Karen T. Sutherland

Robot localization from landmarks using recursive total least squares

In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of the robot position. We propose using a recursive total least squares algorithm to obtain estimates of the robot position. We avoid several weaknesses inherent in the use of the Kalman and extended Kalman filters, achieving much faster convergence without good initial (a priori) estimates of the position. The performance of the method is illustrated both by simulation and on an actual mobile robot with a camera.

Pursuing projections: keeping a robot on path

For an autonomous robot navigating in an unstructured outdoor environment, staying close to a path is crucial to successfully reaching its goal. Although the degree of accuracy with which it estimates its own location affects its ability to stay on the path, accuracy in estimate of lateral distance from the path is far more important for successful navigation than accuracy in estimate of position along the path. Utilizing methods based only on relative angular measurements between landmarks in the environment, we draw from techniques used in statistical pattern recognition to show how landmarks can be chosen for localization which will not only give good estimate of location in spite of the measurement error, but will also keep the robot on the path. We demonstrate how identical landmark configurations can produce very different results in localizing to a path and show how simple heuristics can be used to choose the best configuration for path localization.<<ETX>>

A Rapidly Converging Recursive Method for Mobile Robot Localization

This paper proposes a simple method for estimating the position of a robot from relatively few sensor readings. Our algorithms are intended for applications where sensor readings are expensive or otherwise limited, and the readings that are taken are subject to con siderable errors or noise. This method exhibits faster convergence with fewer measurements and greater accuracy than that exhibited by the discrete Kalman filter in this type of application. Our approach is validated with a mobile robot, on which a camera is used to obtain bearing information with respect to landmarks in the environment.

Recursive Total Least Squares: An Alternative to Using the Discrete Kalman Filter in Robot Navigation

In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of the robot position. The discrete Kalman filter, commonly used for prediction and detection of signals in communication and control problems, has become a popular method to reduce the effect of uncertainty from the sensor data. However, in the domain of robot navigation, sensor readings are not only uncertain, but can also be relatively infrequent compared to traditional signal processing applications. In addition, a good initial estimate of location, critical for Kalman convergence, is often not available. Hence, there is a need for a filter that is capable of converging with a poor initial estimate and many fewer readings than the Kalman filter. To this end, we propose the use of a Recursive Total Least Squares Filter. This filter is easily updated to incorporate new sensor data, and in our experiments converged faster and to greater accuracy than the Kalman filter.

Internet robotics: an educational experiment

Robotics is a subject which captures the imagination of undergraduate students in many disciplines including Computer Science and Mechanical Engineering. Despite this interest, when the topic is covered at all in an undergraduate program, the critical hands-on component of the course is often omitted. This is due to a number of causes ranging from the complexity of the subject to the availability of equipment. This paper describes a project to allow experimentation with robots through the Internet for the purpose of undergraduate education. An experimental system has been developed which links computer science students at the University of Wisconsin/LaCrosse with robots and Mechanical Engineering Students at Wilkes University, Wilkes-Barre PA. Unlike such systems constructed for the purpose of experimentation with time delay for shallow space or undersea manipulation, the focus of this system is the education of undergraduate students. This paper discusses how the educational goals affect the design of this system as well as the selection of tasks. Although there are clear advantages in capital and maintenance costs to sharing equipment, the emphasis here is on the significant educational benefits of this type of system. We show that remote operation leads to an understanding of the complexity and difficulty in specifying robot motions for an uncontrolled environment. This understanding is very difficult to achieve in a simulated or local settings where students have much greater control over the execution environment of the robot. The system was constructed in the summer of 1995, with experiments performed during the fall semester of 1995. Results of the experiments run by the joint undergraduate research groups as well as the associated educational outcomes are presented.

Sharing resources over the Internet for robotics education

At small, undergraduate institutions, resources are scarce and the educational challenges are great. In the area of robotics, the need for physical experimentation to reinforce and validate theoretical concepts is particularly strong, yet the requirements of maintaining a robotics laboratory can be onerous to teaching faculty. Experimental robotics often requires a software sophistication well beyond that which can be expected from undergraduate mechanical engineers, who are most often only required to write simple programs in manufacturer supplied languages. This paper describes an effort to provide an undergraduate robotics research experience in the presence of these challenges. We have teamed undergraduate mechanical engineers at Wilkes University with undergraduate computer scientists at University of Wisconsin - La Crosse in a collaborative experimental effort. The goal of this project is to remotely control a PUMA 760 robot located at Wilkes University from an operator station located at UW-La Crosse.

Project update: sharing resources over the Internet for robotics education

At small, undergraduate institutions, resources are scarce and the educational challenges are great. In the area of robotics, the need for physical experimentation to reinforce and validate theoretical concepts is particularly strong, yet the requirements of maintaining a robotics laboratory can be onerous to teaching faculty. Experimental robotics often requires a software sophistication well beyond that which can be expected from undergraduate mechanical engineers, who are most often only required to write simple programs in manufacturer supplied languages. This paper is the third in a series describing an effort to provide an undergraduate robotics research experience in the presence of these challenges. For the last three years we have teamed undergraduate mechanical engineers at Wilkes University with undergraduate computer scientists at University of Wisconsin - La Crosse in a collaborative experimental effort. The goal of this project is to remotely control a PUMA 760 robot located at Wilkes University form an operator station located at UW-La Crosse.

Internet robotics: transmitting images

This paper presents results from an ongoing collaboration between Wilkes University and the University of Wisconsin-La Crosse in using the Internet for undergraduate education in robotics. An interface has been developed which allows computer science students at UW-La Crosse to control a robotic manipulator on the Wilkes University campus using images transmitted from Wilkes. The focus of this paper is the interface which monitors the image transmission and the control which the student user has over that transmission. An option in the interface allows the user to crop the image to a desired size in order to focus on a specific feature. Results of experiments performed by the joint undergraduate research groups at both institutions in using this component, as well as the associated educational outcomes, are presented here.

Distinguishing characteristics in underwater images of Northern and Eurasian watermilfoils

This paper describes a simple scheme for distinguishing between two very similar watermilfoils, Eurasian or Myriophyllum spicatum L. and Northern or Myriophyllum exalbescens. Leaf images were isolated from underwater images of the plants. Characteristic features consisting of the ratio of black to white pixels within the convex hull of an edge-mapped leaf, the eccentricity of the ellipse surrounding the leaf and a spatial-dependency analysis, measuring the frequency of change of pixel intensity of an edge-mapped leaf were combined to provide a measure which could be used to determine whether a leaf was Northern or Eurasian.