Algorithms for Robot-based Network Deployment, Repair, and ...

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Algorithms for Robot-based Network Deployment, Repair, and ...

  1. 1. Algorithms for Robot-based Network Deployment, Repair, and Coverage Gaurav S. Sukhatme Center for Robotics and Embedded Systems Center for Embedded Networked Sensing Computer Science Department University of Southern California [email_address] http://robotics.usc.edu/resl
  2. 2. Introduction <ul><li>Synoptic sensing: sense everywhere in parallel </li></ul><ul><li>Enablers: small computers, sensors, radios </li></ul><ul><li>Role of robotics: Deploy sensors, Localize sensors, Replenish and repair network </li></ul><ul><li>Potential Applications: </li></ul><ul><ul><li>Ecosystem bio-complexity monitoring </li></ul></ul><ul><ul><li>Marine microorganism monitoring </li></ul></ul><ul><ul><li>Structural health monitoring </li></ul></ul><ul><ul><li>… </li></ul></ul>
  3. 3. Network Deployment
  4. 4. Deployment Constraints and Tradeoffs <ul><li>Connectivity </li></ul><ul><ul><li>Final/Intermediate </li></ul></ul><ul><ul><li>K-connectedness, K-degree (density) </li></ul></ul><ul><li>Visibility </li></ul><ul><ul><li>Communication visibility, sensing visibility </li></ul></ul><ul><li>Efficiency </li></ul><ul><ul><li>How many nodes ? How quickly ? </li></ul></ul>
  5. 5. Network Repair
  6. 6. Repair Constraints <ul><li>Minimal Intervention </li></ul><ul><ul><li>Smallest number of nodes are subjected to small displacements </li></ul></ul><ul><ul><li>Small number of new nodes deployed </li></ul></ul><ul><li>Speed </li></ul><ul><ul><li>Faster than (re)deployment </li></ul></ul><ul><li>Preserve connectivity/visibility </li></ul>
  7. 7. Robot-based Network Deployment <ul><li>Case 1: All the network nodes are mobile robots </li></ul><ul><li>Case 2: Single ‘capable’ robot drops off nodes at their places </li></ul><ul><ul><li>Network nodes are stationary </li></ul></ul><ul><ul><li>Repair: Robot ‘plugs holes’ in the resulting network using the same algorithm </li></ul></ul>Sameera Poduri and Gaurav S. Sukhatme, &quot;Constrained Coverage for Mobile Sensor Networks,&quot; IEEE International Conference on Robotics and Automation , 2004 Maxim Batalin, Gaurav S. Sukhatme, and Myron Hattig, &quot;Mobile Robot Navigation using a Sensor Network,&quot; IEEE International Conference on Robotics and Automation , 2004 Maxim Batalin and Gaurav S. Sukhatme, &quot;Using a Sensor Network for Distributed Multi-Robot Task Allocation,&quot; IEEE International Conference on Robotics and Automation , 2004.
  8. 8. What’s in it for the Robot(s) ? <ul><li>An efficient deployment strategy (linear in the network size), is also an efficient exploration strategy for the robot </li></ul><ul><li>Once the network is emplaced </li></ul><ul><ul><li>any robot can use it to navigate (path planning is done ‘in-network’) </li></ul></ul><ul><ul><li>in-network (de-centralized) task allocation can coordinate the actions of multiple robots </li></ul></ul>
  9. 9. Approach <ul><li>Robot Loop </li></ul><ul><li>If no beacon within radio range </li></ul><ul><li>deploy beacon </li></ul><ul><li>Else </li></ul><ul><li>move in direction suggested by nearest beacon </li></ul><ul><li>Beacon Loop </li></ul><ul><li>Emit least recently visited direction </li></ul>M. Batalin, G. S. Sukhatme, Coverage, Exploration and Deployment by a Mobile Robot and Communication Network, Telecommunications Systems , April 2004 (accepted, to appear) M. Batalin, G. S. Sukhatme, Efficient Exploration Without Localization Proceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA'03) , Taipei, Taiwan, May 12 - 17, 2003.
  10. 10. Robot deploys network Network Deployment
  11. 11. Environment change Network extension Adapting to Environment Change
