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E190Q – Project Introduction Autonomous Robot Navigation

E190Q – Project Introduction Autonomous Robot Navigation. Team Member 1 Name Team Member 2 Name. Problem Definition Written definition Overview image Provide performance metrics Background Include 3+ references Be sure to provide full citation Use images from references

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E190Q – Project Introduction Autonomous Robot Navigation

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  1. E190Q – Project IntroductionAutonomous Robot Navigation Team Member 1 Name Team Member 2 Name

  2. Problem Definition Written definition Overview image Provide performance metrics Background Include 3+ references Be sure to provide full citation Use images from references Describe key findings of paper Preliminary Project Presentation

  3. Proposed Solution Block Diagram including sensors and actuators (inputs, outputs, closed loop ) Measurable Outcomes List potential plots or tables of performance metrics Milestones List major tasks with dates Identify team member responsible if applicable Preliminary Project Presentation

  4. Notes: 5 minute time limit for slides Both students must present Students will help with assessment Presentations on Monday, April 1, 2013 Preliminary Project Presentation

  5. To design a Multi AUV Task Planner that considers kinematic constraints Problem Definition

  6. To design a Multi AUV Task Planner that considers kinematic constraints Problem Definition

  7. Given N task point locations and M AUVs Determine The assignment of tasks to AUVs and AUV tours of assigned task points that minimizes the maximum path length all AUV tours. Problem Definition

  8. Performance Metrics Maximum AUV tour length Planning Time or run time complexity Problem Definition

  9. [1] R. Zlot, A. Stentz, M. B. Dias, and S. Thayer, Multi-robot exploration controlled by a market economy, in Proc. IEEE Conf. Robotics and Automation, vol.3, Washington, DC, pp. 3016-3023, 2002. Used an auction based method in which task points are auctioned off to robot with the highest bid (i.e. lowest additional path cost). Decentralized. Fast, O(MN), but Sub-optimal Background

  10. [2] L. E. Dubins, On curves of minimum length with a constraint on average curvature and with prescribed initial and terminal position and tangents, American J. Mathematics, vol. 79, no. 3, pp. 497-516, Jul. 1957. Demonstrated the shortest path between points when minimum turn radius is a constraint Shortest Path is a connected curve of minimum radius, straight line segment, and curve of minimum radius Background

  11. [3] Chow, Clark, Huissoon, Assigning Closely Spaced Targest to Multiple Autonomous Underwater Vehicles, Journal of Ocean Engineering, Vol. 41-2 2007. Algorithm considers vehicle dynamics and currents Demonstrated that using euclidean distance between task points is a poor metric for calculating tour path length when task points are tightly spaced Real Ocean Deployments Background

  12. [3] Chow, Clark, Huissoon, Assigning Closely Spaced Targest to Multiple Autonomous Underwater Vehicles, Journal of Ocean Engineering, Vol. 41-2 2007. Algorithm considers vehicle dynamics and currents Demonstrated that using euclidean distance between task points is a poor metric for calculating tour path length when task points are tightly spaced Real Ocean Deployments Background

  13. Proposed Solution N Task Point Locations Task Assignment Algorithm Task Sequence Algorithm AUV Path Construction Algorithm M Task Assignments M Task Sequences M AUV Paths M AUV Locations

  14. Task Assignment Algorithm Cluster N points into M groups K-means clustering algorithm Assign one AUV to each cluster using a greedy assignment algorithm Task Sequence Algorithm Find next closest point algorithm AUV Path Construction Algorithm Fit arc path segments between each task point of a sequence Proposed Solution

  15. Run time as a function of the number of robots Average AUV path length for various ratios of N/M Comparison of average AUV path length when using standard MTSP planner and MTSP planner that considers kinematic constraints Measurable Outcomes

  16. Milestones

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