Communication-based Decentralized Cooperative Object Transportation Using Nonlinear Model Predictive Control

Verginis, Christos K.; Νίκου, Αλέξανδρος; Dimarogonas, Dimos V.

doi:10.23919/ecc.2018.8550305

Cited by 25 publications

(28 citation statements)

References 25 publications

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“…Model-based force-control control protocols with unilateral constraints are developed in [30], [31]. Formation control approaches are considered in [31], [32] and a navigation-function scheme is used in [33]; [34] includes hybrid control with intermittent contacts and in our previous works [35], [36] we considered MPC approaches for cooperative object transportation. Finally, internal force and load distribution analysis in cooperative manipulation tasks is performed in a variety of works (e.g., [37]- [41]).…”

Section: Introductionmentioning

confidence: 99%

Robust Cooperative Manipulation Without Force/Torque Measurements: Control Design and Experiments

Verginis

Mastellaro

Dimarogonas

2020

IEEE Trans. Contr. Syst. Technol.

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This paper presents two novel control methodologies for the cooperative manipulation of an object by N robotic agents. Firstly, we design an adaptive control protocol which employs quaternion feedback for the object orientation to avoid potential representation singularities. Secondly, we propose a control protocol that guarantees predefined transient and steadystate performance for the object trajectory. Both methodologies are decentralized, since the agents calculate their own signals without communicating with each other, as well as robust to external disturbances and model uncertainties. Moreover, we consider that the grasping points are rigid, and avoid the need for force/torque measurements. Load distribution is also included via a grasp matrix pseudo-inverse to account for potential differences in the agents' power capabilities. Finally, simulation and experimental results with two robotic arms verify the theoretical findings.

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Section: Introductionmentioning

confidence: 99%

Robust Cooperative Manipulation Without Force/Torque Measurements: Control Design and Experiments

Verginis

Mastellaro

Dimarogonas

2020

IEEE Trans. Contr. Syst. Technol.

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“…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. Verginis et al [28] studied the problem of cooperative transportation of objects rigidly grasped by N robotic agents and presented the communication-based decentralized cooperative object transportation using nonlinear model predictive control. Tsai et al [29] presented a decentralized cooperative transportation control method with obstacle avoidance using fuzzy wavelet neural networks and a consensus algorithm for a group of Mecanum-wheeled omnidirectional robots with uncertainties, in order to move a large payload together.…”

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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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“…The obstacles are modeled as spheres (1 m radius) and are located in the workspace in order to complicate the transportation task of the object. In this respect, a Navigation Function is constructed following (13) in order to handle the aforementioned constrained workspace. Since, only the leading UVMS (blue) is aware of the object's desired configuration, the followers will estimate it via the proposed algorithm (43), by simply observing the motion of the object and without communicating explicitly with the leader.…”

Section: Estimation Schemementioning

confidence: 99%

Decentralized Impedance Control for Cooperative Manipulation of Multiple Underwater Vehicle Manipulator Systems under Lean Communication

Heshmati-alamdari

Bechlioulis

Karras

et al. 2018

2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)

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This paper addresses the problem of cooperative object transportation for multiple Underwater Vehicle Manipulator Systems (UVMSs) in a constrained workspace with static obstacles, where the coordination relies solely on implicit communication arising from the physical interaction of the robots with the commonly grasped object. We propose a novel distributed leader-follower architecture, where the leading UVMS, which has knowledge of the object's desired trajectory, tries to achieve the desired tracking behavior via an impedance control law, navigating in this way, the overall formation towards the goal configuration while avoiding collisions with the obstacles. On the other hand, the following UVMSs estimate the object's desired trajectory via a novel prescribed performance estimation law and implement a similar impedance control law. The feedback relies on each UVMS's force/torque measurements and no explicit data is exchanged online among the robots. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. Finally, various simulation studies clarify the proposed method and verify its efficiency.Index Terms-Underwater Vehicle Manipulator System, Cooperative Manipulation, Implicit communication.

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Communication-based Decentralized Cooperative Object Transportation Using Nonlinear Model Predictive Control

Cited by 25 publications

References 25 publications

Robust Cooperative Manipulation Without Force/Torque Measurements: Control Design and Experiments

Robust Cooperative Manipulation Without Force/Torque Measurements: Control Design and Experiments

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

Decentralized Impedance Control for Cooperative Manipulation of Multiple Underwater Vehicle Manipulator Systems under Lean Communication

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