Flexible Redundancy Model for IoRT Resource Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In Internet of Robotic Things (IoRT) environments, the reliability of task execution is compromised due to failures in robots, sensors, actuators, Cloud/Edge servers, and wireless networks, with traditional redundancy assignment schemes being cost-ineffective for budget-constrained deployments, leading to inefficiencies in resource management.
Innovation Solution
Implementing an At-most M Modular Flexible Redundancy Model that dynamically assigns redundancy based on resource availability and reliability, using a processor-driven method to manage resources by initializing redundancy parameters, computing resource reliability, generating priority lists, and strategically replacing failed resources with warm standbys to ensure task completion within constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional redundancy assignment schemes are used to ensure reliable task execution, then system reliability is improved, but resource usage costs increase significantly
Solution Approach 1:
The patent implements dynamic redundancy assignment where the redundancy level for each robot is adjusted based on real-time reliability requirements and resource availability. The system continuously monitors task criticality and robot performance, dynamically allocating redundant resources only where and when needed, rather than maintaining static redundancy across all robots. This dynamic approach resolves the contradiction by ensuring high reliability for critical tasks while minimizing unnecessary resource consumption for less critical operations.
Solution Approach 2:
The patent applies different redundancy strategies to different robots and tasks based on their specific reliability requirements and resource constraints. Each robot receives a customized redundancy assignment tailored to its role, the criticality of its tasks, and the available resources. This local differentiation allows the system to maintain high reliability for mission-critical robots while reducing redundancy for support functions, thereby resolving the contradiction between overall system reliability and total resource consumption.
2Ease of operation
If static assignment of redundant resources is implemented, then resource allocation is simplified, but resource wastage increases
Solution Approach 1:
The system transitions from static to dynamic resource allocation by continuously monitoring robot performance, task criticality, and resource availability. The redundancy assignment is automatically adjusted in real-time based on changing conditions, ensuring that redundant resources are activated only when actually needed. This dynamic approach maintains operational simplicity through automated decision-making while eliminating the resource wastage inherent in static assignment schemes.
Solution Approach 2:
The patent implements a self-managing resource allocation system where robots and the central controller automatically adjust redundancy levels based on real-time system state without requiring manual intervention. The system monitors its own performance and resource consumption, dynamically reallocating redundant resources as needed. This self-service mechanism maintains ease of operation while preventing resource wastage through automated, context-aware allocation decisions.
3Reliability
If more redundant resources are deployed to handle failures, then system reliability is improved, but deployment cost increases
Solution Approach 1:
The patent implements dynamic redundancy assignment that adjusts the level of redundancy based on real-time reliability requirements and resource availability. The system calculates the optimal redundancy level for each robot considering task criticality, robot performance history, and current resource constraints. This dynamic approach ensures adequate redundancy is deployed only where reliability concerns are most pressing, rather than uniformly across all robots, thereby resolving the contradiction between system reliability and deployment cost.
Solution Approach 2:
The system changes the redundancy parameter M for different robots based on their specific reliability requirements and the criticality of their tasks. By varying the redundancy level as a可调 parameter rather than using a fixed value for all robots, the system optimizes the balance between reliability and cost. Critical robots receive higher redundancy levels while less critical robots operate with minimal or no redundancy, resolving the contradiction through parameter differentiation.
Data Source
AI summary
Cloud robotics infrastructures generally support heterogeneous services that are offered by heterogeneous resources whose reliability or availability also varies widely with varying lifetime. For such systems, defining a static redundancy configuration for all services is difficult and often biased. Also, it is not feasible to define a redundancy configuration separately for each unique service. Therefore, in the present disclosure a trade-off between the two is ensured by providing At-most M-Modular Flexible Redundancy Model wherein an exact degree of redundancy is defined and is given to each service in a heterogeneous service environment and monitoring each task and subtask status to ensure that each subtask gets accomplished thereby enabling the tuning of the tradeoff between redundancy and cost and determining efficiency of the system by estimating number of resources utilized to complete specific subtask and comparing the resources utilization with the exact degree of redundancy defined.