Mr. Kovid Mathur Profile

Kovid Mathur

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Publications (3)

Proceedings Article | 18 January 2010 Paper

Comparison of three control methods for an autonomous vehicle

Anup Deshpande, Kovid Mathur, Ernest Hall

Proceedings Volume 7539, 75390T (2010) https://doi.org/10.1117/12.838897

KEYWORDS: Control systems, Motion controllers, Computer programming, Servomechanisms, Safety, Amplifiers, Actuators, Switches, Computing systems, Vehicle control

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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.

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