Hybrid Force/Position Control of a Collaborative Parallel Robot Using Adaptive Neural Network

Zabihifar, Seyedhassan; Yuschenko, A.S.

doi:10.1007/978-3-319-99582-3_29

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…For instance, many haptic interfaces are made of parallel architectures [23]. In [24], the authors applied an hybrid force/position controller to a Delta robot in order to allow safe interactions. More recently in [25], a collaborative parallel robot for pick-and-place tasks has been proposed.…”

Section: Introductionmentioning

confidence: 99%

R-Min: a Fast Collaborative Underactuated Parallel Robot for Pick-and-Place Operations

Jeanneau

Begoc

Briot

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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This paper introduces an intrinsically safe parallel manipulator dedicated to fast pick-and-place operations, called R-Min. It has been designed to reduce the risk of injury during a collision with a human operator, while maintaining high speed and acceleration. The proposed architecture is based on a modification of the well-known planar five-bar mechanism, where additional passive joints are introduced to the distal links in order to create a planar seven-bar mechanism with two degrees of underactuation, so that it can passively reconfigure in case of collision. A supplementary passive leg, in which a tension spring is mounted, is added between the base and the end-effector in order to constrain the additional degrees of freedom.A prototype of this new collaborative parallel robot is designed and its equilibrium configurations under several types of loadings are analyzed. Its dynamics is also studied. We analyze the impact force occurring during a collision between our prototype and the head of an operator and compare these results with those that would have been obtained with a rigid five-bar mechanism. Simulation results of impact during a standard pick-and-place trajectory of duration 0.3 s show that a regular five-bar mechanism would injure a human, while our robot would avoid the trauma.

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Section: Introductionmentioning

confidence: 99%

R-Min: a Fast Collaborative Underactuated Parallel Robot for Pick-and-Place Operations

Jeanneau

Begoc

Briot

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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“…where λ > 0 is a positive definite design parameter. By computing its time derivative, one can obtaiṅ r =ë 0 +ë 1 + λė 1 (10) substituting ( 6) and ( 8) into (10) and simplifyinġ…”

Section: System Dynamicsmentioning

confidence: 99%

“…Based on this universal approximation property, many important adaptive neural network-based control schemes have been developed to solve highly nonlinear control problems including many industrial applications for the tracking control of robot manipulators with uncertainties. 9,10 The adaptive neural network has been applied to achieve control goals due to their approximation property, and the sliding surface brings us robustness property. Regarding the stabilization problem, neural networks have been used in various pendulum-type systems.…”

Section: Introductionmentioning

confidence: 99%

Dual Adaptive Neural Network Controller for Underactuated Systems

Zabihifar¹,

Navvabi²,

Yushchenko³

2021

Robotica

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SUMMARY A new stable adaptive controller based on a neural network for underactuated systems is proposed in this paper. The control scheme has been developed for two underactuated systems as examples. The Furuta pendulum and the Inertia Wheel Pendulum (IWP) have been examined in this paper. The presented approach aims to address the control problem of the given system in swing up, stabilization, and disturbance rejection. To avoid oscillations, two adaptive neural networks (ANNs) are implemented. The first one is used to approximate the equivalent control online and the second one to minimize the oscillations.

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“…Combinations of the robot's components are carried out through blocks joint. The file containing all the mentioned components is described in xml language [22][23].…”

Section: Model Descriptionmentioning

confidence: 99%

Computer Simulation of a Group of Mobile Robots in Special Software Tools

Raskatov

Rubtsov

2020

ITM Web Conf.

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The problem of controlling a group of special robots for application in the high-risk conditions (for example, for fire extinguishing, search for hazardous substances, or elimination of consequences of emergencies) is solved. According to the task, robots operate in the industrial and urban environment, i.e., the movement of robots is planar. Various strategies of group interaction between robots are analyzed; a literature review is conducted. Due to the task specifics, a decentralized management strategy has been selected. For solving the problem, a movement algorithm of a group of mobile robots with a subsequent visual check of the resulting algorithm operation is proposed. Implementation of the algorithm’s work is performed in the MATLAB environment. For preliminary algorithm verification and modeling of various dangerous cases, a model of robot group using the Mobile Robotics Simulation Toolbox of MATLAB was made. For a more detailed and accurate analysis of the real robot’s behavior, a model in the ROS package has been developed. That model interacts with the developed algorithm through the ROS Toolbox package of MATLAB. This software can be used for the training of engineering staff in the field of mathematical modeling, group control, and multi-agent systems. As a result of verification, the program and the model successfully solve the task. Yet, the model in the ROS package requires greater computational power because of the large number of complex calculations.

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Hybrid Force/Position Control of a Collaborative Parallel Robot Using Adaptive Neural Network

Cited by 9 publications

References 9 publications

R-Min: a Fast Collaborative Underactuated Parallel Robot for Pick-and-Place Operations

R-Min: a Fast Collaborative Underactuated Parallel Robot for Pick-and-Place Operations

Dual Adaptive Neural Network Controller for Underactuated Systems

Computer Simulation of a Group of Mobile Robots in Special Software Tools

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