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PeriSim: A Simulator for Optimizing Peristaltic Table Control
PeriSim: A Simulator for Optimizing Peristaltic Table Control
Ross M. McKenzie
(School of Engineering Institute for Integrated Micro and Nano Systems Scottish Microelectronics Centre The University of Edinburgh Alexander Crum Brown Road, King's Buildings Edinburgh EH9 3FF UK)
Jamie O. Roberts
(School of Engineering Institute for Integrated Micro and Nano Systems Scottish Microelectronics Centre The University of Edinburgh Alexander Crum Brown Road, King's Buildings Edinburgh EH9 3FF UK)
Mohammed E. Sayed
(School of Engineering Institute for Integrated Micro and Nano Systems Scottish Microelectronics Centre The University of Edinburgh Alexander Crum Brown Road, King's Buildings Edinburgh EH9 3FF UK)
Adam A. Stokes
(School of Engineering Institute for Integrated Micro and Nano Systems Scottish Microelectronics Centre The University of Edinburgh Alexander Crum Brown Road, King's Buildings Edinburgh EH9 3FF UK)
Journal: Advanced Intelligent Systems
•
• Wiley
• ISSN: 2640-4567
Abstract
Peristaltic conveyance can be used for the sorting and transport of delicate and nonrigid objects such as meat or soft fruit. The non‐linearity and stochastic behavior of peristaltic systems make them difficult to control. Optimizing controllers using machine learning represents a promising path to effective peristaltic control but currently, there is no suitable simulated model of a peristaltic table in which to run these optimizations. A simple, simulated model of a peristaltic conveyor that can be used for optimizing peristaltic control on a variety of peristaltic tables is presented. This simulator is demonstrated through a limited control problem evaluated on our real‐world system that is built for peristaltic conveyance. This simulator is available as the python package PeriSim so that it can be used by the robotics community for peristaltic control development.
Ross M. McKenzie, Jamie O. Roberts, Mohammed E. Sayed, et al. (2019). "PeriSim: A Simulator for Optimizing Peristaltic Table Control". Advanced Intelligent Systems. https://doi.org/10.1002/aisy.201900070