Wail Gueaieb

An Intelligent Mobile Robot Navigation Technique Using RFID Technology

This paper presents an innovative mobile robot navigation technique using radio frequency identification (RFID) technology. Navigation based on processing some analog features of an RFID signal is a promising alternative to different types of navigation methods in the state of the art. The main idea is to exploit the ability of a mobile robot to navigate a priori unknown environments without a vision system and without building an approximate map of the robot workspace, as is the case in most other navigation algorithms. This paper discusses how this is achieved by placing RFID tags in the 3-D space so that the lines linking their projections on the ground define the ldquofree waysrdquo along which the robot can (or is desired to) move. The suggested algorithm is capable of reaching a target point in its a priori unknown workspace, as well as tracking a desired trajectory with a high precision. The proposed solution offers a modular, computationally efficient, and cost-effective alternative to other navigation techniques for a large number of mobile robot applications, particularly for service robots, such as, for instance, in large offices and assembly lines. The effectiveness of the proposed approach is illustrated through a number of computer simulations considering testbeds of various complexities.

ANN-Based Adaptive Control of Robotic Manipulators With Friction and Joint Elasticity

This paper proposes a control strategy based on artificial neural networks (ANNs) for a positioning system with a flexible transmission element, taking into account Coulomb friction for both motor and load, and using a variable learning rate for adaptation to parameter changes and accelerate convergence. A control structure consists of a feedforward ANN that approximates the manipulators inverse dynamical model, an ANN feedback control law, a reference model, and the adaptation process of the ANNs with a variable learning rate. A supervisor that adapts the neural networks learning rate and a rule-based supervisor for online adaptation of the parameters of the reference model are proposed to maintain the stability of the system for large variations of load parameters. Simulation results highlight the performance of the controller to compensate the nonlinear friction terms, particularly Coulomb friction, and flexibility, and its robustness to the load and drive motor inertia parameter changes. Internal stability, which is a potential problem in such a system, is also verified. The controller is suitable for DSP and very large scale integration implementation and can be used to improve static and dynamic performances of electromechanical systems.

A Robust Hybrid Intelligent Position/Force Control Scheme for Cooperative Manipulators

We examine in this paper the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed controller does not require a precise dynamical model of the systems dynamics. As a matter of fact, the controller can achieve its control objectives starting from partial or no a priori knowledge of the systems dynamics. The ability to incorporate the already acquired knowledge about the systems dynamics is among what distinguishes the proposed controller from its predecessor adaptive fuzzy controllers. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The position and the internal force errors are also shown to asymptotically converge to zero under such conditions

Ant Colony-Based Reinforcement Learning Algorithm for Routing in Wireless Sensor Networks

The field of routing and sensor networking is an important and challenging research area of network computing today. Advancements in sensor networks enable a wide range of environmental monitoring and object tracking applications. Routing in sensor networks is a difficult problem: as the size of the network increases, routing becomes more complex. Therefore, biologically-inspired intelligent algorithms are used to tackle this problem. Ant routing has shown excellent performance for sensor networks. In this paper, we present a biologically-inspired swarm intelligence-based routing algorithm, which is suitable for sensor networks. Our proposed ant routing algorithm also meet the enhanced sensor network requirements, including energy consumption, success rate, and time delay. The paper concludes with the measurement data we have found.

Type-2 Fuzzy Logic Control of a Flexible-Joint Manipulator

A type-2 fuzzy logic controller (FLC) is proposed in this article for robot manipulators with joint elasticity and structured and unstructured dynamical uncertainties. The proposed controller is based on a sliding mode control strategy. To enhance its real-time performance, simplified interval fuzzy sets are used. The efficiency of the control scheme is further enhanced by using computationally inexpensive input signals independently of the noisy torque and acceleration signals, and by adopting a trade off strategy between the manipulator’s position and the actuators’ internal stability. The controller is validated through a set of numerical experiments and by comparing it against its type-1 counterpart. It is shown through these experiments the higher performance of the type-2 FLC in compensating for larger magnitudes of uncertainties with severe nonlinearities.

