Distributed trajectory generation for cooperative multi-arm robots via virtual force interactions

Tsuji, Toshio; Jazidie, Achmad; Kaneko, Makoto

doi:10.1109/3477.623238

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…The current adopted by different smart motors will flow through the front controller; therefore, it is only necessary to take the waveforms of each frequency, and the basic driving features of each smart motor can be analyzed using Eqs. (1) to (4). The traditional PWM technology does not have the communication capability, but it is widely used in the drive control of heterogeneous motors.…”

Section: Communication Strategy From Smart Motor With Pkm Power Wavementioning

confidence: 99%

“…1, a collaborative drive must be achieved through interface communications such as Ethernet, EtherCAD, or RS232. (1)(2)(3)(4)(5)(6)(7) However, this hides the correctness and stability of the conversion of the control signal flow in different communication interfaces and the response time of the overall drive system. Another problem, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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A Smart Motor Driving System Using Pu’s Count Modulation Multichannel Communication

Pu¹,

Lin²,

Wu³

2020

Sensors and Materials

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In this paper, we propose a new-generation motor architecture (smart motor) constructed by first integrating a motor, driver, controller, and communication interface into a control unit, and then using Pu's count modulation (PKM) technology to replace the traditional pulse width modulation (PWM) technology to power a smart motor; the proposed motor architecture and PKM technology have a communication function. The purpose of this study is to virtualize the motor control through the design of the smart motor, while simultaneously driving the heterogeneous motor. However, the proposed PKM technology uses the masterservant communication architecture to transmit the message of only one smart motor at a time. Therefore, as the number of responding smart motors increases, the overall response time is directly increased; this indirectly affects the response sensitivity of the smart motor drive system. Another contribution of this paper is the proposal that the establishment of an ADALINE artificial neural network can facilitate a set of multichannel, parallel communication architectures for computing the signal from a sensor to reduce the overall communication time and increase system response capabilities; this system is called multichannel PKM (MPKM) technology. In the smart motor architecture, the drive system only needs power busbars, which can randomly increase or decrease the number of drive motors, simplify the drive program, control the power signal interference problem, and reduce the number and weight of control lines. At the end of this paper, the signal is analyzed using MATLAB/Simulink ® , and the microcontroller (MCU) ATMEL328PU is used as the control center. The hardware of the architecture system can be used to verify its feasibility.

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Section: Communication Strategy From Smart Motor With Pkm Power Wavementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Smart Motor Driving System Using Pu’s Count Modulation Multichannel Communication

Pu¹,

Lin²,

Wu³

2020

Sensors and Materials

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“…Cheah et al [4][5][6][7] × -Gudiño-Lau et al 8,9 × × × -Tsuji et al 10 × × -Lee et al 11,12 × × × -Sirouspour et al 13 × × × -Kawasaki et al 14 × -Tang et al 15 × × × -Sarikaya et al 16 × -Rastegari et al 17 × × -Tinós et al 18 × × × -Monfaredi et al 19 × Non-model-based controller R. Monfaredi et al 20 Non-model-based controller Mohajerpoor et al [21][22][23] × × -Current paper…”

Section: Unknown Objectmentioning

confidence: 99%

“…Some studies have considered handling of an object with known COM. [8][9][10] Cooperative handling of an object via three robots has been investigated by Lee et al, in which an object with known shape was considered. 11,12 Sirouspour developed a multi-master multi-slave (MMMS) cooperative telerobotic system in which the slave robots cooperatively manipulate a known tool to perform a task on an environment.…”

Section: Introductionmentioning

confidence: 99%

A new observer-based adaptive controller for cooperative handling of an unknown object

2014

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A new observer-based adaptive controller for cooperative handling of an unknown object. Robotica, SUMMARYThis paper presents a new observer-based adaptive controller for handling an object with unknown geometry, center of mass, and inertia using a cooperative robotic system. The cooperative robotic system comprises three Cartesian robots, where robots and the grasped object form a closed-loop kinematic chain. The unknown object is approximated by three virtual links of unknown lengths rigidly attached to one another at the object's center of mass (COM). Due to the unknown COM and unknown inertia of the object, the lengths and inertia of these virtual links are unknown, resulting in kinematic and dynamic uncertainties in the control system. A parameter estimator is proposed to estimate the object's COM to compensate for kinematic uncertainties of the system. Moreover, a new dynamic adaptation law is developed to cope with dynamic uncertainties of the object. The dynamic equations of the cooperative system are transformed from joint space into task space. These task space dynamics are transformed into object space by passively decomposing the dynamics into two decoupled systems, i.e. locked and shaped systems. An adaptive controller is developed for the locked system, and the shaped system is controlled by a composite controller based on a PD controller plus a stabilizing damping term. The stability of the proposed controllers is shown using the passivity concept and Lyapunov theorem. Simulation results show that the closed-loop position error asymptotically converges to zero. It is also shown that kinematic and dynamic adaptation parameters converge to real and bounded values respectively.

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“…The measures can be used to evaluate the manipulability of objects with various shapes, for path and trajectory synthesis, and for designing object shapes. Tsuji et al (1997) proposed a trajectory generation method for multi-arm robots through cooperative and competitive interactions among multiple end-effectors. The method can generate the trajectories of the multiple arms in a distributed manner based on a concept of a virtual interaction force which represents an interaction between an end-effector and an environment.…”

Section: Force and Position Controlmentioning

confidence: 99%

Robotic grasping: gripper designs, control methods and grasp configurations – a review of research

Boubekri

Chakraborty

2002

Integrated Manufacturing Systems

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

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Distributed trajectory generation for cooperative multi-arm robots via virtual force interactions

Cited by 12 publications

References 14 publications

A Smart Motor Driving System Using Pu’s Count Modulation Multichannel Communication

A Smart Motor Driving System Using Pu’s Count Modulation Multichannel Communication

A new observer-based adaptive controller for cooperative handling of an unknown object

Robotic grasping: gripper designs, control methods and grasp configurations – a review of research

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