Motivation:

Interaction with the physical world through our machines becomes increasingly important as we expand the frontiers of computer science, the Internet, and information technology. We are faced with new challenges as we design engineering systems to solve problems such as environmental monitoring, security sweeping and surveillance, automated transportation, and even household maintenance.

In such cyber-physical systems, information arrives directly from sensors. Furthermore, physical devices may be commanded to move around to improve information gathering capabilities or accomplish physical tasks. Successful design and deployment of such systems depends on understanding the interplay between sensing, actuation, and computation. By considering these together, one central theme emerges: Understand the information requirements of a particular task so that the complexity of the overall system can be reduced. This offers many benefits, such as improving robustness and reducing costs.

This course provides fundamental concepts for reasoning about the information spaces that arise in these systems. Inferences are made from sensing and actuation information in combination with geometric, topological, or statistical models. This leads to approaches that do not require full state estimation. They instead work by directly manipulating information states. In many cases, the sensing requirements may be dramatically reduced. This leads sensor-based algorithms and even theoretical computation models that depart from standard Turing machines.

Topics:

Sensor models, visibility sensors, sensor networks, inference problems, information spaces, actuation models, minimalist planning, visual sweeps, searching with limited information, pursuit-evasion games, sensor-based navigation tasks, coordinate-free models, stochastic models, nontraditional communication models, sensor-centric models of computation, decidability and complexity for actuated sensor systems.

Course Calendar:

Class #	Date	Lecture Topic	Reading	Annoucements
Week 1
1	8/25	Course overview		handout given
2	8/27	Physical characteristics of sensors	Fraden, Ch. 1,2
Week 2
3	9/1	Sensor examples, physical state spaces	Sec 2, 3.1 of TR
4	9/3	Physical state spaces	Sec 3.1 of TR
Week 3
5	9/8	Bodies, fields, time	Sec 3.1 of TR
6	9/10	Virtual sensor models	Sec 3.2 of TR
Week 4
7	9/15	Virtual sensor models	3.2,3.3 of TR
	9/17	CLASS CANCELED
Week 5
8	9/22	Virtual sensor models	Sec 3.2 of TR
9	9/24	Virtual sensor models	Sec 3.2 of TR
Week 6
10	9/29	Sensor lattices	Sec 3.4 of TR
11	10/1	Complicated sensor models	Sec 3.3 of TR
Week 7
12	10/6	Spatial filtering	Sec 4.1 of TR
13	10/8	Temporal filtering	Sec 4.2 of TR
Week 8
14	10/13	Nondet, Bayesian filters	Sec 4.2 of TR
15	10/15	Combinatorial filters	Sec 4.3 of TR
Week 9
16	10/20	Speaker: Dmitry Yershov	Paper #23
17	10/22	Speaker: Katrina Gossman	Paper #8
Week 10
18	10/27	Speaker: Charlie Mooney	Paper #18
19	10/29	Speaker: Syed Bilal Mehdi	Paper #15
Week 11
20	11/3	Speaker: Leonardo Bobadilla	Paper #3
21	11/5	Speaker: Man Ki Yoon	Paper #25
Week 12
22	11/10	Speaker: Taylor Johnson	Paper #9
23	11/12	Speaker: Jung Eun Kim	Paper #27
Week 13
24	11/17	Speaker: David Caballero	Paper #29
25	11/19	Speaker: Devin Bonnie	Paper #28
Week 14
26	12/1	Speaker: Oscar Sanchez	Paper #20
27	12/3	Speaker: Ari Blumenthal	Paper #21
Week 15
28	12/8	Speaker: Max Katsev	Paper #6

Materials Related to Lectures:

• Fraden: Handbook of Modern Sensors: Physics, Designs, and Applications, by Jacob Fraden, 2003.
• The Measurement, Instrumentation and Sensors Handbook, CRC Press, 2630 pages!, 1998.
• TR: Filtering and Planning in Information Spaces (CS UIUC Tech. Report, Oct. 2009)
• Slides on Virtual sensor models, preimages, sensor lattices

Paper List:

