Learning-based Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats
Name
IROS_2019.pdf
Description
Accepted version
Size
2.54 MB
Format
Adobe PDF
Checksum (MD5)
b1f0d812b38a0ef7a01e552c03b0fe27
Author(s)
Kayacan, Erkan
Park, Shinkyu
Ratti, Carlo
Rus, Daniela
Date Issued
November 2019
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
IEEE
Citation
Kayacan, Erkan, Park, Shinkyu, Ratti, Carlo and Rus, Daniela. 2019. "Learning-based Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats." IEEE International Conference on Intelligent Robots and Systems.
Version
Author's final manuscript
Abstract
© 2019 IEEE. This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm for reconfigurable autonomous vessels to facilitate high-accurate path tracking. Each vessel is designed to latch to a pre-defined point of another vessel that allows the vessels to form a rigid body. The number of possible configurations of such vessels exponentially grows as the total number of vessels increases, which imposes a technical challenge in modeling and identification. In this work, we propose a framework consisting of a real-time parameter estimator and a feedback control strategy, which is capable of ensuring high-accurate path tracking for any feasible configuration of vessels. Novelty of our method is in that the parameter is estimated on-line and adjusts control parameters (e.g., cost function and dynamic model) simultaneously to improve path-tracking performance. Through experiments on different configurations of connected-vessels, we demonstrate stability of our proposed approach and its effectiveness in high-accuracy in path tracking.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Senseable City Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros40897.2019.8967525
