Distributed centroid estimation and motion controllers for collective transport by multi-robot systems

Habibi, Golnaz; Kingston, Zachary; Xie, William; Jellins, Mathew; McLurkin, James

doi:10.1109/icra.2015.7139356

Cited by 44 publications

(26 citation statements)

References 19 publications

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“…Alonso-Mora et al [24] presented a constrained optimization method for multi-robot formation control in dynamic environments. Habibi et al [25,26] presented a scalable distributed path planning algorithm for transporting large objects through unknown environments using a group of homogeneous robots. Lippi et al [27] studied the modeling and planning problems of a system composed of multiple ground and aerial robots involved in a transportation task.…”

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confidence: 99%

Kinematic Modeling of a Combined System of Multiple Mecanum-Wheeled Robots with Velocity Compensation

Dai

et al. 2019

Sensors

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In industry, combination configurations composed of multiple Mecanum-wheeled mobile robots are adopted to transport large-scale objects. In this paper, a kinematic model with velocity compensation of the combined mobile system is created, aimed to provide a theoretical kinematic basis for accurate motion control. Motion simulations of a single four-Mecanum-wheeled virtual robot prototype on RecurDyn and motion tests of a robot physical prototype are carried out, and the motions of a variety of combined mobile configurations are also simulated. Motion simulation and test results prove that the kinematic models of single-and multiple-robot combination systems are correct, and the inverse kinematic correction model with velocity compensation matrix is feasible. Through simulations or experiments, the velocity compensation coefficients of the robots can be measured and the velocity compensation matrix can be created. This modified inverse kinematic model can effectively reduce the errors of robot motion caused by wheel slippage and improve the motion accuracy of the mobile robot system.Sensors 2020, 20, 75 2 of 37 robot called MC-Drive, and the MC-Drive TP200 robot has been used to carry aircraft at Airbus manufacturing plants [13,14]. The KUKA omniMove UTV-2 set is a heavy-duty mobile platform with 12 Mecanum wheels that can be used to carry large objects [15]. (2) Using a combination of multiple Mecanum-wheeled robot platforms, which can be considered as a whole with cooperative omnidirectional motion and transport. Usually, mobile platforms used for cooperative transportation are symmetrically arranged. For example, a railcar body can be carried cooperatively by four KUKA omniMove mobile platforms at the Siemens plant in Krefeld, Germany [16]. The four mobile platforms are symmetrically arranged at four corners of the railcar body. Omni-directional mobile platforms with 8, 12, 16, or 32 Mecanum wheels can also be considered as specific combinations of multiple basic mobile platforms.The mature basic theory of four-Mecanum-wheeled robots is the basis of research on multi-robot systems. Muir [17][18][19] carried out basic research on Mecanum-wheeled robots and developed a kinematic and dynamic model and control on a four-Mecanum-wheeled robot. Campion et al. [20] studied structural properties and classification of kinematic and dynamic models of mobile wheeled robots and derived a motion constraint equation of a Mecanum wheel that can be used in kinematic research of multiple Mecanum-wheeled robots. Robots with four or more Mecanum wheels are overactuated systems with one or more motion constraints (wheel velocities are linearly correlated). Every additional wheel, and every additional robot, adds new motion constraints. So, the motion constraints of a multiple-Mecanum-wheeled robot system can be developed [21], which is also valid for multi-robot systems. The problem of transporting objects with a combined system is also a typical cooperative object transport problem in multi-robot systems, which is a growing rese...

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Kinematic Modeling of a Combined System of Multiple Mecanum-Wheeled Robots with Velocity Compensation

Dai

et al. 2019

Sensors

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“…This identification is not that easy because different robots in the scene may performed close and symmetric movements. This process is however necessary for many applications like exploration [4] or transportation [5].…”

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confidence: 99%

Self-localization of Anonymous Mobile Robots from Aerial Images

Poulet¹,

Guérin²,

Guinand³

2018

2018 European Control Conference (ECC)

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This paper presents three methods for anonymous mobile robots localization within a global frame. An aerial camera takes, at regular time intervals, pictures of the area in which robots are moving. The camera determines the coordinates of each robot. Each robot receives the whole set of coordinates extracted from each picture. Mobile Robots are all identical, they do not have any identifier and they can neither communicate with each other nor they can detect themselves. The first localization method is based on the analysis of the angular variation between two images. The second method relies on the analysis of the distances stemmed from three successive pictures. The last one determines if there exists an orientation allowing a specific robot to travel the path between two successive positions. A simulation plateform using augmented reality and the multi-robot software Player-Stage are presented. This plateform is used for validating the different localization methods. Tests and results are presented and compared. CONFIDENTIAL. Limited circulation. For review only.

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“…Li e Liu [6] abordaram o problema de controle de robôs quadrúpedes, e Habibi et al [5] trabalharam com o problema de transporte de um objeto por múltiplos robôs trabalhando coletivamente. Duarte et al [3] desenvolveram controles para robôs aquáticos realizarem algumas tarefas, utilizando redes neurais geradas a partir do algoritmo evolutivo NEAT (Neuroevolution of Augmenting Topologies) [8].…”

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Controle baseado em redes neurais artificiais para agentes móveis em formação

Freitas¹,

Quiles²,

Macau³

2018

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo. Redes neurais artificiais são utilizadas em diversos contextos, como classificação, controle inteligente, recuperação de informação, previsão do tempo, entre outros. O presente trabalho apresenta um modelo de agentes móveis reativos, que utiliza um controle baseado em redes neurais artificiais. O objetivo do modeloé fazer com que um grupo de agentes siga uma referência móvel e evite colisões com os agentes vizinhos. A criação do controle se deu via utilização de um algoritmo de otimização evolutivo, para redes neurais artificiais.

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Distributed centroid estimation and motion controllers for collective transport by multi-robot systems

Cited by 44 publications

References 19 publications

Kinematic Modeling of a Combined System of Multiple Mecanum-Wheeled Robots with Velocity Compensation

Kinematic Modeling of a Combined System of Multiple Mecanum-Wheeled Robots with Velocity Compensation

Self-localization of Anonymous Mobile Robots from Aerial Images

Controle baseado em redes neurais artificiais para agentes móveis em formação

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