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Sensor Network Assisted Collaboration for Pursuit-Evasion Problem

University of Missouri Columbia. Sensor Network Assisted Collaboration for Pursuit-Evasion Problem. Peng Zhuang, Yi Shang, & Hongchi Shi University of Missouri-Columbia Columbia, Missouri, USA. Pursuer 2. Evader 1. Evader 2. Pursuer 1. The Pursuit-Evasion Problem.

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Sensor Network Assisted Collaboration for Pursuit-Evasion Problem

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  1. University of Missouri Columbia Sensor Network Assisted Collaboration for Pursuit-Evasion Problem Peng Zhuang, Yi Shang, & Hongchi Shi University of Missouri-Columbia Columbia, Missouri, USA

  2. Pursuer 2 Evader 1 Evader 2 Pursuer 1 The Pursuit-Evasion Problem • Pursuers capture mobile evaders. • Examples: search and rescue operation, surveillance, tracking moving parts in a warehouse, search and capture mission in a battle field • The goal is to minimize the time to capture all the evaders. Pursuer 3

  3. Challenges • The pursuer performance (how soon to capture the evaders) depends on how much evader information the pursuers have (pursuers’“vision”). • Evader information: • location, speed, direction, moving pattern, predicted location • Sensor network is used to extend the vision of pursuers. • Sensing accuracy, sampling rate, packet delay • When we fix the “vision” of pursuers, the performance depends on the coordination and collaboration strategy the pursuers adopt.

  4. Sensors Pursuer Evader General Approach • The base-station • Predicts the evader future • location and moving pattern. • Assign pursuer(s) to capture one • or more evaders. Pursuers capture the evader within the squared capturing range. Sensor nodes use multi-hop communication to send the detection information to the base-station. Evaders are heterogeneous (with different moving patterns). *The field is discretized into a N x N grid field.

  5. Outline • Evader Moving Dynamics • Coordination between Sensor Nodes and Pursuers • Collaboration between Pursuers • Evader Moving Pattern Recognition • Accuracy on Evader Moving Pattern Recognition • Effect of Collaboration • Conclusion

  6. Evader Moving Dynamics • Randomly Moving Evader (RME) • At each time step, an RME has equal probability to move to either of its 8 neighboring or staying at the original cell. • Actively Escaping Evader (AEE) • An RME chooses the next move Cx based on the location Cj of the closest pursuer j

  7. Coordination between Sensor Nodes and Pursuers Sensor Node Sensor Node Pursuers Check if the info is new Assign pursuers to evaders Current global map Detections Assignments Pursuers Sensor Node Update Base-station Sensor Node

  8. Collaboration between Pursuers • When the pursuers outnumber the evaders, the extra pursuers perform as helpers to reduce the capturing time. • Experimental result shows that the collaboration is more effective in the case of an AEE. • The performance is also related to the angle between the evader and the pursuers.

  9. Evader Moving Pattern Recognition • The sensor network is used to recognize whether it is an RME or an AEE. • Basic idea: a knn nearest neighbor classification algorithm • Dimensions: the largest evader turning angles and the longest duration of an evader maintaining the same directions

  10. Accuracy on Evader Moving Pattern Recognition

  11. Effect of Collaboration

  12. Conclusion • A solution that adapts to different evader dynamics • The effectiveness of collaboration strategy under different conditions • An algorithm to detect the evader moving patterns • The performance better than an intuitive systematic search

  13. Thanks!Questions / Comments?

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