Multi-Robot Part Exchange Using Digital Twins for Fault Recovery
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Solution Overview
Problem
Existing multi-robotic systems face challenges in maximizing collaborative effectiveness due to issues such as malfunctioning sensors or modules, which affect the overall performance and efficiency of the robotic ecosystem.
Innovation Solution
A computer-implemented method utilizing digital twin models to simulate scenarios of exchanging inter-exchangeable parts among robots, identifying an optimum scenario for maximizing collaborative effectiveness through cost-benefit analysis, and instructing robots to perform physical exchanges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots perform activities in a multi-robotic ecosystem with fixed configurations, then system stability is maintained, but collaborative effectiveness decreases when sensors or modules malfunction
Solution Approach 1:
The system implements dynamic reconfiguration by enabling robots to exchange inter-exchangeable parts (sensors, modules, grippers) during operation. Digital twin models simulate various configuration scenarios to determine optimal assignments that maximize collaborative effectiveness when malfunctions occur, allowing the system to adapt from static to dynamic configurations in response to changing conditions
Solution Approach 2:
The system changes the operational parameters of the multi-robotic ecosystem by modifying which robot possesses which sensor or module. Through cost-benefit analysis of different assignment scenarios using digital twin simulations, the system optimizes the distribution of inter-exchangeable parts across robots to improve collaborative effectiveness while accounting for malfunction conditions
2Reliability
If digital twin simulations are performed to identify optimum exchange scenarios, then collaborative effectiveness is maximized, but computational complexity and time increase
Solution Approach 1:
The system segments the complex optimization problem into manageable components by creating digital twin models for individual robots and their inter-exchangeable parts. Each robot's capabilities, sensor performances, and module characteristics are modeled separately, allowing the system to simulate and evaluate different exchange scenarios through combinatorial assignments of segmented components
Solution Approach 2:
The system creates digital twin copies of physical robots and their components to simulate exchange scenarios without affecting actual operations. These virtual models replicate the behavioral and performance characteristics of physical robots, enabling comprehensive scenario evaluation while isolating the complexity from the real system
3Adaptability or versatility
If physical exchange of inter-exchangeable parts is performed among robots, then system adaptability improves, but time and operational disruption increase
Solution Approach 1:
The system performs preliminary digital twin simulations to identify the optimum exchange scenario before executing physical exchanges. By pre-evaluating all possible assignments of inter-exchangeable parts among robots and selecting the scenario that maximizes collaborative effectiveness, the system minimizes trial-and-error physical exchanges and reduces the time required for reconfiguration
Solution Approach 2:
The system introduces digital twin models as an intermediary between the decision to exchange parts and the actual physical exchange. The digital twins serve as a virtual testing ground that mediates the optimization process, allowing the system to determine the optimal exchange plan without immediate physical disruption, thereby reducing actual exchange time
4Productivity
If cost-benefit analysis is performed for each scenario, then optimal resource allocation is achieved, but computational load increases
Solution Approach 1:
The system performs cost-benefit analysis selectively rather than exhaustively for all possible scenarios. By using digital twin simulations to evaluate representative scenarios and applying heuristics to prune obviously suboptimal assignments, the system achieves sufficient resource allocation efficiency while reducing computational energy consumption compared to exhaustive enumeration of all possible exchanges
Data Source
AI summary
A computer-implemented method, a computer program product, and a computer system for maximizing collaborative effectiveness among multi-robots with dynamic inter-exchangeability. A computer identifies inter-exchangeable parts among robots performing an activity in a multi-robotic ecosystem. A computer uses digital twin models to simulate scenarios of combining respective ones of the inter-exchangeable parts and respective ones of the robots. A computer identifies an optimum scenario in which collaborative effectiveness is maximized by exchanging the inter-exchangeable parts among the robots, based on results of digital twin model simulations. A computer, for the optimum scenario, identifies among the robots first robots whose inter-exchangeable parts are to be exchanged and one or more second robots that help the first robots exchange the inter-exchangeable parts. A computer instructs the first robots and the one or more second robots to perform physical exchange of the inter-exchangeable parts of the first robots.