AI Assignment of Robotic Edge Devices Under Latency Constraints
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Solution Overview
Problem
In edge computing ecosystems, robotic devices often face latency issues that disrupt communication and task completion due to their geographic location and communication range, leading to processing discontinuity and inefficiencies in task allocation.
Innovation Solution
A computer-implemented method and system that intelligently assigns robotic edge devices based on their attributes such as physical capabilities, edge computation latency, and communication range, using AI to determine the most suitable devices for a task and dynamically reassign them if needed to maintain efficient communication and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic edge devices are assigned to perform tasks based on traditional task allocation methods, then task completion can be achieved, but latency issues and communication disruptions occur due to geographic location and communication range limitations
Solution Approach 1:
The system dynamically changes assignment parameters by evaluating device attributes including communication range, latency metrics, and geographic location. The processor determines optimal device-task assignments by analyzing these parameters in real-time, assigning tasks to devices that maintain communication within acceptable latency thresholds while maximizing productivity
Solution Approach 2:
The patent replaces traditional manual or rule-based task allocation mechanisms with an AI-driven intelligent assignment system. The processor automatically evaluates device attributes and makes optimization decisions, substituting mechanical assignment methods with intelligent algorithms that consider communication constraints and device capabilities
2Area of stationary object
If robotic edge devices operate outside optimal communication ranges to access broader geographic locations, then task coverage area increases, but latency-induced processing discontinuity occurs
Solution Approach 1:
The system applies local quality by evaluating and optimizing communication conditions for each specific device-task assignment. The processor determines which devices can operate within acceptable communication ranges for specific tasks, matching device location capabilities with task requirements to maintain continuous processing while covering necessary geographic areas
3Device complexity
If manual task allocation methods are used for robotic edge devices, then system complexity is reduced, but adaptability to device attributes and communication constraints is poor
Solution Approach 1:
The intelligent assignment system enables self-service by automatically evaluating device attributes and making optimal task assignments without manual intervention. The processor independently determines which devices should receive which tasks based on communication range, latency, and device capabilities, allowing the system to adapt to changing conditions while maintaining manageable complexity
Data Source
AI summary
Provided is a computer-implemented method, system, and computer program product for intelligently assigning robotic edge devices to perform a task using an edge computing ecosystem. A processor may identify a plurality of robotic edge devices in a geographic location. The processor may determine attributes for each robotic edge device of the plurality of robotic edge devices. The processor may identify a task to be performed at the geographic location by the plurality of robotic edge devices. The processor may determine, based on the attributes, a subset of robotic edge devices that are capable of completing the task. The processor may assign the subset of robotic edge devices to complete the task.