Toward Predicting Collective Performance in Multirobot Teams

The increased deployment of multirobot systems (MRS) in various fields has led to the need to analyze system-level performance. However, creating consistent metrics for MRS is challenging due to the wide range of team and task parameters, such as the number of robots and the size of the environment....

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Veröffentlicht in:IEEE transactions on robotics Jg. 41; S. 5229 - 5245
Hauptverfasser: Xin, Pujie, Xie, Zhanteng, Dames, Philip
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
Big data in robotics and automation
dimen- sionless variable analysis
Measurement
Meters
Multi-robot systems
multirobot systems (MRS)
performance evaluation and benchmarking
Polynomials
Predictive models
Robot kinematics
Robot sensing systems
System performance
Training
Vectors
ISSN:1552-3098, 1941-0468
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Zusammenfassung:The increased deployment of multirobot systems (MRS) in various fields has led to the need to analyze system-level performance. However, creating consistent metrics for MRS is challenging due to the wide range of team and task parameters, such as the number of robots and the size of the environment. This article presents a new analytical framework for MRS based on dimensionless variable analysis that effectively condenses the complex relationships between the team and task parameters that influence MRS performance into a manageable set of dimensionless variables. Then, we use these dimensionless variables to fit a predictive parameteric model of team performance. We apply our methodology to two MRS applications: multirobot multitarget tracking and multiagent path finding. The application of dimensionless variable analysis to MRS offers a promising method for MRS analysis that effectively reduces complexity, improves understanding of system behavior, and can inform the design and management of future MRS deployments.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2025.3600164