Fuzzy entropy: a brief survey

This paper presents a survey about different types of fuzzy information measures. A number of schemes have been proposed to combine the fuzzy set theory and its application to the entropy concept as a fuzzy information measurements. The entropy concept, as a relative degree of randomness, has been utilized to measure the fuzziness in a fuzzy set or system. However, a major difference exists between the classical Shannon entropy and the fuzzy entropy. In fact while the later deals with vagueness and ambiguous uncertainties, the former tackles probabilistic uncertainties (randomness).

The hierarchical expert tuning of PID controllers using tools of soft computing

We present soft computing-based results pertaining to the hierarchical tuning process of PID controllers located within the control loop of a class of nonlinear systems. The results are compared with PID controllers implemented either in a stand alone scheme or as a part of conventional gain scheduling structure. This work is motivated by the increasing need in the industry to design highly reliable and efficient controllers for dealing with regulation and tracking capabilities of complex processes characterized by nonlinearities and possibly time varying parameters. The soft computing-based controllers proposed are hybrid in nature in that they integrate within a well-defined hierarchical structure the benefits of hard algorithmic controllers with those having supervisory capabilities. The controllers proposed also have the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes.

Brief paper: Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics

This article presents a decentralized control scheme for the complex problem of simultaneous position and internal force control in cooperative multiple manipulator systems. The proposed controller is composed of a sliding mode control term and a force robustifying term to simultaneously control the payloads position/orientation as well as the internal forces induced in the system. This is accomplished independently of the manipulators dynamics. Unlike most controllers that do not require prior knowledge of the manipulators dynamics, the suggested controller does not use fuzzy logic inferencing and is computationally inexpensive. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying systems dynamics. The payloads position/orientation and the internal force errors are also shown to asymptotically converge to zero under such conditions.

Wireless Sensor Network Transport Layer: State of the Art

This chapter describes the essence of a generic transport layer of a Multi-hop Wireless Sensor Network (WSN). The transport layer of the Internet handles the congestion generated due to the network traffic and the end-to-end reliability of individual packets. Similar to the Internet, many WSN applications require a congestion control mechanism to regulate the amount of traffic injected within the WSN to avoid packet loss and to guarantee end-to-end reliable packet/event delivery. WSN researchers thus argue the presence of a transport layer for WSN similar to the Internet. Because of the resource constraint nature of sensor devices, researchers however admit that an Internet-scale transport layer will indeed be a matter of challenge. Literature reveals detailed analysis of the requirements and constraints of a WSN transport layer. The advancements in microprocessor technology, high speed and large memories, high speed networks, Ultra Wide Band frequency spectrums, very efficient sensor network Operating Systems and miniaturization of many heterogeneous sensor devices, to name a few, have led to the development of many transport layer protocols. This chapter addresses the unique characteristics of a WSN transport layer, classifies the attributes that characterize different functionalities offered by a transport layer, presents the most popular transport layer protocols based on the attributes found in each protocol, and finally, points out open research issues of this domain, which need further attention to overcome the aforementioned challenges.

Ant colony-based many-to-one sensory data routing in Wireless Sensor Networks

An ant colony-based routing protocol is presented in this paper that is specifically designed to route many-to-one sensory data in a multi-hop wireless sensor network (WSN). Because a many-to-one routing paradigm generates lots of traffic in a multi- hop WSN resulting in greater energy wastage, higher end-to-end delay and packet loss, the proposed routing protocol also comes with a lightweight congestion control mechanism, which is capable of handling both event-based and periodic upstream sensory data flow to the base station. The proposed protocol works in two-phases. During the first phase, the protocol uses ant-based intelligence to find and enforce the shortest path and in the second phase, when the actual many- to-one sensory data transmission takes place, the protocol combines the knowledge gained during the first phase with the congestion control mechanism to avoid packet loss and traffic while routing the sensory data. When compared with the related algorithms, the proposed algorithm shows promising results.