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IEEE Convention of Electrical and Electronics Engineers in Israel

Fuzzy model of control for quantum-controlled mobile robots

Alexander Rybalov, Eugene Kagan, Yochai Manor, Irad Ben‐Gal

Published Nov 1, 2010• 10 citations
Publisher

Abstract

In this work we consider a navigation of quantum-controlled mobile robot by the use of fuzzy methods. A fuzzy model of the robot behavior is obtained by transformation of the robot's states (that appear in a form of qubits) into the pairs of membership functions. For a full description of the robot's turns, we define a reverse fuzzy Hadamard operator that completes the known Hannachi, Hatakeyama and Hirota model. The suggested transformations and methods were comparatively studied by field trials using small mobile robots and by numerical simulations that model the robot's behavior in the presence of noise.

DOI
10.1109/eeei.2010.5662220

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In this work we consider a navigation of quantum-controlled mobile robot by the use of fuzzy methods. A fuzzy model of the robot behavior is obtained by transformation of the robot's states (that appear in a form of qubits) into the pairs of membership functions. For a full description of the robot's turns, we define a reverse fuzzy Hadamard operator that completes the known Hannachi, Hatakeyama and Hirota model. The suggested transformations and methods were comparatively studied by field trials using small mobile robots and by numerical simulations that model the robot's behavior in the presence of noise.
This work addresses informational-based methods for decision-making in Hilbert space, widely known as quantum decision-making, and their application to the control of mobile robot. We consider a mobile robot, whose objective is to find objects in a changing environment. We compare the robot performance under both a classical and a quantum decision making (control) approaches. Both approaches are driven by similar information-theory decision criteria. The robot behavior is physically studied by using an NXT Lego Robot at the CIM lab in Tel Aviv University.
The article presents a method of fuzzy quantum control and the structure of a fuzzy quantum controller. The authors suggest a method to represent the membership function of fuzzy variables as quantum values. A fuzzy quantum controller calculates the parameters for the optimum control of a quantum system on the basis of dynamic programming. The control task is the transfer of the system from its initial state into a required final state with a set merit functional. At the setting of the fuzzy quantum controller the authors use expert data. The input variable is transformed into fuzzy values and the membership functions are created. The membership functions are represented as quantum values. Further, the controlled system equation is derived. The authors set up a merit functional for optimal regulation and apply the optimal control method. The authors present a circuit to connect the fuzzy quantum controller to a control system.
In this paper, we propose the concepts that apply quantum circuits to model Fuzzy Sets, thereby creating a new method to model behaviors for a humanoid robot. It is similar to the standard fuzzy sets from two points of view -1) of measured probabilities of state |1〉, and 2) that of the normalized measurement in a quantum ensemble computer. However, one can also look at the internal quantum states created by the operators of the new model. This extends fuzzy sets from [0, 1] interval to some other domain, similar to complex fuzzy logic, but also brings superposed, entangled and parallel quantum behaviors. The standard fuzzy logic aspect (external) of the new model maps the numbers in interval [0,1] to points on one meridian of the Bloch Sphere and after measurement, the results map back to numbers from interval [0,1]. However, the other, more important, aspect of this model (internal) is the operation on arbitrary quantum states, and the realization of additional states via phase measurements. We discuss and illustrate how this model can be used to represent a hidden state of reasoning agents or an emotional state of a humanoid robot.
In the present work we propose a scheme to control the network of wheeled mobile robots using Quantum computing. This scheme provides a fast and efficient way to navigate a system of networked robots from one position to another using a PID Controllers. It is a well established fact that the robotic systems aredefined using Lagrangian dynamics and PID control is an efficient way to control the movement of robotic systems . In the present work we propose a framework to use Quantum Computers and Photic couplings among robots to form a consensus and move a the coupled robotic system from one place to another. We propose that all the computations must be carried out on quantum computers for efficient control and fast execution. We propose an algorithm that enables the proposed scheme. Numerical Simulations that show synchronized motion of coupled robots have been reported.
An approach for emulating quantum circuits using conventional analog hardware is presented based on the intuitive similarity between fuzzy logic and quantum superposition, as well as some geometrical analogies. This approach has the advantage of being easy to implement on dedicated hardware for parallel processing of membership functions and fuzzy inference, compared to conventional quantum computing, which requires quantum mechanical systems which are extremely sensitive to noise and difficult to extend to large scale systems. Using geometrical analogies and a suitable transformation, qubits are modeled as pairs of fuzzy membership functions evolving on the unit square and basic one qubit gates are modeled as transformations on this unit square. A fuzzy implementation of the one bit and two bit Deutsch-Jozsa algorithm is proposed. Physical implementation and advantages, such as the possibility of implementing nonlinear or non-unitary gates, as well as drawbacks of the proposed model compared to conventional quantum computing are shown.