Real-Time Formation Planning for Multirobot Cooperation: A Neural Informatics Perspective
Wang, Tinglei; Hua, Cheng; Wang, Yufei; Cao, Xinwei; Liao, Bolin; Li, Shuai (2025-07-23)
IEEE
23.07.2025
T. Wang, C. Hua, Y. Wang, X. Cao, B. Liao and S. Li, "Real-Time Formation Planning for Multirobot Cooperation: A Neural Informatics Perspective," in IEEE Transactions on Industrial Electronics, vol. 72, no. 12, pp. 13703-13715, Dec. 2025, doi: 10.1109/TIE.2025.3579075
https://rightsstatements.org/vocab/InC/1.0/
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202605063015
https://urn.fi/URN:NBN:fi:oulu-202605063015
Tiivistelmä
Abstract
Multirobot formation planning has wide applications across various domains. This article proposes an innovative neural-controller-based approach to address the formation planning problem. The proposed noise-tolerant fixed-time zeroing neural network (NT-FTZNN) controller achieves convergence under constant noise, dynamic bounded noise, and even dynamic unbounded noise, demonstrating strong adaptability in complicated scenarios. To the best of our knowledge, this article is the first application of a neural controller with both noise robustness and fixed-time convergence properties to multirobot formation planning tasks. In the presence of various kinds of noises, the proposed method achieves a formation error on the order of 10−7, which significantly outperforms other advanced formation control methods that typically reach only the 10−2 level. Moreover, rigorous theoretical analysis proves that the proposed controller guarantees global stability and fixed-time convergence under various noise conditions. Extensive numerical simulations and physical experiments further validate the superiority of the proposed approach over existing methods, confirming its practical effectiveness in real-world multirobot formation tasks.
Multirobot formation planning has wide applications across various domains. This article proposes an innovative neural-controller-based approach to address the formation planning problem. The proposed noise-tolerant fixed-time zeroing neural network (NT-FTZNN) controller achieves convergence under constant noise, dynamic bounded noise, and even dynamic unbounded noise, demonstrating strong adaptability in complicated scenarios. To the best of our knowledge, this article is the first application of a neural controller with both noise robustness and fixed-time convergence properties to multirobot formation planning tasks. In the presence of various kinds of noises, the proposed method achieves a formation error on the order of 10−7, which significantly outperforms other advanced formation control methods that typically reach only the 10−2 level. Moreover, rigorous theoretical analysis proves that the proposed controller guarantees global stability and fixed-time convergence under various noise conditions. Extensive numerical simulations and physical experiments further validate the superiority of the proposed approach over existing methods, confirming its practical effectiveness in real-world multirobot formation tasks.
Kokoelmat
- Avoin saatavuus [43138]