1. robotics.usc.edu/~embedded Physics-based Sensing and State Estimation Algorithms for Robotic Sensor Networks Gaurav. S. Sukhatme Robotic Embedded Systems Lab University of Southern California IPAM 5/17/2002

2. robotics.usc.edu/~embedded Sensing and State Estimation Algorithms for Robotic Sensor Networks based on Nature Gaurav. S. Sukhatme Robotic Embedded Systems Lab University of Southern California IPAM 5/17/2002

3. robotics.usc.edu/~embedded Robotics and Sensor Networks Sensor Nets: Couple information gathering, processing, and communication resources to the physical environment to monitor, understand, and ultimately affect the physical world Robots: –Improve nature and quality of sensing –Effect changes in the physical world by manipulation and motion

4. robotics.usc.edu/~embedded Sensor Coordinated Actuation Physical phenomena occur at diverse spatial and temporal scales Synoptic sampling at arbitrarily fine spatio-temporal scales is impossible with a finite number of sensors Actuation provides a means to focus sensing where it is needed, when it is needed To enable sensing of uncharted phenomena, in dynamic, unknown, and partially observable environments, actuation cannot be based on planning a priori Actuation for improved sensing critically depends on using sensor data in situ to reposition and refocus sensors

5. robotics.usc.edu/~embedded Challenges Coordinate the actions of large number of actuated nodes carrying sensors without central control (or even representation) Sub-problems: Localization, Navigation, Deployment, Mapping, Calibration, Energy Management, Connectivity

6. robotics.usc.edu/~embedded Localization, Mapping, Deployment & Navigation Cooperative Localization and Mapping: robots estimate poses relative to each other using intermittent observations and maximum likelihood estimation Deployment: robots spread out through local interactions (potential fields) to cover an unknown environment with sensors Navigation: robots leave trail markers (like ants) and navigate from source to sink

7. robotics.usc.edu/~embedded Localization (x1,y1) (x0,y0) (x2,y2) (x3,y3) (x4,y4) (x5,y5) (x6,y6) (x7,y7) x y

8. robotics.usc.edu/~embedded Localization (x1,y1) (x0,y0) (x2,y2) (x3,y3) (x4,y4) (x5,y5) (x6,y6) (x7,y7) x y

9. robotics.usc.edu/~embedded Localization Past approaches (ours and others) include –filtering inertial sensors for location estimation –using landmarks (based on vision, laser etc.) –using maps Algorithms vary from Kalman filters, to Markov localization to Particle filters Our approach for sensor networks –Exploit communication for in-network localization –Physics-based models

10. robotics.usc.edu/~embedded Static Localization System contains beacons and beacon detectors Assumptions: beacons are unique, beacon detectors determine correct identity. Static localization: determine the relative pose of each pair of beacons/detectors

11. robotics.usc.edu/~embedded Mesh Definition: Damped spring mass system

12. robotics.usc.edu/~embedded Mesh Energy Kinetic energy Potential energy

13. robotics.usc.edu/~embedded Mesh Forces and Equations of Motion Forces Equations of motion

14. robotics.usc.edu/~embedded Encoding

15. robotics.usc.edu/~embedded SLAM: Simultaneous Localization and Mapping

16. robotics.usc.edu/~embedded Multi-robot SLAM

17. robotics.usc.edu/~embedded Sensor Network Calibration

18. robotics.usc.edu/~embedded Extension Relaxation not the only way to minimize energy Significantly more efficient techniques exist –Equivalent to MLE approaches

19. robotics.usc.edu/~embedded Team Localization using MLE Construct a set of estimates H = {h} where: h is the pose of robot r at time t. Construct a set of observations O = {o} where o is either: the measured pose of robot r b relative to robot r a at time t, or the measured change in pose of robot r between times t a and t b. Assuming statistical independence between observations find the set of estimates H that maximizes:

20. robotics.usc.edu/~embedded Approach Equivalently, find the set H that minimizes:

21. robotics.usc.edu/~embedded Gradient-based Estimation Each estimate Each observation Measurement uncertainty assumed normal Relative  Absolute

22. robotics.usc.edu/~embedded Gradient Descent Compute set of poses q that minimizes U(O|H) Gradient-based algorithm

23. robotics.usc.edu/~embedded Results (small environment)

24. robotics.usc.edu/~embedded Range Error vs. Time Robots bump into each other

25. robotics.usc.edu/~embedded Bearing Error vs. Time

26. robotics.usc.edu/~embedded Orientation Error vs. Time

27. robotics.usc.edu/~embedded Results (large environment)

