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Cooperative learning control of uncertain nonholonomic wheeled mobile robots with state constraints. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06342-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Synchronization Approach to Formation Control of Mobile Robots from the Cluster Space Perspective. J INTELL ROBOT SYST 2021. [DOI: 10.1007/s10846-021-01495-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Cruz-Morales RD, Velasco-Villa M, Castro-Linares R, Palacios-Hernandez ER. Leader-Follower Formation for Nonholonomic Mobile Robots: Discrete-Time Approach. INT J ADV ROBOT SYST 2017. [DOI: 10.5772/62344] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
This paper presents a novel solution for the classical leader-follower formation problem considering the case of nonholonomic mobile robots. A formation control strategy is proposed in a discrete-time context by considering the exact discrete-time discretization of the non-linear continuous-time kinematic model of the vehicle. The geometric formation of the robots allows us to derive an alternative model that describes the time evolution of the relative distance and angle between the robots. These variables are obtained in real-time by a vision-based localization system on board, in which the follower robot is equipped with a Kinect device, together with a recognition board mounted on the leader robot. The boundedness of the relative position error is formally proven by considering a feedback law that is delayed by one sampling period of time. Numerical simulations and real-time experiments are presented to verify the performance of the control strategy.
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Affiliation(s)
- Raul Dali Cruz-Morales
- CINVESTAV-IPN, Departamento de Ingeniería Eléctrica, Sección de Mecatrónica, Ciudad de México, Mexico
| | - Martin Velasco-Villa
- CINVESTAV-IPN, Departamento de Ingeniería Eléctrica, Sección de Mecatrónica, Ciudad de México, Mexico
| | - Rafael Castro-Linares
- CINVESTAV-IPN, Departamento de Ingeniería Eléctrica, Sección de Mecatrónica, Ciudad de México, Mexico
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Integral terminal sliding mode formation control of non-holonomic robots using leader follower approach. ROBOTICA 2016. [DOI: 10.1017/s0263574716000230] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
SUMMARYMulti-robot formation control has become an important area of research due to its advantages and applications. This paper presents multi-robot formation control using a leader–follower approach without considering the leader's velocity information or estimation. The leader–follower formation is formulated by incorporating the model uncertainties and disturbances. A novel formation controller is presented using integral terminal sliding mode (ITSM) control, which drives the formation tracking error convergence to zero in finite-time. The stability of the close-loop control scheme is verified by using Lyapunov theory. Furthermore, obstacle detection and avoidance are incorporated to avoid collision while maintaining the formation. The effectiveness of the proposed controller is verified and validated using sine and lamniscate curve trajectories. Moreover, the performance of the proposed ITSM formation controller is compared with the standard linear sliding mode (LSM) control.
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Dierks T, Jagannathan S. Asymptotic Adaptive Neural Network Tracking Control of Nonholonomic Mobile Robot Formations. J INTELL ROBOT SYST 2009. [DOI: 10.1007/s10846-009-9336-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Dierks T, Jagannathan S. Neural Network Control of Mobile Robot Formations Using RISE Feedback. ACTA ACUST UNITED AC 2009; 39:332-47. [DOI: 10.1109/tsmcb.2008.2005122] [Citation(s) in RCA: 85] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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