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Safety Sensors (IR and ultrasonic compression). Concerns about sensors  Price  Range  Coverage  time  Temperature sensitivity.

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Presentation on theme: "Safety Sensors (IR and ultrasonic compression). Concerns about sensors  Price  Range  Coverage  time  Temperature sensitivity."— Presentation transcript:

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 Picture of a photo-resistor and circuit Light detector with built-in signal processing circuit for light modulation system Ambient light detector (microsemi.com)

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

15 Best First Search

16  PROS  Efficient amount of work  Fast  CONS  Greedy: uses a heuristic to guide itself

17 A* Algorithm

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

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

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

28 Seekur & PowerBot

29 Competitive Specifications

30 Competitive Specifications (cont.)

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41 Review  Segway RMP 400  Raymond Model 102XM  Seekur  PowerBot

42 Questions


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