Roborodentia Robot: Tektronix, 2016 California Polytechnic State University, San Luis Obispo
Roborodentia Robot: Tektronix, Sean Yap
Tektronix is a robot created to compete in the 2016 Roborodentia Competition. This report details the full function and implementation of the robot.
Senior Project: Control System For An Underwater Remotely Operated Vehicle, 2016 California Polytechnic State University, San Luis Obispo
Senior Project: Control System For An Underwater Remotely Operated Vehicle, Tyler Mau, Joseph Mahoney
No abstract provided.
The Story Of Beyoncé: The Roborodentia 2016 Contestant, 2016 California Polytechnic State University, San Luis Obispo
The Story Of Beyoncé: The Roborodentia 2016 Contestant, Brandon Arnold, Mana Kulkarni, Aaron Newberg, Jon Sleep
No abstract provided.
Roborodentia 2016: Scorpion, 2016 California Polytechnic State University, San Luis Obispo
Roborodentia 2016: Scorpion, Tyler Whalen
This report showcases my entry into the Roborodentia 2016 competition, and my senior project. I chose this project because robotics has always interested me, and this was a great opportunity to jump in headfirst.
I will step through my design decisions and detail all information necessary for replicating this build.
Roborodentia Xxi Robot, 2016 California Polytechnic State University, San Luis Obispo
Roborodentia Xxi Robot, Jose A. Villa
This report covers the design and implementation of a robot built to compete in Roborodentia 2016.
Roborodentia Robot (Amazon Prime), 2016 California Polytechnic State University, San Luis Obispo
Roborodentia Robot (Amazon Prime), Alec Cheung
Roborodentia is an annual autonomous robotics competition sponsored and hosted by Cal Poly. In the 2016 competition, participants are to design a robot that scores the most points by gathering rings from marked supply pegs and placing them onto marked scoring pegs. For Roborodentia I designed, constructed, and programmed a robot, named Amazon Prime, to compete.
Red Piston (Roborodentia), 2016 California Polytechnic State University, San Luis Obispo
Red Piston (Roborodentia), Gabriel Lee Hernandez, Davie Sy
Roborodentia is an annual robotics competition held during Cal Poly’s Open House showcase. Teams of one to three people come together to build an autonomous robot to typically collect rings for points. The specifications of the competition will be detailed in the problem statement. For Roborodentia 2016, we built a machine dubbed Red Piston to tackle on this year’s competition. The following report will detail the design process and implementation of our robot.
A Stroke Therapy Brace Design, 2016 California Polytechnic State University, San Luis Obispo
A Stroke Therapy Brace Design, Evan Kirkbride
Victims of stroke often have difficulty with rehabilitation. With limited movement on their affected arm, patients often do not want to move much for physical therapy. In this project, we design a robotic brace that helps stroke patients move their arm more effectively in a reaching or pulling motion. By giving patients more movement in their affected arm than they would have otherwise, patients gain more from rehabilitation. The brace also adapts to the patient’s needs, providing more inclination or resistance as needed for their physical therapy. This kind of therapy engages patients rather than relying on their likely ...
3-D Shape Recovery From A Single Camera Image, 2016 Purdue University
3-D Shape Recovery From A Single Camera Image, Vijai Jayadevan, Aaron Michaux, Edward Delp, Zygmunt Pizlo
3-D shape recovery is an ill-posed inverse problem which must be solved by using a priori constraints. We use symmetry and planarity constraints to recover 3-D shapes from a single image. Once we assume that the object to be reconstructed is symmetric, all that is left to do is to estimate the plane of symmetry and establish the symmetry correspondence between the various parts of the object. The edge map of the image of an object serves as a good representation of its 2-D shape and establishing symmetry correspondence means identifying pairs of symmetric curves in the edge map. The ...
A Cyber-Physical System, 2016 Liberty University
A Cyber-Physical System, Andrew Davis, Dustin Bowe, Josiah Nagel
Montview Liberty University Journal of Undergraduate Research
The team was tasked with the creation of an autonomous cyber-physical system that could be continually developed as a post-capstone class by future STEM students and as a means to teach future engineering students. The strict definition of a cyber-physical system is a computation machine that networks with an embedded computer that performs a physical function. The autonomous aspect was achieved through two sonic sensors to monitor object distances in order to avoid walls and obstacles. The integrated system was based on the Intel Edison computation module. A primary goal for future addition is automation capabilities and machine learning applications.
Adaptive Kalman Filtering Methods For Low-Cost Gps/Ins Localization For Autonomous Vehicles, 2016 Carnegie Mellon University
Adaptive Kalman Filtering Methods For Low-Cost Gps/Ins Localization For Autonomous Vehicles, Adam Werries, John M. Dolan
For autonomous vehicles, navigation systems must be accurate enough to provide lane-level localization. Highaccuracy sensors are available but not cost-effective for production use. Although prone to significant error in poor circumstances, even low-cost GPS systems are able to correct Inertial Navigation Systems (INS) to limit the effects of dead reckoning error over short periods between sufficiently accurate GPS updates. Kalman filters (KF) are a standard approach for GPS/INS integration, but require careful tuning in order to achieve quality results. This creates a motivation for a KF which is able to adapt to different sensors and circumstances on its own ...
