A framework for analyzing the accuracy, complexity, and long-term performance 
of cable-driven parallel robot models

Fabritius, M; Miermeister, P; Kraus, W; Pott, A

doi:10.1016/j.mechmachtheory.2023.105331

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A framework for analyzing the accuracy, complexity, and long-term performance of cable-driven parallel robot models

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Miermeister, P
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.sciencedirect.com/science/article/pii/S0094114X23001027
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Citation

Fabritius, M., Miermeister, P., Kraus, W., & Pott, A. (2023). A framework for analyzing the accuracy, complexity, and long-term performance of cable-driven parallel robot models. Mechanism and Machine Theory, 185: 105331. doi:10.1016/j.mechmachtheory.2023.105331.

Cite as: https://hdl.handle.net/21.11116/0000-000C-E01C-8

Abstract

Cable-driven parallel robots require accurate and reliable control models to satisfy the demands of their diverse applications. The models’ usefulness in practice depends on their structure, the considered effects, and the ability to determine their best parameter values. This work establishes a unified model framework, containing eleven of the most commonly used cable robot models. The framework can optimize, evaluate, and compare models based on measurement datasets that capture the behavior of the robot. The models are evaluated based on their accuracy in the platform pose and the cable forces. Their computational costs, optimization process indicators, and the influence of the dataset size are also studied. This analysis is exercised for a large-scale cable robot based on two measurement datasets that are recorded 1.5 months apart and cover different regions of the robot’s state–space. The results show that a catenary model with force offsets offers the best overall accuracy for the given cable robot. While the models’ relative behavior is consistent in both datasets, their accuracy is significantly reduced on the second dataset due to long-term effects acting on the robot.