Submitted version of the thesis - Airlab, the Artificial Intelligence ...

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46 Chapter 4. Control B.3. The most common used one is the mixed angular and linear movement. Almost every path was initially implemented with this movement approach. Others have been used where optimization is needed to cover problems due to the lack of encoders to control the position of the motors. 4.3 PWM Control Motors are controlled by applying a PWM signal to the H-Bridges. In order to control velocity we did not use any encoders, so we don’t have feedback aboutthemotor rotation, or thedisplacement achieved withtheresultofthe applied PWM. To have an odometry information without using encoders, we decided to review and replicate the work done at AIRLab about using the mice odometry to get feedback from the motors. On the previously work, a set of mice were reconfigured to obtain the position information, and calculations were made from these readings to find the odometry. The working configuration was able get a feedback when the distance between the mouse sensors and lens was about 1mm, and the distance between lens and ground was also 1 mm, but this distance is too small for our robot. In order to increase the distance to 3 cm which is the distance between the ground and the chassis of the robot, we experimented changing the lens of the mice. The mice work like a camera, and the image is captured by the sensor inside it through a lens placed in front of it. We tested lenses with different focal lengths and different orientations in order to achieve the 3 cm configuration. During the experiments, we obtained different performance on different grounds and different lighting conditions. We tried several illumination alternatives, such as laser, led, and table lamp. We were able to find the correct illumination using the led with a wave length readable from the sensor. And for the lens we used a configuration with a focal length of 10 mm, leaving the distance between the lens and the sensor at 15 mm and lens to ground at 30 mm. Instead of using encoders that each cost around 50 euro, the possible configuration costs around 5 euro, but the work is not finished yet and we decided to include this on the robot later. Since there is no information on the motor speed and displacement of the robot, the control process mainly depends on vision, and it is not very precise. There are situations where we cannot determine whether the motor is moving. When the robot is stuck in a place and the sensors do not detect
4.3. PWM Control 47 acollision, such as entrance of thewheels to a gap in thefloor, sincewe don’t have a feedback on the motors we cannot determine whether the motors are turning. The motors are controlled by PWM generated from the microcontroller. To drive motors we used a PWM signal and varied the duty cycle to act as a throttle: 100% duty cycle = full speed, 0% duty cycle = coast, 50% duty cycle = half speed, etc. After some testing we realized that the percentage of the duty cycle and the speed are not changing with the same ratio (e.g. 50% of the duty cycle does not correspond the half speed). Also there is a minimum percentage of the duty cycle which the wheels start turning, and a maximum percentage of the duty cycle at which the speed remains as full speed after that. The minimum PWM value to start the motion is % 45 of the duty cycle and the speed set to full speed after exceeding the % 90 of the duty cycle which is the maximum PWM that can be applied. Since there is a non-linear increase between the ratio of rotation of the motors and applied PWM rather than a linear one, the speed changes are not distinguishable. In other words, the range of PWM signal that enables movement is very narrow and the difference between the minimum speed and maximum speed is not very apparent. The script that is calculating the motor contributions according to the target position is not taking into account the motor’s maximum or minimum speed. The script can return a motor contribution with 4000 mm/s or 3 mm/s which is cannot be performed physically by the motors. After some experiments, to limit the script that calculates the motor contributions, we decided to bound the PWM signal value to be applied to the motors. The maximum PWM value defined for a motor is 2.5 meters/second and the minimum PWM value is around 300 millimeters/second. Using both the limits coming the physical characteristic of motor, and the PWM-rotation limitations, we implemented a map that transforms the motor contribution calculated by the script into a PWM signal. To do so, all the speeds between 2500 mm/s and 300 mm/s should be mapped to PWM signals between 45% and 90% of duty cycle. Mapping 300 mm/s to the 45% and 2500 mm/s to 90% did not resulted as expected since reflecting speed changes is not possible. In order to express most of the speeds, we decided to tune the PWM sent to motors. Main idea is to boundto speeds in a dynamically changeable manner. We introduced, a fuzzy-like behavior that is based on the robot’s
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POLITECNICO DI MILANO Corso di Laur
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To my family...
