Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control
Using a brain-computer interface (BCI) rather than limbs to control multiple robots (i.e., brain-controlled multi-robots) can better assist people with disabilities in daily life than a brain-controlled single robot. For example, one person with disabilities can move by a brain-controlled wheelchair...
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sg-ntu-dr.10356-1737232024-03-01T15:36:25Z Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control Yang, Zhenge Bi, Luzheng Chi, Weiming Shi, Haonan Guan, Cuntai School of Computer Science and Engineering Computer and Information Science Brain-computer interface Human-machine collaboration Using a brain-computer interface (BCI) rather than limbs to control multiple robots (i.e., brain-controlled multi-robots) can better assist people with disabilities in daily life than a brain-controlled single robot. For example, one person with disabilities can move by a brain-controlled wheelchair (leader robot) and simultaneously transport objects by follower robots. In this paper, we explore how to control the direction, speed, and formation of a brain-controlled multi-robot system (consisting of leader and follower robots) for the first time and propose a novel multi-robot predictive control framework (MRPCF) that can track users' control intents and ensure the safety of multiple robots. The MRPCF consists of the leader controller, follower controller, and formation planner. We build a whole brain-controlled multi-robot physical system for the first time and test the proposed system through human-in-the-loop actual experiments. The experimental results indicate that the proposed system can track users' direction, speed, and formation control intents when guaranteeing multiple robots' safety. This paper can promote the study of brain-controlled robots and multi-robot systems and provide some novel views into human-machine collaboration and integration. Published version This work was supported by the National Natural Science Foundation of China (No. 51975052). 2024-02-26T01:15:26Z 2024-02-26T01:15:26Z 2022 Journal Article Yang, Z., Bi, L., Chi, W., Shi, H. & Guan, C. (2022). Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control. Complex System Modeling and Simulation, 2(4), 307-321. https://dx.doi.org/10.23919/CSMS.2022.0019 2096-9929 https://hdl.handle.net/10356/173723 10.23919/CSMS.2022.0019 2-s2.0-85147176971 4 2 307 321 en Complex System Modeling and Simulation © 2022 The author(s). The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). application/pdf |
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Computer and Information Science Brain-computer interface Human-machine collaboration Yang, Zhenge Bi, Luzheng Chi, Weiming Shi, Haonan Guan, Cuntai Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control |
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Using a brain-computer interface (BCI) rather than limbs to control multiple robots (i.e., brain-controlled multi-robots) can better assist people with disabilities in daily life than a brain-controlled single robot. For example, one person with disabilities can move by a brain-controlled wheelchair (leader robot) and simultaneously transport objects by follower robots. In this paper, we explore how to control the direction, speed, and formation of a brain-controlled multi-robot system (consisting of leader and follower robots) for the first time and propose a novel multi-robot predictive control framework (MRPCF) that can track users' control intents and ensure the safety of multiple robots. The MRPCF consists of the leader controller, follower controller, and formation planner. We build a whole brain-controlled multi-robot physical system for the first time and test the proposed system through human-in-the-loop actual experiments. The experimental results indicate that the proposed system can track users' direction, speed, and formation control intents when guaranteeing multiple robots' safety. This paper can promote the study of brain-controlled robots and multi-robot systems and provide some novel views into human-machine collaboration and integration. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Yang, Zhenge Bi, Luzheng Chi, Weiming Shi, Haonan Guan, Cuntai |
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Article |
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Yang, Zhenge Bi, Luzheng Chi, Weiming Shi, Haonan Guan, Cuntai |
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Yang, Zhenge |
title |
Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control |
title_short |
Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control |
title_full |
Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control |
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Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control |
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Brain-controlled multi-robot at servo-control level based on nonlinear model predictive control |
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brain-controlled multi-robot at servo-control level based on nonlinear model predictive control |
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2024 |
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https://hdl.handle.net/10356/173723 |
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