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Articles 1 - 11 of 11
Full-Text Articles in Computer Engineering
Evolving Predator Control Programs For An Actual Hexapod Robot Predator, Gary Parker, Basar Gulcu
Evolving Predator Control Programs For An Actual Hexapod Robot Predator, Gary Parker, Basar Gulcu
Computer Science Faculty Publications
In the development of autonomous robots, control program learning systems are important since they allow the robots to adapt to changes in their surroundings. Evolutionary Computation (EC) is a method that is used widely in learning systems. In previous research, we used a Cyclic Genetic Algorithm (CGA), a form of EC, to evolve a simulated predator robot to test the effectiveness of a learning system in the predator/prey problem. The learned control program performed search, chase, and capture behavior using 64 sensor states relative to the nearest obstacle and the target, a simulated prey robot. In this paper, we present …
Robotron, Fabian Rodriguez, Oscar Daniel Muneton, Adelaido Jimenez
Robotron, Fabian Rodriguez, Oscar Daniel Muneton, Adelaido Jimenez
Computer Engineering
Roborodentia 2012 is a competition where students and alumni could build an autonomous robot to perform a certain task for points. This report is about Robotron, the 3rd place winner of this competition, and how it came to be.
Union Advanced Educational Robot, Erik Skorina
Union Advanced Educational Robot, Erik Skorina
Honors Theses
Our project was the design and construction of a medium sized (approximately 40 kg when fully loaded) mobile robot for educational and research use. This robot was designed to provide a strong baseline chassis with sensors, actuators and necessary software to allow for easy integration with existing robotic equipment, software and curricula. In particular, it was designed for use in intermediate and advanced undergraduate robotics courses and undergraduate research projects in robotics.
Android Powered Autonomous Robot, Dennis Cagle, Zachary Negrey
Android Powered Autonomous Robot, Dennis Cagle, Zachary Negrey
Computer Engineering
The goal of this Senior Project was to create an autonomous robot powered by an Android phone to compete in Roborodentia 2012. In order to accomplish this task, we used the Android Open Accessory Development Kit (Android ADK) to interface an Android phone with a custom Arduino microcontroller (Arduino Mega) designed by Google. The project contained design and implementation of hardware, electronic devices, and software.
Line Following Navigation, Nicole Marie Pennington
Line Following Navigation, Nicole Marie Pennington
Chancellor’s Honors Program Projects
No abstract provided.
Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas
Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas
George J. Pappas
We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
George J. Pappas
In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For static deployment problems, a classical way of designing high- level feedback motion planners is to implement a gradient descent scheme on a suitably chosen objective function. This can lead to computationally expensive deployment algorithms that may not be adaptive to uncertain dynamic environments. We address this challenge by showing that algorithms for a variety of deployment scenarios in stochastic environments and with noisy sensor measurements can be designed as stochastic gradient descent algorithms, …
Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas
Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas
George J. Pappas
We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
George J. Pappas
In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For static deployment problems, a classical way of designing high- level feedback motion planners is to implement a gradient descent scheme on a suitably chosen objective function. This can lead to computationally expensive deployment algorithms that may not be adaptive to uncertain dynamic environments. We address this challenge by showing that algorithms for a variety of deployment scenarios in stochastic environments and with noisy sensor measurements can be designed as stochastic gradient descent algorithms, …
Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas
Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas
George J. Pappas
We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
George J. Pappas
In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For static deployment problems, a classical way of designing high- level feedback motion planners is to implement a gradient descent scheme on a suitably chosen objective function. This can lead to computationally expensive deployment algorithms that may not be adaptive to uncertain dynamic environments. We address this challenge by showing that algorithms for a variety of deployment scenarios in stochastic environments and with noisy sensor measurements can be designed as stochastic gradient descent algorithms, …