The purpose of this paper is to discuss the challenge of engineering robust intelligent robots. Robust
intelligent robots may be considered as ones that not only work in one environment but rather in all types of
situations and conditions. Our past work has described sensors for intelligent robots that permit adaptation
to changes in the environment. We have also described the combination of these sensors with a "creative
controller" that permits adaptive critic, neural network learning, and a dynamic database that permits task
selection and criteria adjustment. However, the emphasis of this paper is on engineering solutions which
are designed for robust operations and worst case situations such as day night cameras or rain and snow
solutions. This ideal model may be compared to various approaches that have been implemented on
"production vehicles and equipment" using Ethernet, CAN Bus and JAUS architectures and to modern,
embedded, mobile computing architectures. Many prototype intelligent robots have been developed and
demonstrated in terms of scientific feasibility but few have reached the stage of a robust engineering
solution. Continual innovation and improvement are still required. 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 where robust and reliable performance is essential.
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.
The purpose of this paper is to describe the design, development and simulation of a real time controller for an intelligent, vision guided robot. The use of a creative controller that can select its own tasks is demonstrated. This creative controller uses a task control center and dynamic database. 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. Also, 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 is that the method is applicable to both robot arm manipulators and robot bases 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, and medical robotics aids.
The purpose of this paper is to describe the concept and architecture for an intelligent robot system that can adapt, learn and predict the future. This evolutionary approach to the design of intelligent robots is the result of several years of study on the design of intelligent machines that could adapt using computer vision or other sensory inputs, learn using artificial neural networks or genetic algorithms, exhibit semiotic closure with a creative controller and perceive present situations by interpretation of visual and voice commands. This information processing would then permit the robot to predict the future and plan its actions accordingly. In this paper we show that the capability to adapt, and learn naturally leads to the ability to predict the future state of the environment which is just another form of semiotic closure. That is, predicting a future state without knowledge of the future is similar to making a present action without knowledge of the present state. The theory will be illustrated by considering the situation of guiding a mobile robot through an unstructured environment for a rescue operation. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots.
An estimated 100 million landmines which have been planted in more than 60 countries kill or maim thousands of civilians every year. Millions of people live in the vast dangerous areas and are not able to access to basic human services because of landmines’ threats. This problem has affected many third world countries and poor nations which are not able to afford high cost solutions. This paper tries to present some experiences with the land mine victims and solutions for the mine clearing. It studies current situation of this crisis as well as state of the art robotics technology for the mine clearing. It also introduces a survey robot which is suitable for the mine clearing applications. The results show that in addition to technical aspects, this problem has many socio-economic issues. The significance of this study is to persuade robotics researchers toward this topic and to peruse the technical and humanitarian facets of this issue.
Intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths to accomplish a variety of tasks. Such machines have many potential useful applications in medicine, defense, industry and even the home so that the design of such machines is a challenge with great potential rewards. Even though intelligent systems may have symbiotic closure that permits them to make a decision or take an action without external inputs, sensors such as vision permit sensing of the environment and permit precise adaptation to changes. Sensing and adaptation define a reactive system. However, in many applications some form of learning is also desirable or perhaps even required. A further level of intelligence called understanding may involve not only sensing, adaptation and learning but also creative, perceptual solutions involving models of not only the eyes and brain but also the mind. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots with examples of adaptive, creative and perceptual learning. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots that could lead to important beneficial applications.
For thousands of years, humans have looked to nature to find solutions for their problems. This trend has affected the robotics field as well as artificial intelligence, manufacturing, biomechanics, vision and many others. In the robotics field, there are many unsolved problems which amazingly have been solved in nature. These problems vary from basic motion control to high level intelligence problems. Insects' motion, human's walking, driving, exploring an unstructured environment, and object recognition are examples of these problems. Robotics researchers have looked to nature to find solutions to these problems. However, what is missing is human-like computation ability. The presumption is that if we want to create a human like robot, we should implement systems which perceive and operate similar to humans. This paper is a survey on how robotics has been inspired by mimicking nature. It introduces different trends and reviews the modern biologically inspired technology. It also focuses on human perception and potentials for perception based robotics. The significance of this work is that it provides an understanding of the importance of perception in the design of a robot controller.
Mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. That is why mobile robotics problems are complex with many unanswered questions. To reach a high degree of autonomous operation, a new level of learning is required. On the one hand, promising learning theories such as the adaptive critic and creative control have been proposed, while on other hand the human brain’s processing ability has amazed and inspired researchers in the area of Unmanned Ground Vehicles but has been difficult to emulate in practice. A new direction in the fuzzy theory tries to develop a theory to deal with the perceptions conveyed by the natural language. This paper tries to combine these two fields and present a framework for autonomous robot navigation. The proposed creative controller like the adaptive critic controller has information stored in a dynamic database (DB), plus a dynamic task control center (TCC) that functions as a command center to decompose tasks into sub-tasks with different dynamic models and multi-criteria functions. The TCC module utilizes computational theory of perceptions to deal with the high levels of task planning. The authors are currently trying to implement the model on a real mobile robot and the preliminary results have been described in this paper.
An autonomous robot must be able to sense its environment and react appropriately in a variable environment. The University of Cincinnati Robot team is actively involved in building a small, unmanned, autonomously guided vehicle for the International Ground Robotics Contest organized by Association for Unmanned Vehicle Systems International (AUVSI) each year. The unmanned vehicle is supposed to follow an obstacle course bounded by two white/yellow lines,
which are four inches thick and 10 feet apart. The navigation system for one of the University of Cincinnati’s designs, Bearcat, uses 2 CCD cameras and an image-tracking device for the front end processing of the image captured by the cameras. The three dimensional world co-ordinates were reduced to two dimensional image coordinates as a result of the transformations taking place from the ground plane to the image plane. A novel automatic calibration system was designed to transform the image co-ordinates back to world co-ordinates for navigation purposes. The purpose of this paper is to simplify this tedious calibration using an artificial neural network. Image processing is used to automatically detect calibration points. Then a back projection neural algorithm is used to learn the relationships between the image coordinates and three-dimensional coordinates. This transformation is the main focus of this study. Using these
algorithms, the robot built with this design is able to track and follow the lines successfully.
The purpose of this paper is to demonstrate a new benchmark for comparing the rate of convergence in neural network classification algorithms. The benchmark produces datasets with controllable complexity that can be used to test an algorithm. The dataset generator uses the concept of random numbers and linear normalization to generate the data. In a case of a one-layer perceptron, the output datasets are sensitive to weight or bias of the perceptron. A Matlab implemented algorithm analyzed the sample datasets and the benchmark results. The results demonstrate that the convergence time varies based on some selected specifications of the generated dataset. This benchmark and the generated datasets can be used by researchers that work on neural network algorithms and are looking for a straightforward and flexible dataset to examine and evaluate the efficiency of neural network classification algorithms.
Secure remote access with inter-operatability for operating a robot can be successfully achieved using the web services provided in the .NET framework. The complete design of the machine discussed in this paper is made on the .NET framework. The server which operates the robot is configured to IIS. The algorithm for obstacle detection is coded on a different server using the .NET framework. By using web services, the robot can be accessed by other servers. These web services are consumed by the server on which the robot executes. A proxy is created on this server. The whole control is given in the form of a series of web pages which can be accessed by any web browser. However in order to input parameters and control the robot, authentication is required. The user provides authentication credentials which are matched with the existing information on the data base. After authentication, the user proceeds further to control the robot. The security and reliability of remote access is provided by the components that come with the web services namely, SOAP, WSDL and Proxy.