Aaron W. Dennis

Vision Aided Stabilization and the Development of a Quad-Rotor Micro UAV

Micro Unmanned Air Vehicles are well suited for a wide variety of applications in agriculture, homeland security, military, search and rescue, and surveillance. In response to these opportunities, a quad-rotor micro UAV has been developed at the Robotic Vision Lab at Brigham Young University. The quad-rotor UAV uses a custom, low-power FPGA platform to perform computationally intensive vision processing tasks on board the vehicle, eliminating the need for wireless tethers and computational support on ground stations. Drift stabilization of the UAV has been implemented using Harris feature detection and template matching running in real-time in hardware on the on-board FPGA platform, allowing the quad-rotor to maintain a stable and almost drift-free hover without human intervention.

Contour Matching for Fish Species Recognition and Migration Monitoring

A variety of matching and classification techniques have been employed in applications requiring pattern recognition. In this chapter we introduce a simple and accurate real-time contour matching technique specifically for applications involving fish species recognition and migration monitoring. We describe FishID, a prototype vision system that employs a software implementation of our newly developed contour matching algorithms. We discuss the challenges involved in the design of this system, both hardware and software, and we present results from a field test of the system at Prosser Dam in Prosser, Washington. In tests with up to four distinct species, the algorithm correctly determines the species with greater than 90 percent accuracy.

Noninvasive diagnosis of pulmonary hypertension using heart sound analysis

Right-heart catheterization is the most accurate method for measuring pulmonary artery pressure (PAP). It is an expensive, invasive procedure, exposes patients to the risk of infection, and is not suited for long-term monitoring situations. Medical researchers have shown that PAP influences the characteristics of heart sounds. This suggests that heart sound analysis is a potential method for the noninvasive diagnosis of pulmonary hypertension. We describe the development of a prototype system, called PHD (pulmonary hypertension diagnoser), that implements this method. PHD uses patient data with machine learning algorithms to build models of how pulmonary hypertension affects heart sounds. Data from 20 patients were used to build the models and data from another 31 patients were used as a validation set. PHD diagnosed pulmonary hypertension in the validation set with 77% accuracy and 0.78 area under the receiver-operating-characteristic curve.

An Embedded Vision System for an Unmanned Four-rotor Helicopter

In this paper an embedded vision system and control module is introduced that is capable of controlling an unmanned four-rotor helicopter and processing live video for various law enforcement, security, military, and civilian applications. The vision system is implemented on a newly designed compact FPGA board (Helios). The Helios board contains a Xilinx Virtex-4 FPGA chip and memory making it capable of implementing real time vision algorithms. A Smooth Automated Intelligent Leveling daughter board (SAIL), attached to the Helios board, collects attitude and heading information to be processed in order to control the unmanned helicopter. The SAIL board uses an electrolytic tilt sensor, compass, voltage level converters, and analog to digital converters to perform its operations. While level flight can be maintained, problems stemming from the characteristics of the tilt sensor limits maneuverability of the helicopter. The embedded vision system has proven to give very good results in its performance of a number of real-time robotic vision algorithms.

A simple, inexpensive, and effective implementation of a vision-guided autonomous robot

This paper discusses a simple, inexpensive, and effective implementation of a vision-guided autonomous robot. This implementation is a second year entrance for Brigham Young University students to the Intelligent Ground Vehicle Competition. The objective of the robot was to navigate a course constructed of white boundary lines and orange obstacles for the autonomous competition. A used electric wheelchair was used as the robot base. The wheelchair was purchased from a local thrift store for

Learning the Architecture of Sum-Product Networks Using Clustering on Variables

28. The base was modified to include Kegresse tracks using a friction drum system. This modification allowed the robot to perform better on a variety of terrains, resolving issues with last years design. In order to control the wheelchair and retain the robust motor controls already on the wheelchair the wheelchair joystick was simply removed and replaced with a printed circuit board that emulated joystick operation and was capable of receiving commands through a serial port connection. Three different algorithms were implemented and compared: a purely reactive approach, a potential fields approach, and a machine learning approach. Each of the algorithms used color segmentation methods to interpret data from a digital camera in order to identify the features of the course. This paper will be useful to those interested in implementing an inexpensive vision-based autonomous robot.