KEYWORDS: Databases, Robots, Control systems, Data modeling, Computer programming, Neural networks, Mobile robots, Complex systems, Systems modeling, Navigation systems
History shows that problems that cause human confusion often lead to inventions to solve the problems,
which then leads to exploitation of the invention, creating a confusion-invention-exploitation cycle.
Robotics, which started as a new type of universal machine implemented with a computer controlled
mechanism in the 1960's, has progressed from an Age of Over-expectation, a Time of Nightmare, an Age
of Realism, and is now entering the Age of Exploitation.
The purpose of this paper is to propose architecture for the modern intelligent robot in which sensors permit
adaptation to changes in the environment are combined with a "creative controller" that permits adaptive
critic, neural network learning, and a dynamic database that permits task selection and criteria adjustment.
This ideal model may be compared to various controllers that have been implemented using Ethernet, CAN
Bus and JAUS architectures and to modern, embedded, mobile computing architectures. Several
prototypes and simulations are considered in view of peta-computing. The significance of this comparison
is that it provides some insights that may be useful in designing future robots for various manufacturing,
medical, and defense applications.
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.
This paper presents a modified virtual force based obstacle avoidance approach suited for laser range
finder. The modified method takes advantage of the polar coordinate based data sent by the laser sensor by
mapping the environment in a polar coordinate system. The method also utilizes a Gaussian function based
certainty values to detect obstacle. The method successfully navigates through complex obstacles and
reaches target GPS waypoints.
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