Prof. Souma Alhaj Ali Profile

Prof. Souma Alhaj Ali

at Hashemite Univ

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Author

Publications (8)

Proceedings Article | 18 January 2010 Paper

Engineering robust intelligent robots

E. Hall, S. M. Alhaj Ali, M. Ghaffari, X. Liao, M. Cao

Proceedings Volume 7539, 753904 (2010) https://doi.org/10.1117/12.838730

KEYWORDS: Robots, Databases, Control systems, Data modeling, Neural networks, Mobile robots, Computer programming, Complex systems, Sensors, Systems modeling

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Proceedings Article | 19 January 2009 Paper

Architectures for intelligent robots in the age of exploitation

E. Hall, S. M. Alhaj Ali, M. Ghaffari, X. Liao, Saurabh Sarkar, Kovid Mathur, Srinivas Tennety

Proceedings Volume 7252, 725203 (2009) https://doi.org/10.1117/12.806232

KEYWORDS: Databases, Robots, Control systems, Data modeling, Computer programming, Neural networks, Mobile robots, Complex systems, Systems modeling, Navigation systems

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Proceedings Article | 10 September 2007 Paper

Eclectic theory of intelligent robots

E. Hall, M. Ghaffari, X. Liao, S. M. Alhaj Ali, Saurabh Sarkar, Scott Reynolds, Kovid Mathur

Proceedings Volume 6764, 676403 (2007) https://doi.org/10.1117/12.730799

KEYWORDS: Databases, Robots, Data modeling, Control systems, Mobile robots, Complex systems, Computer programming, Neural networks, Motion models, Device simulation

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The purpose of this paper is to introduce a concept of eclecticism for the design, development, simulation and implementation of a real time controller for an intelligent, vision guided robots. The use of an eclectic perceptual, creative controller that can select its own tasks and perform autonomous operations is illustrated. This eclectic controller is a new paradigm for robot controllers and is an attempt to simplify the application of intelligent machines in general and robots in particular. The idea is to uses a task control center and dynamic programming approach. However, the information required for an optimal solution may only partially reside in a dynamic database so that some tasks are impossible to accomplish. So a decision must be made about the feasibility of a solution to a task before the task is attempted. Even when tasks are feasible, an iterative learning approach may be required. The learning could go on forever. The dynamic database stores both global environmental information and local information including the kinematic and dynamic models of the intelligent robot. The kinematic model is very useful for position control and simulations. However, models of the dynamics of the manipulators are needed for tracking control of the robot's motions. Such models are also necessary for sizing the actuators, tuning the controller, and achieving superior performance. Simulations of various control designs are shown. Much of the model has also been used for the actual prototype Bearcat Cub mobile robot. This vision guided robot was designed for the Intelligent Ground Vehicle Contest. A novel feature of the proposed approach lies in the fact that it is applicable to both robot arm manipulators and mobile robots such as wheeled mobile robots. This generality should encourage the development of more mobile robots with manipulator capability since both models can be easily stored in the dynamic database. The multi task controller also permits wide applications. The use of manipulators and mobile bases with a high-level control are potentially useful for space exploration, certain rescue robots, defense robots, medical robotics, and robots that aids older people in daily living activities.

Proceedings Article | 2 October 2006 Paper

Intelligent robot control using an adaptive critic with a task control center and dynamic database

E. Hall, M. Ghaffari, X. Liao, S. M. Alhaj Ali

Proceedings Volume 6384, 63840J (2006) https://doi.org/10.1117/12.685367

KEYWORDS: Databases, Data modeling, Control systems, Mobile robots, Global Positioning System, Sensors, Complex systems, Neural networks, Robot vision, Motion models

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Proceedings Article | 25 October 2005 Paper

Design and simulation of a motion controller for a wheeled mobile-robot autonomous navigation

Souma Alhaj Ali, Ernest Hall

Proceedings Volume 6006, 60060M (2005) https://doi.org/10.1117/12.628557

KEYWORDS: Device simulation, Mobile robots, Motion controllers, Detection and tracking algorithms, Feedback control, Computed tomography, Fuzzy logic, Software development, Navigation systems, Control systems

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Proceedings Article | 25 October 2005 Paper

Intelligent robots that adapt, learn, and predict

E. Hall, X. Liao, M. Ghaffari, S. Alhaj Ali

Proceedings Volume 6006, 600602 (2005) https://doi.org/10.1117/12.630920

KEYWORDS: Databases, Robots, Control systems, Mobile robots, Data modeling, Neural networks, Complex systems, Computer programming, Intelligence systems, Process control

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Proceedings Article | 25 October 2004 Paper

Advances in learning for intelligent mobile robots

Ernest Hall, Masoud Ghaffari, Xiaoqun Liao, Souma Alhaj Ali

Proceedings Volume 5608, (2004) https://doi.org/10.1117/12.571195

KEYWORDS: Robots, Mobile robots, Databases, Control systems, Data modeling, Sensors, Device simulation, Intelligent sensors, Adaptive control, Computer programming

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Proceedings Article | 30 September 2003 Paper

Learning for intelligent mobile robots

Ernest Hall, Xiaoqun Liao, Souma Alhaj Ali

Proceedings Volume 5267, (2003) https://doi.org/10.1117/12.514831

KEYWORDS: Robots, Mobile robots, Databases, Control systems, Device simulation, Sensors, Data modeling, Adaptive control, Process control, Computer programming

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Unlike intelligent industrial robots which often work in a structured factory setting, intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. However, such machines have many potential applications in medicine, defense, industry and even the home that make their study important. Sensors such as vision are needed. However, in many applications some form of learning is also required. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots. During the past 20 years, the use of intelligent industrial robots that are equipped not only with motion control systems but also with sensors such as cameras, laser scanners, or tactile sensors that permit adaptation to a changing environment has increased dramatically. However, relatively little has been done concerning learning. Adaptive and robust control permits one to achieve point to point and controlled path operation in a changing environment. This problem can be solved with a learning control. In the unstructured environment, the terrain and consequently the load on the robot’s motors are constantly changing. Learning the parameters of a proportional, integral and derivative controller (PID) and artificial neural network provides an adaptive and robust control. Learning may also be used for path following. Simulations that include learning may be conducted to see if a robot can learn its way through a cluttered array of obstacles. If a situation is performed repetitively, then learning can also be used in the actual application. To reach an even higher degree of autonomous operation, a new level of learning is required. Recently learning theories such as the adaptive critic have been proposed. In this type of learning a critic provides a grade to the controller of an action module such as a robot. The creative control process is used that is “beyond the adaptive critic.” A mathematical model of the creative control process is presented that illustrates the use for mobile robots. Examples from a variety of intelligent mobile robot applications are also presented. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots that could lead to many applications.

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