• Journal of Drive and Control
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• v.17 no.4
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• pp.31-37
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• 2020

We are concerned with the dual-arm manipulation for disaster-responding special-purpose machinery. This paper presents a control strategy for performing complex work in an irregular environment, the control algorithm, the hydraulic circuit, and the master devices. The occurrence of collapse accidents at disaster sites such as natural disasters and building collapses is increasing, which is emerging as a social problem. In particular, for the initial response, various tasks must be performed in an irregular environment. The Marionette algorithm for intuitive control of 'as if the operator's arm is moving' was presented as a control strategy for dual-arm manipulators with attachments and the prototype. Next, the hydraulic circuit, control system, and wearable-type master device presented to implement the Marionette algorithm were explained and verified through an experiment in which rebar-cutting, drum-lifting, and lifting a bottle with one arm and pouring the water into the bucket with the other arm were tested.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.3
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• pp.136-148
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• 2017

The paper presents dual arm ROV manipulation using deep reinforcement learning. The purpose of this underwater manipulator is to investigate and excavate natural resources in ocean, finding lost aircraft blackboxes and for performing other extremely dangerous tasks without endangering humans. This research work emphasizes on a self-learning approach using Deep Reinforcement Learning (DRL). DRL technique allows ROV to learn the policy of performing manipulation task directly, from raw image data. Our proposed architecture maps the visual inputs (images) to control actions (output) and get reward after each action, which allows an agent to learn manipulation skill through trial and error method. We have trained our network in simulation. The raw images and rewards are directly provided by our simple Lua simulator. Our simulator achieve accuracy by considering underwater dynamic environmental conditions. Major goal of this research is to provide a smart self-learning way to achieve manipulation in highly dynamic underwater environment. The results showed that a dual robotic arm trained for a 3DOF movement successfully achieved target reaching task in a 2D space by considering real environmental factor.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.1
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• pp.35-47
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• 2004

In this paper presents a new approach to the design and real-time implementation of fuzzy control system based-on digital signal processors(DSP:IMS320C80) in order to improve the precision and robustness for system of industrial robot(Dual-Arm with 8 joint Robot). The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The IMS320C80 is used in implementing real time fuzzy control to provide an enhanced motion control for robot manipulators. In this paper, a Self-Organizing Fuzzy Controller(SOFC) for the industrial robot manipulator with a actuator located at the base is studied. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a FLC(Fuzzy Logic Controller), one of the most difficult problems is the determination of linguistic control rules from the human operators. To overcome this difficult SOFC is proposed for a hierarchical control structure consisting of basic and high levels that modify control rules. The proposed SOFC scheme is simple in structure, Int in computation, and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for a Dual-Arm robot with eight joints.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1223-1232
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• 2004

We present a new technique to the design and real-time implementation of fuzzy control system basedon digital signal processors in order to improve the precision and robustness for system of industrial robot in this paper. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C80 is used in implementing real time fuzzy control to provide an enhanced motion control for robot manipulators. In this paper, a Self-Organizing Fuzzy Controller for the industrial robot manipulator with a actuator located at the base is studied. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a Fuzzy Logic Controller, one of the most difficult problems is the determination of linguistic control rules from the human operators. To overcome this difficult Self-Organizing Fuzzy Controller is proposed for a hierarchical control structure consisting of basic and high levels that modify control rules. The proposed Self-Organizing Fuzzy Controller scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for a Dual-Arm robot with eight joints.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.16-24
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• 2022

This paper presents a method that can reduce the computational cost of the hierarchical quadratic programming (HQP)-based robot controller. Hierarchical controllers can effectively manage articulated robots with many degrees of freedom (DoFs) to perform multiple tasks. The HQP-based controller is one of the generic hierarchical controllers that can provide a control solution guaranteeing strict task priority while handling numerous equality and inequality constraints. However, according to a large amount of computation, it can be a burden to use it for real-time control. Therefore, for practical use of the HQP, we propose a method to reduce the computational cost by decreasing the size of the decision variable. The computation time and control performance of the proposed method are evaluated by real robot experiments with a 15 DoFs dual-arm manipulator.