Robotics and Intelligent Systems
(MAE 345)
Fall 2009
Princeton University
School of Engineering and Applied Science
Robotics and Intelligent Systems, MAE 345, provides students with a working knowledge of methods for design and analysis of robotic and intelligent systems. Particular attention is given to modeling dynamic systems, measuring and controlling their behavior, and making decisions about future courses of action. The content is necessarily broad, and the course level is introductory. The intent is to motivate and prepare students to conduct research projects and for further study through advanced courses in related areas.
Prerequisites for the course are the same as those of the Certificate Program in Robotics and Intelligent Systems; the course is open to juniors and seniors who have applied by written proposal to the instructor. The course is particularly accessible to those majoring in Chemical Engineering, Civil and Environmental Engineering, Computer Science, Electrical Engineering, Mechanical and Aerospace Engineering, Operations Research and Financial Engineering, Philosophy, and Psychology.
Course Format
Two lectures are given each week of the regular term. All reading material for the course is accessible on the Internet, much of it compiled in the Robotics and Intelligent Systems Virtual Textbook and in Lecture Slides (copies of the 2007 slides can be accessed below). Three to five computer-based ("Virtual Laboratory") assignments are given during the term, and a final term paper/project is due on the Dean's Date. Students typically use MATLAB and Simulink application software in preparing assignments.
Course Content
- System Modeling
- Biological and Cognitive Paradigms
- Dynamical Systems
- Turing Machines and Concepts of Machine Intelligence
- The Declarative-Procedural-Reflexive Hierarchy
- Intelligent Agents
- Principles of Control
- Open- and Closed-Loop Control
- Optimality and Constraints
- Stability and Performance
- Control Actuation
- Principles of Measurement and Estimation
- Sensors and Sensing
- Probability and Error Models
- Sensor-based Estimation
- Classifiers
- Vision and Image Analysis
- Principles of Decision-Making
- Crisp and Fuzzy Logic
- Decision Trees
- Case-based Reasoning
- Bayesian Belief Networks
- Path Planning
- Voronoi Diagrams
- Numerical Methods
- Evaluation and Search
- Monte Carlo Simulation
- Genetic Algorithms
- Simulated Annealing
- Particle Swarm Optimization
- Neural Networks
- Static Networks
- Associative Networks
- Cerebellar Model Articulation Controller
- Expert Systems
- Production Systems
- Forward Chaining
- Backward Chaining
As presented in the linked Syllabus, the course is divided in two parts. Half of the course is devoted to Robotics, and half addresses Intelligent Systems.
Examples of Previous Term Paper Topics
The term papers were required to address one of the following objectives:
- Simulation of a robotic device
- Design and simulation of a neural network
- Design and simulation of an intelligent system
Topics for completed term papers include:
- Ebots: Application of an Unguided Evolutionary Neural Network to Control Virtual Evolving Robots
- Use of Neural Networks to Distinguish Between Notes Played by Different Musical Instruments
- Using Markov Decision Processes to Decentralize Earthquake Relief Response in Afghanistan
- Evaluation of Neural Networks' Ability to Generalize in Pattern Recognition
- Federal Land Management Expert System
- Analysis of the US Postal Service as an Intelligent System
- Design of a Neural Network for Classification of News Articles
- Dynamic Modeling and Simulation of Flocking Behavior for Sandhill Cranes and Tufted Ducks
- Java Simulation of Autonomous Vehicles
- Hardware/Software Implementation of Laser-Pointer Control Using Head Motion
- Control of an Inverted Pendulum Using a Puma-Style Robot
- Design and Simulation of an Automated Mailman
- Use of a Modified Game of Life for Basic Image Enhancement
- Robotic Thermo-Probe for Sub-Surface Exploration of Europa
- Genetics-Based Learning System for Playing Poker
- Neural Network Design for Automated Spectral Calibration
- Neural Network for Scheduling Lateral Control Gains of a Large Jet Transport
- Neural Networks for Footstep Recognition
- Navigation Through a Randomly Generated Maze
