Networked Robotics: From Distributed Robotics to Sensor Networks

1. Networked Robotics:From Distributed Robotics to Sensor Networks Gaurav S. Sukhatme Center for Robotics and Embedded Systems Center for Embedded Networked Sensing Computer Science Department University of Southern California gaurav@usc.edu http://robotics.usc.edu/resl http://robotics.usc.edu/~gaurav

2. Introduction • Synoptic sensing: sense everywhere in parallel • Enablers: small computers, sensors, radios • Role of robotics: Deploy sensors, Localize sensors, Replenish and repair network • Potential Applications: • Ecosystem bio-complexity monitoring • Marine microorganism monitoring • Structural health monitoring • … http://robotics.usc.edu/~gaurav

3. Network Deployment http://robotics.usc.edu/~gaurav

4. Deployment Constraints and Tradeoffs • Connectivity • Final/Intermediate • K-connectedness, K-degree (density) • Visibility • Communication visibility, sensing visibility • Efficiency • How many nodes ? How quickly ? http://robotics.usc.edu/~gaurav

5. Network Repair http://robotics.usc.edu/~gaurav

6. Repair Constraints • Minimal Intervention • Smallest number of nodes are subjected to small displacements • Small number of new nodes deployed • Speed • Faster than (re)deployment • Preserve connectivity/visibility http://robotics.usc.edu/~gaurav

7. Multi-Robot Task Allocation (MRTA) B B http://robotics.usc.edu/~gaurav

8. MRTA Constraints • Speed of event detection • ‘Robot only’ solution could be slow • Network effectively extends sensing range • Speed of response • Simple vs. complex robots • Computation on the robots vs. in-network http://robotics.usc.edu/~gaurav

9. Today • Network deployment of robotic nodes • Sensor coverage and line-of-sight connectivity • Sensor coverage and k-neighbor connectivity • Network deployment of static nodes using mobile robots • Sensor coverage and fast exploration, no environment map • Network repair using mobile robots • Speed, no map of the environment • Multi-robot task allocation using a network • Navigation: No central planning, no constraints on adding robots dynamically • Adaptive sampling http://robotics.usc.edu/~gaurav

10. Deployment • Assumptions • Robotic nodes initially confined to a small area • Each node has a finite sensing radius Rs and communication radius Rr • Goal • Maximize total sensor coverage such that each node has k neighbors within communication range • Approach • Repulsive potential field with damping and switching http://robotics.usc.edu/~gaurav

11. The Deployment Algorithm Each robot is controlled according to virtual forces Andrew Howard, Maja J. Mataric and Gaurav S. Sukhatme, "Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem," in Proceedings of the International Symposium on Distributed Autonomous Robotic Systems, 2002. http://robotics.usc.edu/~gaurav

12. Static Deployment using a Potential Field • Tuning the damping term • Use value that gives critical damping for a two node system • Rs and Rr not used explicitly • No control over k http://robotics.usc.edu/~gaurav

13. Modified Potential Field Approach If degree > k Repel neighbors (increase coverage) else Attract neighbors (maintain degree) Sameera Poduri and Gaurav S. Sukhatme, "Constrained Coverage for Mobile Sensor Networks," IEEE International Conference on Robotics and Automation, 2004 http://robotics.usc.edu/~gaurav

14. Role of Rr/Rs d • d<=2Rs Coverage depends on d • d>2Rs Coverage independent of d • 2 regimes Rr> 2Rs Rr<= 2Rs d d d http://robotics.usc.edu/~gaurav

15. Evaluation • Performance metric • Coverage: Area covered/ NπRs2 • Compare with • Regular tilings • Random Networks (uniformly distributed) • Centralized iterative approach http://robotics.usc.edu/~gaurav

16. Regular k-Tilings • Rr<= 2Rs and large N • Exact formulas for coverage for k = 0,1,2,3,4,6 http://robotics.usc.edu/~gaurav

17. Random Networks • Uniformly distributed random network – Poisson distribution • Probability of exactly m nodes in an area S : • Degree depends on ρ • For , find ρ such that http://robotics.usc.edu/~gaurav

18. A Centrally Computed Baseline • Generate a configuration prior to deployment (assumes an environment map) • Loop: • Start with a network N of min. degree k • Randomly displace a node to get Nnew • If Nnew also has min. degree k, N=Nnew • If Average_degree < k+2, stop, else goto 2 http://robotics.usc.edu/~gaurav

19. Results Rr < 2Rs http://robotics.usc.edu/~gaurav

20. Results Rr = 2Rs Rr=8, Rs=4, n=49 http://robotics.usc.edu/~gaurav

21. Results Rr > 2Rs http://robotics.usc.edu/~gaurav

22. Potential Field-based Deployment • Distributed algorithm • No map of the environment • Need range and bearing to neighbors – impractical on small sensor-constrained nodes • Experimental results: • Reasonably good coverage • Connectivity > 97% • #nodes with degree at least k > 95% • Converges quickly http://robotics.usc.edu/~gaurav

23. Today • Network deployment of robotic nodes • Sensor coverage and line-of-sight connectivity • Sensor coverage and k-neighbor connectivity • Network deployment of static nodes using mobile robots • Sensor coverage and fast exploration, no environment map • Network repair using mobile robots • Speed, no map of the environment • Multi-robot task allocation using a network • Navigation: No central planning, no constraints on adding robots dynamically • Adaptive sampling http://robotics.usc.edu/~gaurav

