MAE6790 | Intelligent Sensor Planning and Control

Fall 2017

Class will meet on Mondays and Fridays, at 2:55PM – 4:10PM, Location TBA

Silvia Ferrari
Department of Mechanical and Aerospace Engineering
Cornell University


Course Description

An introductory course on learning and intelligent-systems techniques for the modeling, planning, and control of dynamic sensors. Methods for intelligent sensor fusion, sensor management, and mobile sensor navigation and control will be covered in detail in this course. Topics also include neural networks, Bayesian networks, and information theory, as they apply to problems drawn from environmental monitoring, sensor path planning, sensing-and-pursuit games, target tracking, classification, and satisficing searches.


Course Outline

  • Introduction

Scope of the course
Motivating Sensing Applications

  • Sensor Modeling

Platform dynamics
Targets
Sensor Field-of-View (FoV)
Sensor Measurements
Environmental Variability and Feedback

  • Sensor Performance and Estimation

Coverage
Probability of Detection and False Alarms
Classification
Tracking
Localization

  • Sensor Placement, Navigation, and Control

Placement Optimization
Path Planning
Trajectory Optimization
Integrated Control and Navigation

Elements from the following topics will be reviewed throughout the semester:
Linear systems, systems theory, graph theory, probability theory, information theory, optimal control, and planning.


Principal Sources

Textbook:

Silvia Ferrari and Tom A. Wettergren Information-driven Planning and Control, CRC Press, to appear in 2017

References:

  1. David A. White and Donald A. Sofge, Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, Van Nostrand Reinhold, NY, 1992.
  2. H. K. Khalil, Nonlinear Systems, Third Ed., Prentice Hall, 2002
  3. R. F. Stengel, Optimal Control and Estimation, Dover Publications, 1994.
  4. D. E. Kirk, Optimal Control Theory; an Introduction, Prentice-Hall, 1970.
  5. R. J. Vanderbei, Linear Programming: Foundations and Extensions, Kluwer, 1997.
  6. F. V. Jensen, Bayesian Networks and Decision Graphs, Springer, 2002.
  7. J. Si, A. Barto, W. Powell, D. Wunsch, Eds., Learning and Approximate Dynamic Programming: Scaling Up to the Real World, IEEE Press and John Wiley & Sons, 2004.
  8. P. Antsaklis and K. Passino, An Introduction to Intelligent and Autonomous Control, Kluwer, 1993.
  9. E. Charniak and D. McDermott, Introduction to Artificial Intelligence, Addision-Wesley, 1985.
  10. P. Cohen and E. Feigenbaum, ed., The Handbook of Artificial Intelligence, William Kaufmann, 1982.
  11. J. Kowalik, ed., Knowledge Based Problem Solving, Prentice-Hall, 1986.
  12. L.-X. Wang, A Course in Fuzzy Systems and Control, Prentice-Hall, 1997.
  13. J. Pearl, Probabilistic Reasoning: Networks of Plausible Inference, Morgan Kaufmann, 1988.
  14. D. Hudson and M. Cohen, Neural Networks and Artificial Intelligence for Biomedical Engineering, IEEE Press, 2000.
  15. C. Lau, ed., Neural Networks: Theoretical Foundations and Analysis, IEEE Press, 1992.
  16. R. Stengel, “Toward Intelligent Flight Control,” IEEE Trans. Systems, Man, and Cybernetics, Vol. 23, No. 6, Nov-Dec 1993, pp. 1699-1717.
  17. J. Holland, Adaptation in Natural and Artificial Systems, MIT Press, 1994.
  18. R. Sutton and A. Barto, Reinforcement Learning, Bradford, 1998.
  19. G. Weiss, Multiagent Systems, MIT Press, 1999.
  20. M. Wooldridge, An Introduction to MultiAgent Systems, J. Wiley & Sons, 2002.