Ernest L. Hall

• Ph D University of Missouri
• Professor Emeritus at University of Cincinnati

The sampling theorem shows how we can represent a continuous function with its discrete samples if the sampling rate is high enough. Computer binary logic and DNA are now accepted as discrete signals.

This is a wonderful translation of our book " Robotics A User Friendly Introduction" in Chinese. Professors Zuoling Cao and Yuyu Huang and their colleagues performed the translation.

Image Segmentation. An object can be easily detected in an image if the object has sufficient contrast from the background. One can use edge detection and basic morphology tools to detect a prostate cancer cell. Key concepts: Edge detection, structuring element, erosion, dilation, segmentation.

Calibration used at the International Ground Robotics Contest. 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 University of Cincinnati’s designs,Bearcat II used 2 CCD cameras and an image tracking device for the front en...

Local Global Navigation. The GPS is a worldwide radio-navigation system formed from a constellation of 24 satellites and their ground stations. hile there are millions of civilian users of GPS world-wide, the system was originally designed for and is operated by the U. S. Department of Defense (DOD). Today, the GPS is finding its usage into cars, a...

Robot Design Homework 1 Kinematic solution for a cylindrical coordinate robot using Mathcad for matrix transformations.

Engineering Robust Intelligent Robots. This presentation was given at the 2010 International Society for Optical Engineering (SPIE meeting.)

Robot Design This is a demonstration of the controls for steering, speed and brakes for the robot jeep.

Robot Design Bearcat Dynamics from Dissertation of Dr. Masoud Ghaffari. Developed using Mathcad.

Robot Design: Kinematic Analysis of the Bearcat Robot. This analysis was included in Dr. Souma Alhaj Ali's Dissertation and was developed in Mathcad.

Robot Design Lecture 2 Introduction to Mathcad. Developing the kinematic and dynamic equations for a robot is quite difficult. Therefore, we use the excellent PTC Program Mathcad to assist with the equations and analysis. Then the task becomes easy and fun. See http://www.ptc.com/engineering-math-software/mathcad for more up to date information.

Robot Design Example. The Bearcat Cub was built by the University of Cincinnati Robot Team in 2003 for the Intelligent Ground Vehicle Contest. It provides an example of a mobile robot designed from ground up.

Robot Design Example. Bearcat III was built in 2003 by the University of Cincinnati Robot Team and entered in the Intelligent Ground Vehicle Contest.

Robot Design Lecture 3 Robot Arm Kinematics. The kinematics of a robot arm are easy to understand if one uses transformations such as the Denavit and Hartenberg (D-H) representation.

Robot Design Lecture 1 Transformations simplify the study of complex geometries and motions of manipulator linkages, simplify the description of the perspective, projective transformation of a camera, reduce to a single 4 by 4 homogeneous coordinates transformation matrix, permit high speed implementation.

Robot Design. Introduction. Robot Design introduces the kinematics and dynamics of a robot manipulator.

Robotics 1 Lecture 20 Flexible Manufacturing Cells. Flexible manufacturing cell concepts are presented to illustrate the importance of an overall cell control for successful manufacturing.

Robotics 1 Lecture 19 Intelligent Robots. Intelligent robots in the news, some history of intelligent control and conceptual designs of the intelligent controller.

Robotics 1 Lecture 18. Interfacing the robot work cell can involve a variety of wired and more recent wireless communication options. This lecture reviews some of the most common ones.

Robotics 1 Lecture 17. Three dimensional vision can be used for line following if one solves the magic matrix. Then the line can be followed with a control system that tracks both the angle and the distance from the line. This method was tested at the International Ground Vehicle Contest.

Robotics 1 Lecture 16 Introduction to machine vision, one of the most powerful sensors that can be added to an intelligent robot.

Robotics 1 Lecture 15 High Level Languages. This lecture reviews some concepts of hierarchical control and high level robot and computer programming languages. A good language makes a robot versatile.

