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Safety Sensors

Safety Sensors. (IR and ultrasonic compression). Concerns about sensors. Price Range Coverage time Temperature sensitivity. Design Vs. Buy OTS. Design our own Advantage low cost Design to spec Disadvantage Time Not appealing Buy OTS sensors Advantages less time

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Safety Sensors

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  1. Safety Sensors (IR and ultrasonic compression)

  2. Concerns about sensors • Price • Range • Coverage • time • Temperature sensitivity

  3. Design Vs. Buy OTS • Design our own • Advantage • low cost • Design to spec • Disadvantage • Time • Not appealing • Buy OTS sensors • Advantages • less time • Easy to manufacture • appealing • Disadvantages • costly

  4. Additional Accessories -addition to safety -addition to wow factor

  5. Status lights + Auto lighting • Status lights • Auto lighting

  6. Status lights • Individual color LEDs indicate the operational status of the robot • Example of an LED driver (analog.com)

  7. Auto-Lighting • By using ambient light transducers, or photoresistor, lighting around the robot can be turned on/off autonomously Ambient light detector (microsemi.com) Picture of a photo-resistor and circuit Light detector with built-in signal processing circuit for light modulation system

  8. The Intelligent Algorithm Speaker: James Harris

  9. Overview • Introduction • Initial Thoughts • Pathfinding Process • Types • Optimization • Conclusion

  10. Initial Thoughts • Ant Foraging • Bacterial Foraging • Particle Swarm Optimization • Pathfinding Algorithm • Binary Tree

  11. Background

  12. Types • Dijkstra’s Algorithm • Best First-Search • A* Algorithm

  13. Dijkstra’s Algorithm

  14. PROS Finds a shortest path Guaranteed to work CONS Simplest case Inefficient in amount of work Dijkstra’s Algorithm

  15. Best First Search

  16. PROS Efficient amount of work Fast CONS Greedy: uses a heuristic to guide itself Best First Search

  17. A* Algorithm

  18. A* Algorithm • Most popular • Favors vertices that are close to the starting point (DA) and the goal (BFS) • Faster and more efficient

  19. Optimization Techniques • Different score function • Genetic Algorithms • Neural Networks • Learning

  20. Summary • Introduction • Pathfinding Background • Pathfinding Process • Types • Optimization • Conclusion

  21. Questions

  22. The Competition Speaker: James Harris

  23. Overview • Introduction • Segway RMP 400 • Raymond Model 102XM • Seekur • PowerBot • Conclusion

  24. Segway RMP 400: Specifications • Speed: 6.67 m/s • Weight: 109kg • Dimensions: 76x112x61 cm • Payload: 181kg

  25. Pros All-terrain Looks cool Autonomous Can attach multiple RPs together Stable Operation Can add sensing devices Cons No bumpers Cannot hold objects Segway RMP 400

  26. Raymond Model 102XM: Specifications • Payload: 2042kg • Incline: 60% grade • Speed: 1.7m/s • Weight: 272kg

  27. Pros Pavement Powerful Hand Controls Stable Programmable Cons User-dependent Huge Large turn radius Expensive Little wow factor Raymond Model 102XM:

  28. Seekur & PowerBot

  29. Competitive Specifications

  30. Competitive Specifications (cont.)

  31. Competitive Specifications (cont.)

  32. Competitive Specifications (cont.)

  33. Competitive Specifications (cont.)

  34. Competitive Specifications (cont.)

  35. Competitive Specifications (cont.)

  36. Competitive Specifications (cont.)

  37. Competitive Specifications (cont.)

  38. Competitive Specifications (cont.)

  39. Competitive Specifications (cont.)

  40. Competitive Specifications (cont.)

  41. Review • Segway RMP 400 • Raymond Model 102XM • Seekur • PowerBot

  42. Questions

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