1. Directional Sensor Placement with Optimal Sensing Range, Field of View and Orientation, Yahya Esmail Osais, Marc St-Hilaire, Fei R. Yu, Download
2. Active and Passive Acoustics to Locate and Study Fish, David A. Mann, Anthony D. Hawkins, and J. Michael Jech, Download
3. On Information Invariants in Robotics, B. R. Donald, AIJ, 72: 217-304, 1995. Long Version Short Version
4. On Comparing the Power of Robots, J. M. O'Kane and S. M. LaValle, International Journal of Robotics Research, 27:1, 5-23, 2008. Download
5. Simple robots in polygonal environments: A hierarchy, J. Brunner, M. Mihalak, S. Suri, E. Vicari and P. Widmayer, Algorithmic Aspects of Wireless Sensor Networks, pp. 111-124, 2008. Download
6. The Power of a Pebble: Exploring and Mapping Directed Graphs, Michael A. Bender, Antonio Fernandez, Dana Ron, Amit Sahai, and Salil Vadhan, Information and Computation Volume 176, Issue 1, 10 July 2002, Pages 1-21, Download
7. Attentional Landmarks and Active Gaze Control for Visual SLAM, S. Fintrop, P. Jensfelt, IEEE TRANSACTIONS ON ROBOTICS, VOL. 24, NO. 5, OCTOBER 2008, pp. 1054-1065.Download
8. Cooperative Self-Organization in a Heterogeneous Swarm Robotic System, F. Ducatelle, G. A. DiCaro, and L. M. Gambardella, Download
9. A Distributed boundary detection algorithm for multi-robot systems, J. McLurkin and E. D. Demaine, Download
10. Algebraic Approach for Recovering Topology in Distributed Camera Networks, E. J. Lobaton, P. Ahammad, S. S. Sastry, Download
11. Neighborhood-Based Topology Recognition in Sensor Networks, ekete, S.P. and Kroller, A. and Pfisterer, D. and Fischer, S. and Buschmann, C., Download
12. Boundary recognition in sensor networks by topological methods, Wang, Yue and Gao, Jie and Mitchell, Joseph S.B., Download
13. Sensor Network Navigation without Locations, Mo Li, Yunhao Liu, Jiliang Wang, and Zheng Yang, Download
14. Energy-efficient target tracking with a sensorless robot and a network of unreliable one-bit proximity sensors, Jason M. O'Kane and Wenyuan Xu, 2009 IEEE International Conference on Robotics and Automation Download
15. Sensor-Based Exploration: The Hierarchical Generalized Voronoi Graph, H. Choset and J. Burdick, Download
16. Indoor Navigation with Uncertainty using Sensor-Based Motions, M. Khatib, B. Bouilly, T. Simeon, R. Chatila, Download
17. A provably complete exploration strategy by constructing Voronoi diagrams, J. Kim, F. Zhang, and M. Egerstedt, Download
18. Simple Robots with Minimal Sensing: From Local Visibility to Global Geometry, S. Suri, E. Vicari, P. Widmayer, Download
19. Minimalist counting in sensor networks (Noise Helps), Y.M. Baryshnikov, E.G. Coffman, K.J. Kwak, and Bill Moran, Download
20. Integrating topological and metric maps for mobile robot navigation: A statistical approach, S. Thrun, J. Gutmann, D. Fox, W. Burgard, and B. Kuipers, roceedings of the National Conference on Artificial Intelligence. JOHN WILEY & SONS LTD, 1998, pp. 989-996, Download
21. Counting targets with mobile sensors in an unknown environment, B. Gfeller, M. Mihalak, S. Suri, E. Vicari, and P. Widmayer, Algorithmic Aspects of Wireless Sensor Networks, pp. 32-45, 2008, Download
22. The complexity of sensing by point sampling, Y-B. Jia and M. Erdmann, WAFR 1994 Download
23. Real-Time Simultaneous Localisation and Mapping with a Single Camera, A. J. Davison, ICCV 2003, Download
24. Tracking a moving object with a binary sensor network (2003) by Javed Aslam, Zack Butler, Florin Constantin, Valentino Crespi, George Cybenko, Daniela Rus, Download
25. Tracking multiple targets using binary proximity sensors (2007) by Jaspreet Singh, Rajesh Kumar, Upamanyu Madhow, Subhash Suri, Richard Cagley, Download
26. Learning Combinatorial Map Information from Permutations of Landmarks, B. Tovar, L. Freda and S. M. LaValle, to appear in the International Journal of Robotics Research, Download
27. Target Counting Under Minimal Sensing: Complexity and Approximations, Sorabh Gandhi, Rajesh Kumar, and Subhash Suri, ALGOSENSORS 2008, Download
28. Distributed Coverage with Multi-Robot System, C. Kong, N. Peng, I. Rekleitis Proceedings of the 2006 IEEE International Conference on Robotics and Automation. Orlando, Florida - May 2006. Download
29. Passive Mobile Robot Localization within a Fixed Beacon Field, Carrick Detweiler, John Leonard, Daniela Rus, and Seth Teller, WAFR 2006, Download