24. Robot-based Deployment • Its hard to make small mobile network nodes • Not clear if its energetically feasible • Alternate regime: • Deployment: Single ‘capable’ robot drops off nodes at their places • Repair: Robot ‘plugs holes’ in the resulting network using the same algorithm http://robotics.usc.edu/~gaurav

25. What’s in it for the Robot(s) ? • An efficient deployment strategy (linear in the network size), is also an efficient exploration strategy for the robot • Once the network is emplaced • any robot can use it to navigate (path planning is done ‘in-network’) • in-network (de-centralized) task allocation can coordinate the actions of multiple robots http://robotics.usc.edu/~gaurav

26. Approach Robot Loop If no beacon within radio range deploy beacon Else move in direction suggested by nearest beacon Beacon Loop Emit least recently visited direction M. Batalin, G. S. Sukhatme, Coverage, Exploration and Deployment by a Mobile Robot and Communication Network, Telecommunications Systems, April 2004 M. Batalin, G. S. Sukhatme,Efficient Exploration Without LocalizationProceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA'03), Taipei, Taiwan, May 12 - 17, 2003. http://robotics.usc.edu/~gaurav

27. Network Deployment Robot deploys network http://robotics.usc.edu/~gaurav

28. Adapting to Environment Change Environment change Network extension http://robotics.usc.edu/~gaurav

29. Graph Cover Times • Cover time is a measure of exploration speed • Random walk is O(n2) • on a regular graph of n nodes • DFS is O(n) and requires • passive markers • a topological map • markers of 3 colors • Our algorithm is O(n ln n) and requires • infinite active markers, no map http://robotics.usc.edu/~gaurav

30. Robot Navigation using the Network Path to goal computed using dynamic programming Robot uses network to navigate http://robotics.usc.edu/~gaurav

31. Robot Navigation using a Sensor Network goal start goal start goal start • Mica2 mote-based sensor network • Mobile robot navigates based solely on network directives • Results include over 1 km robot traverses in experiments Sensor node robot http://robotics.usc.edu/~gaurav

32. Robot Navigation Using a Sensor Network http://robotics.usc.edu/~gaurav

33. Robot Navigation to Contours • Use sensor network to navigate robot towards a contour of interest • Variant on the previous approach Karthik Dantu and Gaurav S. Sukhatme, "Detecting Level Sets of Scalar Fields Using Actuated Sensor Networks," Submitted to IROS 2004 http://robotics.usc.edu/~gaurav

34. From the Air Peter I. Corke, Stefan E. Hrabar, Ron Peterson, Daniela Rus, Srikanth Saripalli, and Gaurav S. Sukhatme, "Autonomous Deployment and Repair of a Sensor Network using an Unmanned Aerial Vehicle," IEEE International Conference on Robotics and Automation, 2004. http://robotics.usc.edu/~gaurav

35. Today • Network deployment of robotic nodes • Sensor coverage and line-of-sight connectivity • Sensor coverage and k-neighbor connectivity • Network deployment of static nodes using mobile robots • Sensor coverage and fast exploration, no environment map • Network repair using mobile robots • Speed, no map of the environment • Multi-robot task allocation using a network • Navigation: No central planning, no constraints on adding robots dynamically • Adaptive sampling http://robotics.usc.edu/~gaurav

36. Multi-Robot Task Allocation • Problem: Events in the environment, robot needed in vicinity of each event to observe it • Given a pre-deployed sensor network, no environment map, no assumptions about a static environment • Solution: Augment the deployment/exploration algorithm based on event occurrence M. Batalin, G. S. Sukhatme, Sensor Network-based Multi-robot Task Allocation, Proceedings of the 2003 IEEE International Conference on Intelligent Robots and Systems (IROS '03), Las Vegas, Oct 27-31, 2003. http://robotics.usc.edu/~gaurav

37. Outline • Pre-computation: In the exploration phase compute P(s’|s,a) transition probability from node s to s’ for action a • Every event i in the environment is assumed to have a weight wi • Every node computes a suggested direction of travel for a robot in its vicinity http://robotics.usc.edu/~gaurav

38. In-network Computation • Events are flooded through the network • Each node receives an event weight wiand a hop count hi and computes the following utility(i)= wi/hi current alarm = argmax utility(i) V(s’) = C(s,a) + max Σ P(s’|s,a)V(s) Π(s) = argmax Σ P(s’|s,a) V(s) http://robotics.usc.edu/~gaurav

39. Results • Compare aggregate event on-time for ‘exploration/deployment-only’ mode vs. ‘task-allocation’ mode http://robotics.usc.edu/~gaurav

40. Task Allocation Application I • Marine microorganism monitoring • Adaptive sampling of the underwater environment based on physical and chemical measurements Bin Zhang, Gaurav S. Sukhatme, and Aristides A. Requicha, "Adaptive Sampling for Marine Microorganism Monitoring," IROS 2004. http://robotics.usc.edu/~gaurav

41. TA Application II: Networked Infomechanical Systems • Networked mobile nodes • Sensing • Sampling • Energy logistics • Communication • Infrastructure • Enables deterministic and precise motion • Enables proper elevation • Enables mass transport at low energy • System Ecology • Fixed nodes • Mobile nodes • Infrastructure http://robotics.usc.edu/~gaurav

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