- Path Following of a Nonholonomic Wheeled Mobile Robot
- Obstacle Avoidance Algorithms in Hovercraft Maneuvering Situations
- A Semi-Autonomous Robotic Camera for Filming Sporting Events
- Intelligent System for Attack Analysis and Defensive Deployment
- Predicting Student Course Evaluations with Neural Networks
- Genetic Algorithm for Course Scheduling
- Use of a Neural Network to Schedule Linear-Quadratic Control Gains for a Nonlinear Mixing Tank
- Simulation of an Evolutionary Baseball League
- A Robotic Baseball Pitcher
- Application of Neural Networks to EEG for Alertness Prediction
- Stock Market Prediction with Neural Networks
- Simulation of a Gliding Micro-Air Vehicle
- Trajectory Computation for an Autonomous Planetary Lander
- An Intelligent System for Emergency Medical Assessment
- Using a Neural Network Predictive Controller to Manage Type-1 Diabetes
- Control of an Autonomous Helicopter
- Genetic Strategy for Exploring the Martian Surface
- Cerebellar Model Articulation Control of a Solenoid Actuator
- Image Processing Using Neural Networks
- Vehicle Stability Control: Antilock Braking for Lateral Traction Control
- Use of Restricted Boltzmann Machines to Predict Retinal Ganglion Cell Response to White Noise Stimuli
- Using a Neural Network to Process Face Portraits to Determine Race and Sex
- An Intelligent System for Emergency Response
- State Estimation Methods For a Differential-Drive Robot
- Robust Motion Planning: Using Human-Robot Interaction to ensure success
- Simulation of 3D Obstacle Avoidance for an Autonomous Robotic Lamprey
- Design and Simulation of a System of Coordinated Soccer-Playing Mobile Robots
- Optimizing Team Movement in RoboCup
- Neural Networks for Image Compression
- Design and Simulation of a Dextrous Robotic Hand
- Neural Network Control of a Biped Walking Robot
- Neural Network for Robot Tracking and Control
- Visual Odometry Using SURF Feature Detection
- Neural Network to Play Five-in-a-Row
- Dynamic SAGA Animats
- SubSpace Methods for Image Recognition
- Genetic Algorithm and Neural Network to Solve an Extended Traveling Salesman ("Traveling Shopper") Problem
- Trading Stocks with a Radial Basis Function Network
2009 Lecture Slides
SELECTED REFERENCES
- Robotics
- H. Asada and J.-J. Slotine, Robot Analysis and Control, J. Wiley & Sons, 1986.
- C. Asfahl, Robots and Manufacturing Automation, J. Wiley & Sons, 1992.
- D. Auslander, J. Ridgely, and J. Ringgenberg, Control Software for Mechanical Systems, Prentice-Hall, 2002.
- G. Bekey, Autonomous Robots, MIT Press, 2005.
- M. Brady, J. Hollerbach, T. Johnson, T. Lozano-Perez, and M. Mason, Robot Motion: Planning and Control, MIT Press, 1984.
- C. Close and D. Frederick, Modeling and Analysis of Dynamic Systems, Houghton Mifflin, 1993.
- R. Dorf, Robotics and Automated Manufacturing, Reston (Prentice-Hall), 1983.
- J. Jones and A. Flynn, Mobile Robots, A. K. Peters, 1993.
- P. McKerrow, Introduction to Robotics, Addison-Wesley, 1991.
- A. Mutambara, Mechatronics and Robotics: Design and Applications, CRC Press, 1999.
- Y. Nakamura, Advanced Robtics: Redundancy and Optimization, Addison-Wesley, 1991.
- U. Nehmzow, Mobile Robotics: A Practical Introduction, Springer-Verlag, 2000.
- S. Niku, Intoduction to Robotics, Prentice Hall, 2001.
- K. Ogata, System Dynamics, Prentice-Hall, 1998.
- B. A. Ogunnaike and W. H. Ray, Process Dynamics, Modeling, and Control, Oxford University Press, 1994.
- D. Rowell and D. Wormley, System Dynamics: An Introduction, Prentice-Hall, 1997.
- B.-Z. Sandler, Robotics: Designing the Mechanisms for Automated Machinery, Prentice-Hall, 1991.
- B. Siciliano and O. Khatib, Springer Handbook of Robotics, Springer, 2008.
- J. Shearer, B. Kulakowski, and J. Gardner, Dynamic Modeling and Control of Engineering Systems, Prentice-Hall, 1997.
- D. Smith, Introduction to Dynamic Systems Modeling for Design, Prentice-Hall, 1994.
- W. Snyder, Industrial Robots: Computer Interfacing and Control, Prentice-Hall, 1985.
- M. Spong and M. Vidyasagar, Robot Dynamics and Control, J. Wiley & Sons, 1989.
- A. Staugaard, Jr., Robotics and AI: An Introduction to Applied Machine Intelligence, Prentice-Hall, 1987.
- R. Stengel, Optimal Control and Estimation, Dover Publications, 1994. (originally published as STOCHASTIC OPTIMAL CONTROL; Theory and Application, J. Wiley & Sons, 1986.)
