters, and computational results along the processing route, enabling efficient task execution across distributed resources while managing system complexity through standardized data association mechanisms.
Solution Approach 2:
The system performs preliminary actions by caching environmental information and task-related data along the processing route before tasks are executed. This preliminary data collection and association enables faster task execution and better adaptability without requiring real-time complex computations across all nodes.
2Adaptability or versatility
If environmental information is shared across multiple nodes, then robot adaptability and navigation accuracy improve, but information management complexity increases
Solution Approach 1:
Slug trails serve as intermediary structures that standardize environmental information sharing between nodes. They associate location data, sensor information, and task parameters in a consistent format, enabling robots to adapt to different environments without increasing information management complexity through standardized association protocols.
Solution Approach 2:
The slug trail structure provides universal functionality across different nodes and robot types. The same data association mechanism works for various tasks and locations, enabling broad adaptability while maintaining simple, unified information management procedures through standardized interfaces.
3Speed
If real-time data sharing is enabled between robots, then task execution speed increases, but network bandwidth and processing resources are consumed
Solution Approach 1:
The system performs preliminary data caching along the processing route, storing environmental information and task parameters in slug trails before they are needed for task execution. This reduces the need for continuous real-time data retrieval and sharing, decreasing network bandwidth consumption and processing resource usage while maintaining fast task execution speeds.
Solution Approach 2:
The patent extracts essential environmental information and task parameters from the broader data stream and encapsulates them in slug trails. This extraction allows only the necessary data to be shared between nodes, reducing network traffic and processing overhead while maintaining the speed of task execution through efficient data targeting.
Data Source
AI summary
A method includes receiving a first request for a first robot to perform a first task and processing the first request through first and second computing resources to identify instructions for the first robot to perform the first task. The method also includes providing the one or more instructions to the first robot and identifying information associated with the second computing resource, wherein such information includes environmental information related to a location where the first task is to be performed. The method further includes associating the identified information with the first computing resource, receiving a second request for a second robot to perform a second task, processing the second request through the first computing resource, accessing the information associated with the first computing resource, and, based upon the accessed information, providing instructions to the second robot to perform the second task.