28. robotics.usc.edu/~embedded Team Localization –Runs on any platform as long as it can compute its motion via inertial sensing –Unique beacons: robots, people, fixed locations etc. –No model of the environment –Indifferent to changes in the environment –Robust to sensor noise –Permits both centralized and distributed implementation –Will soon be available as part of Player on sourceforge as a service A. Howard, M. Mataric, G. Sukhatme, Localization for Mobile Robot Teams: A MLE Approach, USC Technical Report IRIS-01-407, 2001 A. Howard. M. Mataric, G. Sukhatme, Localization for Mobile Robot Teams using MLE, submitted to IROS 2002 A. Howard, M. Mataric, G. Sukhatme, Team Localization: A MLE Approach, submitted to IEEE TRA

29. robotics.usc.edu/~embedded The Deployment Algorithm Each robot is controlled according to virtual forces

30. robotics.usc.edu/~embedded Deployment Stages

31. robotics.usc.edu/~embedded Static Deployment using a Potential Field

32. robotics.usc.edu/~embedded Coverage vs. Time

33. robotics.usc.edu/~embedded Physics is Great but … Many social animals exhibit self organized, fault tolerant, redundant, scalable, real time behavior Social insects in particular exhibit all of the above and are remarkably simple –The emergent complexity is in the interaction –The system works because of communication Robot navigation using trail following

34. robotics.usc.edu/~embedded Resource Transportation Team of N robots starts from a `home’ location A in an unknown environment Explore to find supply of resource at B Carry resource home and return for more Exemplified by foraging of ants & bees

35. robotics.usc.edu/~embedded Inspiration: Ant Foraging Bring food back to the nest Lay chemical trails for each other to follow (stigmergy)

36. robotics.usc.edu/~embedded Trails in Localization Space Abstract away the chemical trail Communicate landmarks in shared localization space Announce trails only when successful Pros: adaptive, robust, distributed, scalable + lightweight, simple Cons: not necessarily optimal, robots must be localized

37. robotics.usc.edu/~embedded Implementation requirements Low bandwidth communication Exploration/Navigation: –In order to generalize, trail following should be independent of local navigation strategy –Local navigation can (must!) be designed to match the environment –Large populations make random walk attractive Localization: –trail following should be robust wrt. realistic localization error

38. robotics.usc.edu/~embedded Robot “Crumb Trails” A crumb is a landmark/signpost in localization space, indicating the direction to move next Trail represented by a linked list of “crumb” data structures Each crumb contains –a position (x,y) –a suggested heading  –a timestamp t

39. robotics.usc.edu/~embedded Trail Laying Algorithm: the private crumb list Moving ? >Ps since last crumb ? Add crumb to private list N N Y Y start

40. robotics.usc.edu/~embedded Trail Laying Algorithm: the public crumb list Carrying resource ? Reached Goal ? N Y N start Reached Goal ? N Announce private crumbs, clear private crumb list Y Y

41. robotics.usc.edu/~embedded Trail Laying Algorithm: fresh crumbs Received announced crumb ? Is there a crumb older than Qs ? N Y Y start Add crumb to public list Delete crumb N

42. robotics.usc.edu/~embedded Trail Reading Obstacle Robot Suggested movement = vector sum of crumbs within sense radius

43. robotics.usc.edu/~embedded Experiments Localization: Gaussian error model Communication: UDP broadcast Local navigation designed for environment Tested with three controllers –random –directed –ant

44. robotics.usc.edu/~embedded RandomWalk SteerToAvoid FollowHeading Frustration switch Heading to goal Avg crumb heading Directed RandomWalk SteerToAvoid FollowHeading Frustration switch Ant

45. robotics.usc.edu/~embedded Results

46. robotics.usc.edu/~embedded Results - Population Size

47. robotics.usc.edu/~embedded Results - Localization Error

48. robotics.usc.edu/~embedded

49. Resource Transport Summary Demonstrated cooperative search and transport Our trail laying/following method –is robust wrt. real-world odometry error –has low computation and bandwidth requirements –scales linearly with population –is independent of navigation strategy –scales to N sources and sinks

50. robotics.usc.edu/~embedded Dynamic Deployment (Patrolling) using Beacons

51. robotics.usc.edu/~embedded Conclusions Sensor Nets: sensing and communications coupled to the physical world Robots can: –Improve nature and quality of sensing –Effect changes in the physical world by manipulation and motion Localization and Deployment: efficient, scalable services for robotic sensor networks

52. robotics.usc.edu/~embedded More at http://robotics.usc.edu/~embedded http://robotics.usc.edu/~embedded Robot simulator http://playerstage.sourceforge.net Support – NSF, ONR, DOE, DARPA, and Intel