- W. Wolovich, Robotics: Basic Analysis and Design, Holt, Rinehart, and Winston, 1987.
- R. Woods and K. Lawrence, Modeling and Simulation of Dynamic Systems, Prentice-Hall, 1997.
- Intelligent Systems
- Albus, J. I., and Meystel, A. M., Engineering of Mind, J. Wiley & Sons, 2001.
- P. Antsaklis and K. Passino, An Introduction to Intelligent and Autonomous Control, Kluwer, 1993.
- R. Arkin, Behavior-Based Robotics, Bradford, 1998.
- P. Baldi and S. Brunak, Bioinformatics, Bradford, 1998.
- C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
- R. Brooks, Cambrian Intelligence, Bradford, 1999.
- E. Charniak and D. McDermott, Introduction to Artificial Intelligence, Addision-Wesley, 1985.
- P. Cohen and E. Feigenbaum, ed., The Handbook of Artificial Intelligence, William Kaufmann, 1982.
- J. Giarratano and G. Riley, Expert Systems : Principles and Programming, PWS Publishing, 1994.
- D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
- M. Gupta, L. Jin, and N. Homma, Static and Dynamic Neural Networks, J. Wiley & Sons, 2003.
- D. Hofstadter, Godel, Escher, Bach: An Eternal Golden Braid, Vintage, 1980.
- J. Holland, Adaptation in Natural and Artificial Systems, MIT Press, 1994.
- J. Holland, Hidden Order, Addison-Wesley, 1995.
- D. Hudson and M. Cohen, Neural Networks and Artificial Intelligence for Biomedical Engineering, IEEE Press, 2000.
- L. Jain and C. de Silva, Intelligent Adaptive Control: Industrial Applications, CRC Press, 1999.
- S. Jain, D. Osherson, J. Royer, and A. Sharma, Systems That Learn, Bradford, 1999.
- D. Kortenkamp, R. Bonasso, and R. Murphy, ed., Artificial Intelligence and Mobile Robots, AAAI Press, 1998.
- C. Lau, ed., Neural Networks: Theoretical Foundations and Analysis, IEEE Press, 1992.
- P. McCorduck, Machines Who Think, W. H. Freeman, 1979.
- M. Norgaard, O. Ravn, N. Poulsen, and L. Hansen, Neural Networks for Modelling and Control of Dynamic Systems, Springer-Verlag, 2000.
- J. Pearl, Probabilistic Reasoning: Networks of Plausible Inference, Morgan Kaufmann, 1988.
- R. Penrose, The Emperor's New Mind, Penguin Books, 1989.
- B. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996.
- E. Sanchez-Sinencio and C. Lau, ed., Artificial Neural Networks: Paradigms, Applications, and Hardware implementations, IEEE Press, 1992.
- H. Simon, Sciences of the Artificial, MIT Press, 1996.
- R. Stengel, "Toward Intelligent Flight Control," IEEE Trans. Systems, Man, and Cybernetics, Vol. 23, No. 6, Nov-Dec 1993, pp. 1699-1717.
- R. Sutton and A. Barto, Reinforcement Learning, Bradford, 1998.
- S. Tanimoto, The Elements of Artificial Intelligence Using Common Lisp, W. H. Freeman & Co., 1995.
- J. Thompson, Empirical Model Building, J. Wiley & Sons, 1989.
- K. P. Valavanis and G. N. Saridis, Intelligent Robotic Systems: Theory, Design, and Applications, Kluwer, 1992.
- R. Veroff, ed., Automated Reasoning and Its Applications, MIT Press, 1997.
- L.-X. Wang, A Course in Fuzzy Systems and Control, Prentice-Hall, 1997.
- S. Weiss and C. Kulikowski, Computer Systems That Learn, Morgan Kaufmann, 1991.
- D. A. White and D. A. Sofge, Handbook of Intelligent Control, Van Nostrand Reinhold, 1992.
- P. Winston and R. Brown, Artificial Intelligence: An MIT Perspective, MIT Press, 1979.
WEB LINKS
Tools and Concepts
Libraries and Technical Report Servers
Databases and Jump Sites
Simulation, Instrumentation, and Robot Kits
Programs, Organizations, and Contests
http://www.princeton.edu/~stengel/MAE345.html
key words: robotics, intelligent systems, control systems, robot vehicles, industrial robots, optimization, numerical methods, neural networks, expert systems, task planning, Monte Carlo evaluation
Last updated Januaary 28, 2010.
Copyright 2010 by Robert F. Stengel. All rights reserved.