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Summary Aim of this project is the
Page 13 and 14: Contents Sommario I Summary III Tha
Page 15 and 16: List of Figures 2.1 The standard wh
Page 17 and 18: Chapter 1 Introduction 1.1 Goals Th
Page 19 and 20: 1.4. Thesis Structure 3 tailed desc
Page 21 and 22: Chapter 2 State of the Art Advances
Page 23 and 24: 2.1. Locomotion 7 provides low resi
Page 25 and 26: 2.1. Locomotion 9 Figure 2.4: Diffe
Page 27 and 28: 2.1. Locomotion 11 Omnidirectional
Page 29 and 30: 2.2. Motion Models 13 2.2 Motion Mo
Page 31 and 32: 2.4. Games and Interaction 15 a 2.5
Page 33 and 34: 2.4. Games and Interaction 17 We in
Page 35 and 36: 2.4. Games and Interaction 19 lot o
Page 37 and 38: Chapter 3 Mechanical Construction W
Page 39 and 40: 3.2. Motors 23 The initial design o
Page 41 and 42: 3.2. Motors 25 Figure 3.3: The calc
Page 43 and 44: 3.3. Wheels 27 target surface, maxi
Page 45 and 46: 3.5. Bumpers 29 Figure 3.8: Three w
Page 47 and 48: 3.6. Batteries 31 itself also works
Page 49 and 50: 3.7. Hardware Architecture 33 In sh
Page 51 and 52: 3.7. Hardware Architecture 35 The s
Page 53 and 54: 3.7. Hardware Architecture 37 With
Page 55 and 56: Chapter 4 Control The motion mechan
Page 57 and 58: 4.1. Wheel configuration 41 Figure
Page 59 and 60: 4.2. Matlab Script 43 This is case
Page 61: 4.2. Matlab Script 45 Mixed Angular
Page 65 and 66: 4.3. PWM Control 49 mentioned previ
Page 67 and 68: Chapter 5 Vision The vision system
Page 69 and 70: 5.1. Camera Calibration 53 the prin
Page 71 and 72: 5.1. Camera Calibration 55 Figure 5
Page 73 and 74: 5.1. Camera Calibration 57 Later, i
Page 75 and 76: 5.2. Color Definition 59 warmer (ye
Page 77 and 78: 5.2. Color Definition 61 Anotheraut
Page 79 and 80: 5.2. Color Definition 63 9000 8000
Page 81 and 82: 5.3. Tracking 65 any color values.
Page 83 and 84: Chapter 6 Game The robot will be us
Page 85 and 86: • Initialize sensors • Initiali
Page 87 and 88: If the camera is set to SERVO MID,
Page 89 and 90: Chapter 7 Conclusions and Future Wo
Page 91 and 92: ior, which will be useful in the re
Page 93 and 94: Bibliography [1] Battle bots-http:/
Page 95 and 96: BIBLIOGRAPHY 79 [25] Han et al. A n
Page 97 and 98: Appendix A Documentation of the pro
Page 99 and 100: Figure A.2: The flow diagram of the
Page 101 and 102: Appendix B Documentation of the pro
Page 103 and 104: B.1. Microprocessor Code 87 /* Conf
Page 105 and 106: B.1. Microprocessor Code 89 #ifdef
Page 107 and 108: B.1. Microprocessor Code 91 Set_Spe
Page 109 and 110: B.1. Microprocessor Code 93 TIM_DeI
Page 111 and 112: B.1. Microprocessor Code 95 /* Expo
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B.1. Microprocessor Code 97 RGBCube
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B.1. Microprocessor Code 99 /* Incl
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B.1. Microprocessor Code 101 #endif
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B.1. Microprocessor Code 103 } void
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B.1. Microprocessor Code 105 GPIO_I
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B.1. Microprocessor Code 107 /* Sta
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B.1. Microprocessor Code 109 } Inve
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B.1. Microprocessor Code 111 #inclu
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B.1. Microprocessor Code 113 /* * M
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B.1. Microprocessor Code 115 b[1] =
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B.1. Microprocessor Code 117 } * (B
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B.1. Microprocessor Code 119 #defin
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B.2. Color Histogram Calculator 121
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B.3. Object’s Position Calculator
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B.3. Object’s Position Calculator
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B.4. Motion Simulator 127 0 0 0 0 0
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B.4. Motion Simulator 129 if (plot
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B.4. Motion Simulator 131 fill (m a
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B.4. Motion Simulator 133 end text(
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Appendix C User Manual This documen
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C.1. Tool-chain Software 137 Figure
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C.2. Setting up the environment (Qt
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C.3. Main Software - Game software
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Appendix D Datasheet The pin mappin
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Figure D.2: The schematics for the
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