Inferring Intrinsic Beliefs Of Digital Images Using A Deep Autoencoder, Seok H. Lee
Computer Science and Computer Engineering Undergraduate Honors Theses
Training a system of artificial neural networks on digital images is a big challenge. Often times digital images contain a large amount of information and values for artificial neural networks to understand. In this work, the inference model is proposed in order to absolve this problem. The inference model is composed of a parameterized autoencoder that endures the loss of information caused by the rescaling of images and transition model that predicts the effect of an action on the observation. To test the inference model, the images of a moving robotic arm were given as the data set. The inference ...
Robot Detection Using Gradient And Color Signatures, 2016 Bowdoin College
Robot Detection Using Gradient And Color Signatures, Megan Marie Maher
Tasks which are simple for a human can be some of the most challenging for a robot. Finding and classifying objects in an image is a complex computer vision problem that computer scientists are constantly working to solve. In the context of the RoboCup Standard Platform League (SPL) Competition, in which humanoid robots are programmed to autonomously play soccer, identifying other robots on the field is an example of this difficult computer vision problem. Without obstacle detection in RoboCup, the robotic soccer players are unable to smoothly move around the field and can be penalized for walking into another robot ...
Flying By Fire: Making Controlled Burns Safer For Humans And Uavs, 2016 University of Nebraska-Lincoln
Flying By Fire: Making Controlled Burns Safer For Humans And Uavs, Rebecca Horzewski, Carrick Detweiler
UCARE Research Products
A temperature sensing circuit board was developed that will allow Nimbus Lab's controlled burn starting UAV to react to the temperatures around it.
Robust Monocular Flight In Cluttered Outdoor Environments, 2016 Carnegie Mellon University
Robust Monocular Flight In Cluttered Outdoor Environments, Shreyansh Daftry, Sam Zeng, Arbaaz Khan, Debadeepta Dey, Narek Melik-Barkhudarov, J. Andrew Bagnell, Martial Hebert
Recently, there have been numerous advances in the development of biologically inspired lightweight Micro Aerial Vehicles (MAVs). While autonomous navigation is fairly straightforward for large UAVs as expensive sensors and monitoring devices can be employed, robust methods for obstacle avoidance remains a challenging task for MAVs which operate at low altitude in cluttered unstructured environments. Due to payload and power constraints, it is necessary for such systems to have autonomous navigation and flight capabilities using mostly passive sensors such as cameras. In this paper, we describe a robust system that enables autonomous navigation of small agile quad-rotors at low altitude ...
Inference Machines For Nonparametric Filter Learning, 2016 Carnegie Mellon University
Inference Machines For Nonparametric Filter Learning, Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell
Data-driven approaches for learning dynamic models for Bayesian filtering often try to maximize the data likelihood given parametric forms for the transition and observation models. However, this objective is usually nonconvex in the parametrization and can only be locally optimized. Furthermore, learning algorithms typically do not provide performance guarantees on the desired Bayesian filtering task. In this work, we propose using inference machines to directly optimize the filtering performance. Our procedure is capable of learning partially-observable systems when the state space is either unknown or known in advance. To accomplish this, we adapt PREDICTIVE STATE INFERENCE MACHINES (PSIMS) by introducing ...
Online Bellman Residual And Temporal Difference Algorithms With Predictive Error Guarantees, 2016 Carnegie Mellon University
Online Bellman Residual And Temporal Difference Algorithms With Predictive Error Guarantees, Wen Sun, J. Andrew Bagnell
We establish connections from optimizing Bellman Residual and Temporal Difference Loss to worstcase long-term predictive error. In the online learning framework, learning takes place over a sequence of trials with the goal of predicting a future discounted sum of rewards. Our first analysis shows that, together with a stability assumption, any no-regret online learning algorithm that minimizes Bellman error ensures small prediction error. Our second analysis shows that applying the family of online mirror descent algorithms on temporal difference loss also ensures small prediction error. No statistical assumptions are made on the sequence of observations, which could be nonMarkovian or ...
“My Logic Is Undeniable”: Replicating The Brain For Ideal Artificial Intelligence, 2016 Liberty University
“My Logic Is Undeniable”: Replicating The Brain For Ideal Artificial Intelligence, Samuel C. Adams
Senior Honors Theses
Alan Turing asked if machines can think, but intelligence is more than logic and reason. I ask if a machine can feel pain or joy, have visions and dreams, or paint a masterpiece. The human brain sets the bar high, and despite our progress, artificial intelligence has a long way to go. Studying neurology from a software engineer’s perspective reveals numerous uncanny similarities between the functionality of the brain and that of a computer. If the brain is a biological computer, then it is the embodiment of artificial intelligence beyond anything we have yet achieved, and its architecture is ...
Automatic Scheduling For Unmanned Aerial System, 2016 Selected Works
Automatic Scheduling For Unmanned Aerial System, Gene Cao, Cody Soderstrom, Jeremy Straub, Eunjin Kim
Automatic Scheduling For Unmanned Aerial System, 2016 Selected Works