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research-article

Wrench Uncertainty Quantification and Reconfiguration Analysis in Loosely Interconnected Cooperative Systems

[+] Author and Article Information
Javad Sovizi

Department of Mechanical Engineering, University at Buffalo, Buffalo, NY 14260
javadsov@buffalo.edu

Rahul Rai

Department of Mechanical Engineering, University at Buffalo, Buffalo, NY 14260
rahulrai@buffalo.edu

Venkat Krovi

Department of Mechanical Engineering, University at Buffalo, Buffalo, NY 14260
vkrovi@buffalo.edu

1Corresponding author.

ASME doi:10.1115/1.4037122 History: Received August 30, 2016; Revised June 05, 2017

Abstract

Loosely-interconnected cooperative systems such as cable robots are particularly susceptible to uncertainty. Such uncertainty is exacerbated by addition of the base mobility to realize reconfigurability within the system. However, it also sets the ground for predictive base reconfiguration in order to reduce the uncertainty level in system response. To this end, in this paper we systematically quantify the output wrench uncertainty based on which a base reconfiguration scheme is proposed to reduce the uncertainty level for a given task (uncertainty manipulation). Variations in tension and orientation of the cables are considered as the primary sources of the uncertainty responsible for non-deterministic wrench output on the platform. For non-optimal designs/configurations, this may require complex control structures or lead to system instability. The force vector corresponding to each agent (e.g., pulley and cable) is modeled as random vector whose magnitude and orientation are modeled as random variables with Gaussian and von Mises distributions, respectively. In a probabilistic framework, we develop the closed-form expressions of the mean and variances of the output force and moment given the current state (tension and orientation of the cables) of the system. This is intended to enable the designer to efficiently characterize an optimal configuration (location) of the bases in order to reduce the overall wrench fluctuations for a specific task. Numerical simulations as well as real experiments with multiple iRobots are performed to demonstrate the effectiveness of the proposed approach.

Copyright © 2017 by ASME
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