Robotics 1 Lecture 14. Written and oral report formats and examples. Since this course required a project, it was a great time to teach oral and written formats. Some students would eventually attend conferences and present their work. We travelled to Boston, Detroit, Palo Alto, Orland and other fun places for technical conferences.

Robotics 1 Lecture 13 A Programmable Logic Controllers. My students asked about PLCs so I made this lecture mainly from www.plcs.net. A PLC (i.e. Programmable Logic Controller) is a device that was invented to replace the necessary sequential relay circuits for machine control. The PLC works by looking at its inputs and depending upon their state,...

Robotics 1 Lecture 13 Control compensation examples developed for the Bearcat robot at the University of Cincinnati by brilliant students.

Robotics 1 Lecture 12 Work cell simulation and some actual examples.

Robotics 1 Lecture 11 More on control. Servo control is essential for the robot mechanism but non-servo control with Boolean logic may be more appropriate for the cell safety control. This lecture still focusses on servo control.

Robotics 1 Lecture 10. Servo Control. To understand servo control one can made a model and use software to simulate the system.

Robotics 1 Lecture 9 Non-servo and servo controlled robots. Non-servo control is the simplest and cheapest. However, servo control is now the industrial standard.

Robotics 1 Lecture 8 . Methods of Actuation. Hydraulic (most powerful), Electric ( most common), Pneumatic (Lowest cost). Cycle Time Analysis.

Robotics 1 Lecture 7 End Effectors. End effectors can be grippers or process tooling and are essential for a successful application.

Robotics 1 Lecture 6 Wrist configurations. Determine the relationship between various robot applications and the wrist configurations available on commercial robots or automated guided vehicles.

Robotics 1 Lecture 5. Goals: determine the relationship between various robot applications and the arm configurations available on commercial robots or automated guided vehicles; be able to select the appropriate configuration for a robot application; be able to recognize and discuss a certain configuration when you see one.

Robotics 1 Lecture 4 Robot Selection. Selecting a robot for an application requires a good match with requirements and an economic justification and finally a social fit.

Robotics 1 Lecture 3 Justification of a robot for an application is covered in this lecture. Non-economic justification such as using robots for handling radioactive material is important. Economic justification including the internal rate of return concept and computation is also important and can be done with an Excel program.

Robotics 1 Lecture 2 The types, characteristics and applications of industrial robots are covered in this lecture as well as a preview of computing torque for a robot arm.

Robotics 1 Introduction. The purpose of this course is to provide an introduction to the field of industrial robots and their application.

Intelligent Systems Lecture 18. Support Vector Machines. I learned about SVM from my student Saurabh Sarkar, a brilliant researcher. This is one of the presentations he made and I am proud to say that he continued after I retired and completed his PhD. All society benefits from the research work of Saurabh and others.

Intelligent Systems Lecture 17 This adaptive critic paper shows how we apply pattern recognition to a problem of international interest.

Intelligent Systems Lecture 16. Stock prediction is one application of decision making and choosing between lotteries. Remember, a rational decision maker neither loves nor hates gambling.

Intelligent Systems Lecture 15 Fuzzy Logic. Fuzzy logic was made popular by Lotfi Zadeh and is a very different way of thinking about pattern recognition. My students taught me to respect this way of thinking.

Intelligent Systems Lecture 13 Example questions and answers from the Math Works Toolbox and other research.

Intelligent Systems Lecture 14 Genetic Algorithms. This lecture is based on the MS Thesis of Christopher Deters in 1999 who applied genetic algorithms to robot path following.

Intelligent Systems Lecture 12 a Robots in Assembly. When and where and how will they be useful?

Intelligent Systems Lecture 11 a Screwhead ( Bongard ) problem and solution presented as a comic.

Intelligent Systems Lecture 10. Since we are doing projects we need data sets. These links may now be out of date but they should give one the idea that there are large amounts of data available.