  12. 12. Graph Cover Times <ul><li>Cover time is a measure of exploration speed </li></ul><ul><li>Random walk is O( n 2 ) </li></ul><ul><ul><li>on a regular graph of n nodes </li></ul></ul><ul><li>DFS is O( n ) and requires </li></ul><ul><ul><li>passive markers </li></ul></ul><ul><ul><li>a topological map </li></ul></ul><ul><ul><li>markers of 3 colors </li></ul></ul><ul><li>Our algorithm is O( n ln n ) and requires </li></ul><ul><ul><li>infinite active markers, no map </li></ul></ul>
  13. 13. Path to goal computed using dynamic programming Robot uses network to navigate Robot Navigation using the Network
  14. 14. Robot Navigation using a Sensor Network <ul><li>Mica2 mote-based sensor network </li></ul><ul><li>Mobile robot navigates based solely on network directives </li></ul><ul><li>Results include over 1 km robot traverses in experiments </li></ul>robot Sensor node start goal start goal start goal
  15. 15. Robot Navigation Using a Sensor Network Video
  16. 16. Robot Navigation to Contours <ul><li>Use sensor network to navigate robot towards a contour of interest </li></ul><ul><li>Variant on the previous approach </li></ul>Karthik Dantu and Gaurav S. Sukhatme, &quot;Detecting Level Sets of Scalar Fields Using Actuated Sensor Networks,&quot; Submitted to IROS 2004
  17. 17. From the Air Peter I. Corke, Stefan E. Hrabar, Ron Peterson, Daniela Rus, Srikanth Saripalli, and Gaurav S. Sukhatme, &quot;Autonomous Deployment and Repair of a Sensor Network using an Unmanned Aerial Vehicle,&quot; IEEE International Conference on Robotics and Automation , 2004. (to appear) Video
  18. 18. Multi-Robot Task Allocation <ul><li>Problem: Events in the environment, robot needed in vicinity of each event to observe it </li></ul><ul><li>Given a pre-deployed sensor network, no environment map, no assumptions about a static environment </li></ul><ul><li>Solution: Augment the deployment/exploration algorithm based on event occurrence </li></ul>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.
  19. 19. Outline <ul><li>Pre-computation: In the exploration phase compute P(s’|s,a) transition probability from node s to s’ for action a </li></ul><ul><li>Every event i in the environment is assumed to have a weight w i </li></ul><ul><li>Every node computes a suggested direction of travel for a robot in its vicinity </li></ul>
  20. 20. In-network Computation <ul><li>Events are flooded through the network </li></ul><ul><li>Each node receives an event weight w i and a hop count h i and computes the following </li></ul><ul><ul><li>utility(i) = w i / h i </li></ul></ul><ul><ul><li>current alarm = argmax utility(i) </li></ul></ul><ul><ul><li>V(s’) = C(s,a) + max Σ P(s’|s,a) V(s) </li></ul></ul><ul><ul><li>Π (s) = argmax Σ P(s’|s,a) V(s) </li></ul></ul>
  21. 21. Results <ul><li>Compare aggregate event on-time for ‘exploration/deployment-only’ mode vs. ‘task-allocation’ mode </li></ul>
  22. 22. Conclusion <ul><li>Symbiotic relationship between mobile robots and sensor networks </li></ul><ul><ul><li>Actuation enables us to focus sensing where it is needed when it is needed </li></ul></ul><ul><ul><li>Networks extend the effective sensing range of robots and offload some processing </li></ul></ul>Sameera Poduri and Gaurav S. Sukhatme, &quot;Constrained Coverage for Mobile Sensor Networks,&quot; IEEE International Conference on Robotics and Automation , 2004 Maxim Batalin, Gaurav S. Sukhatme, and Myron Hattig, &quot;Mobile Robot Navigation using a Sensor Network,&quot; IEEE International Conference on Robotics and Automation , 2004 Maxim Batalin and Gaurav S. Sukhatme, &quot;Using a Sensor Network for Distributed Multi-Robot Task Allocation,&quot; IEEE International Conference on Robotics and Automation , 2004.

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