25,631 research outputs found
Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit
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
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
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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Quantum Robots and Quantum Computers
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
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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