Intelligent Systems Lecture 9. Multiresolution calculus presents some conceptual ideas for multiresolution calculus including the basic definition and some application ideas. Whenever multiresolution ideas can be used to reduce the clutter and background noise, they lead to an improved decision process.

Intelligent Systems Lecture 8. Rosenblatt's discovery of the perceptron algorithm changed pattern recognition. One of the simplest perceptrons is a single-layer network whose weights and biases could be trained to produce a correct target vector when presented with the corresponding input vector. The training technique used is called the perceptro...

Intelligent Systems Lecture 7. Neural Nets can be incredibly complicated so it is good to start with ones that can implement AND, OR and XOR gate logic. The XOR logic is needed to solve the Bongard problem.

Intelligent Systems Lecture 6 The perceptron algorithm is one of the most famous error correcting decision procedures. The proof of this algorithm is given in this lecture.

Intelligent Systems Lecture 5 When are two sets separable? When their convex hulls are disjoint. This lecture also presents an introduction to neural nets. Neural nets are a step toward a model of the human brain.

Intelligent Systems Lecture 4. N dimensional geometry provides clear ideas about points, lines, planes, hyperplanes and important pattern recognition facts.

Intelligent Systems Lecture 3 N Dimensional Geometry. Many pattern recognition problems have features with more than a few dimensions. N dimensional geometry gives us a tool for working with such data. Matlab also makes it easy to manipulate such data vectors.

Intelligent Systems Lecture 2. A rational decision maker neither loves nor hates gambling. This lecture shows you how to choose between lotteries using a utility function.

Intelligent Systems Lecture 1 Introduction to pattern recognition. Course objective is to provide a broad understanding of the use of intelligent systems and an experience in specifying, designing and presenting a new intelligent system application.

Robot Vision Lecture 14 Determining a path from waypoint to waypoint with obstacle avoidance is a common problem in navigation.

Robot Vision Lecture 13 To determine the circle which best fits a set of boundary points, a method suggested by George Wecksung of Los Alamos National Laboratory may be used.

Robot Vision Lecture 12 Sometimes a numerical example clarifies the ideas. This example 3D computation from Tayib Samu's paper is presented to clarify the matrix computations.

Robot Vision Lecture 11 Both global and local navigation techniques were necessary for the UC Robot Team in the Intelligent Ground Vehicle Contest and this lecture presents some of their results.

A gnomonic projection is one of the oldest mappings of a sphere to a plane. It maps points on a great circle to a straight line.

Robot Vision Lecture 9 Three dimensional vision can let us guide an unmanned vehicle such as the Bearcat robot as shown in this example.

Robot Vision Lecture 8 The stereo vision principle approach uses the scaling between two coordinate systems to determine the relationship between the physical and image coordinates.

Robot Vision Lecture 7 Webcams and Image Acquisition are critical for real time applications. This presentation shows how to do this with Marlab.

Robot Vision Lecture 6 Segmentation. Segmentation is decomposing the image into separate components. The components may be used to define the objects or match the regions. The mathematical formulation for scene segmentation may be called clustering, which is defined as finding “natural grouping” in a set of measurements.

Lecture 5 Edge Detection. An an introduction to edge detection, six different edge detection techniques are described and illustrated.

Lecture 4 presents an overview of the vision processing used by the Bearcat Cub in the 2006 International Ground Vehicle Competition.

Transformations such as translation, rotation, scaling, projective and perspective are fundamental to our understanding of images. The homogeneous transformation matrix gives a concise description of these.

When I started doing vision we had to build a flying spot scanner to digitize x ray images. Later we started collecting our software into libraries. The KHOROS package was one of the first and best and I used it in my courses for some years. But when Matlab came out with their Image Processing Toolbox, I switched to it and my students were very hap...

Lecture 1 of Robot Vision. This is an introduction and overview of some popular vision techniques from 2001.

