25,631 research outputs found

    Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit

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    The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system

    Quantum Algorithm of Imperfect KB Self-organization. Pt II: Robotic Control with Remote Knowledge Base Exchange

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    The technology of knowledge base remote design of the smart fuzzy controllers with the application of the "Soft / quantum computing optimizer" toolkit software developed. The possibility of the transmission and communication the knowledge base using remote connection to the control object considered. Transmission and communication of the fuzzy controller’s knowledge bases implemented through the remote connection with the control object in the online mode apply the Bluetooth or WiFi technologies. Remote transmission of knowledge bases allows designing many different built-in intelligent controllers to implement a variety of control strategies under conditions of uncertainty and risk. As examples, two different models of robots described (mobile manipulator and (“cart-pole” system) inverted pendulum). A comparison of the control quality between fuzzy controllers and quantum fuzzy controller in various control modes is presented. The ability to connect and work with a physical model of control object without using than mathematical model demonstrated. The implemented technology of knowledge base design sharing in a swarm of intelligent robots with quantum controllers. It allows to achieve the goal of control and to gain additional knowledge by creating a new quantum hidden information source based on the synergetic effect of combining knowledge. Development and implementation of intelligent robust controller’s prototype for the intelligent quantum control system of mega-science project NICA (at the first stage for the cooling system of superconducted magnets) is discussed. The results of the experiments demonstrate the possibility of the ensured achievement of the control goal of a group of robots using soft / quantum computing technologies in the design of knowledge bases of smart fuzzy controllers in quantum intelligent control systems. The developed software toolkit allows to design and setup complex ill-defined and weakly formalized technical systems on line

    Quantum Robot: Structure, Algorithms and Applications

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    A kind of brand-new robot, quantum robot, is proposed through fusing quantum theory with robot technology. Quantum robot is essentially a complex quantum system and it is generally composed of three fundamental parts: MQCU (multi quantum computing units), quantum controller/actuator, and information acquisition units. Corresponding to the system structure, several learning control algorithms including quantum searching algorithm and quantum reinforcement learning are presented for quantum robot. The theoretic results show that quantum robot can reduce the complexity of O(N^2) in traditional robot to O(N^(3/2)) using quantum searching algorithm, and the simulation results demonstrate that quantum robot is also superior to traditional robot in efficient learning by novel quantum reinforcement learning algorithm. Considering the advantages of quantum robot, its some potential important applications are also analyzed and prospected.Comment: 19 pages, 4 figures, 2 table

    SciTech News Volume 71, No. 1 (2017)

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    Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

    Quantum Robots and Quantum Computers

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    Validation of a presumably universal theory, such as quantum mechanics, requires a quantum mechanical description of systems that carry out theoretical calculations and experiments. The description of quantum computers is under active development. No description of systems to carry out experiments has been given. A small step in this direction is taken here by giving a description of quantum robots as mobile systems with on board quantum computers that interact with environments. Some properties of these systems are discussed. A specific model based on the literature descriptions of quantum Turing machines is presented.Comment: 18 pages, RevTex, one postscript figure. Paper considerably revised and enlarged. submitted to Phys. Rev.

    Quantum Robots and Environments

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    Quantum robots and their interactions with environments of quantum systems are described and their study justified. A quantum robot is a mobile quantum system that includes a quantum computer and needed ancillary systems on board. Quantum robots carry out tasks whose goals include specified changes in the state of the environment or carrying out measurements on the environment. Each task is a sequence of alternating computation and action phases. Computation phase activities include determination of the action to be carried out in the next phase and possible recording of information on neighborhood environmental system states. Action phase activities include motion of the quantum robot and changes of neighborhood environment system states. Models of quantum robots and their interactions with environments are described using discrete space and time. To each task is associated a unitary step operator T that gives the single time step dynamics. T = T_{a}+T_{c} is a sum of action phase and computation phase step operators. Conditions that T_{a} and T_{c} should satisfy are given along with a description of the evolution as a sum over paths of completed phase input and output states. A simple example of a task carrying out a measurement on a very simple environment is analyzed. A decision tree for the task is presented and discussed in terms of sums over phase paths. One sees that no definite times or durations are associated with the phase steps in the tree and that the tree describes the successive phase steps in each path in the sum.Comment: 30 Latex pages, 3 Postscript figures, Minor mathematical corrections, accepted for publication, Phys Rev
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