... | @@ -283,6 +283,40 @@ Likewise, any changes in the environment (track) will almost certainly result in |
... | @@ -283,6 +283,40 @@ Likewise, any changes in the environment (track) will almost certainly result in |
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Theoretically the fastest possible run for this rigid type of hard coded approach would be 27 seconds, but the robot itself is not able to accelerate accurately at that speed (x3), as one wheel often spins out. This is because of lack of grip of the cheap rubber tyres and the robot trying to accelerate instantly to the required speed.
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Theoretically the fastest possible run for this rigid type of hard coded approach would be 27 seconds, but the robot itself is not able to accelerate accurately at that speed (x3), as one wheel often spins out. This is because of lack of grip of the cheap rubber tyres and the robot trying to accelerate instantly to the required speed.
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##### Fig. 18 - video showing the “proper” run (featuring Rasmus as assistance)
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Pros
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Cons
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Quick to build
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Inflexible
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Quick to program
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Unreliable
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Predictable (to some point)
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Slow
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Fig. ?? - Pros and cons of hard coding a robot for racing
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During this last experiment, we learned that there are many shortcomings of hardcoding a robot to drive a specific track. Even though the perfect way through a track is known, there will always be uncertainties and small differences in the beginning of the run can cause big trouble in the end.
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## Conclusion
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* Through the four experiments we explored different approaches and methods of obtaining an efficient robot for the Alishan train track. Our findings showed:
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* Though speed is an essential component when competing on the Alishan race track, we conclude that gearing the motors is not ideal. Our experiment revealed frictional problems with the wheels lacking the ability to find grip at its maximum speed. To overcome this issue an acceleration method in the program may be feasible. Additionally the robot was very prone to crashing and going into a spin at high speed.
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* Using a PID controller makes the robot follow the black line precisely along the track.
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* Hardcoding the Alishan track corners into the robot’s program was not 100% accurate, as it was difficult to find the black line after a completed turn. For this we tried to methods: One where the robot reacted once several white readings had been conducted by the light sensor and one where the turn behavior was triggered by changes measured by the gyroscope. For further experiments other solutions may be preferable.
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* As the track features flat plateaus and steep paths, the light sensor will become inaccurate as the distance to the ground varies. To overcome this problem a flexible front mount proved to be efficient in keeping the readings consistent.
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* A GUI for the PID control is essential in order to spare the struggle of hard coding values into the program.
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* A behavior pattern is very efficient to manage and prioritise different behaviors.
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* Hard coding everything is a fast approach to complete the track. However this approach reveals some significant shortcomings. Hard coding makes the robot prone to the slightest affections, and once the robot goes out of its path, there is no way it will complete.
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* A mix of hard coded corners and a PID controller to stay on track may be the more preferable way to an efficient robot on the Alishan track.
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## References
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## References
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