The pendulum gives us a good model for a robot arm with a single degree of freedom. With a rigid link, it is natural to drive the rotation by a torque applied to the pinned end and to represent the mass at the center of mass of the link. Other physical variations lead to different robot designs. For example, if we mount the rigid link horizontall...

Controls Lecture 2 Reading assignment and introduction to Matlab for solving a pendulum.

Controls is a senior level course introducing control theory. This is Lecture 1.

Lecture 20 Be able to understand flexible manufacturing cell concepts.

Lecture 19 Intelligent robots in the news

Lecture 18 Understand robotic interfacing methods Parallel interfaces Serial interfaces Web based interfaces Human interfaces

Some examples of three dimensional vision.

Lecture 16 Understand robot intelligence concepts and definitions, Two and three dimensional machine vision, Heuristic problem solving techniques, Programming and sensing for control, Tactile sensors, Applications of intelligent robot systems in flexible manufacturing.

"Thus man is the most intelligent of all animals and so, also, hands are the instruments most suited to an intelligent animal. For it is not because he has hands that he is the most intelligent, as Anaxagoras says, but because he is the most intelligent that he has hands, as Aristotle says, judging most correctly.“ Galen, Greek physician, (c 130-20...

Feasibility study reports in written and oral formats.

Lecture 13 A. A PLC (i.e. Programmable Logic Controller) is a device that was invented to replace the necessary sequential relay circuits for machine control. The PLC works by looking at its inputs and depending upon their state, turning on/off its outputs. The user enters a program, usually via software, that gives the desired results.

Motion control is one of the technological foundations of industrial automation. motion of a product path of a cutting tool motion of an industrial robot arm conducting seam welding motion of a parcel being moved from a storage bin to a loading dock by a shipping cart

Lecture 12 Determine the relationship between various robot applications and the methods of controlling the path available on commercial robots or automated guided vehicles. Examine some examples of actual commercial robots.

Lecture 11 Determine the relationship between various robot applications and the methods of controlling the path available on commercial robots or automated guided vehicles. Be able to distinguish between non-servo and servo motions. Be able to understand servo control for implementing controlled path motion for a robot application.

Lecture 10 The purpose of an annotated bibliography is to save time for a reader by compressing a larger amount of information into a smaller amount. From Websters Bibliography – a list, often with descriptive or critical comments relating to a particular subject The list of works referred to in a text by an author during its production Annotate –...

Lecture 9 Determine the relationship between various robot applications and the methods of controlling the path available on commercial robots or automated guided vehicles. Be able to distinguish between non-servo and servo motions. Be able to select the appropriate method of controlled path motion for a robot application.

Lecture 8 Determine the relationship between various robot applications and the methods of actuation available on commercial robots or automated guided vehicles. Be able to select the appropriate method of actuation for a robot application. Be able to estimate the load force and moments to select the proper method of actuation.

Lecture 7 Determine the relationship between various robot applications and the end effectors and tooling available on commercial robots or automated guided vehicles. Be able to select the appropriate end effector for a robot application. Be able to compute the load force and moments.

Lecture 6 Determine the relationship between various robot applications and the wrist configurations available on commercial robots or automated guided vehicles. Be able to select the appropriate configuration for a robot application. Be able to recognize and discuss a certain configuration when you see one.

Lecture 5 Determine the relationship between various robot applications and the arm configurations available on commercial robots or automated guided vehicles. Be able to select the appropriate configuration for a robot application. Be able to recognize and discuss a certain configuration when you see one.

Lecture 4 Determine the requirements for implementing an industrial robot or automated guided vehicle in an application. Understand need Determine robot requirements Determine installation and operation requirements Consider human relations aspects

Lecture 3 introduces the economic justification topics:.Determine costs associated with the installation of an industrial robot or automated guided vehicle Understand IRR concept Determine cash flow table Compute IRR given the costs using Excel

Introduction to Robotics is a course that focuses on robot applications, economic justification and social implications.

This is the PPT presentation I made in 2009 at the SPIE meeting.