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* Simon Haller (simon.haller@uibk.ac.at)
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## 1. Description:
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In this project different systems for direct user-control for quadcopters are explored. Furthermore a control interface is developed which combines the display of the video-stream of the aircraft using a head-mounted display with gesture-based flight control using gesture capturing devices (e.g. LeapMotion, Kinect, Myo) and the use of tactile feedback to indicate obstacles in the surrounding of the quadcopter.
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In this project different systems for direct user-control for quadcopters are explored. Furthermore a control interface is developed which combines the display of the video-stream of the aircraft using a head-mounted display (HMD) with gesture-based flight control using gesture capturing devices (e.g. LeapMotion, Kinect, Myo) and the use of tactile feedback to indicate obstacles in the surrounding of the quadcopter.
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## 2. Task:
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### 2.1. Visual stream:
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* **Goal:**
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* **Goal:**
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Implementation of visual streaming so that the user can see what the quadcopter camera is filming. Moreover the user should be able to change the viewing direction by head movements. To realize this a normal camera can be used in combination with a gimbal which is controlled by the head movements. Or a omnidirectional camera is used and the the viewed part of the video changes following the users head movements.
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### 2.2. Gesture-based control:
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* **Goal:**
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Control the quadcopter with gestures so no normal remote control is needed. For that task at first several interfaces are tested.
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After the testing the best working devices are combined. For that an algorithm is developed which combines the output data of the interfaces, filter these data to remove outliers and therefore get a robust and stable gesture capturing.
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* **Possible Interfaces:**
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* MYO bracelet:
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Device from Thalmic labs witch can be used to capture hand and arm gestures using electromyographic sensors. Link: [https://www.thalmic.com/myo/](https://www.thalmic.com/myo/)
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* LEAP Motion controller:
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Device from Leapmotion can track hand and finger motion without touching it. The hands just have to be nearer than about 1 meter.
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Device from Leap Motion that can track hand and finger motion without touching it. The hands just have to be nearer than about 1 meter.
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Link: [https://www.leapmotion.com/product](https://www.leapmotion.com/product)
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* Kinect:
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Device from Microsoft. It is a depth camera which can be used for whole body gesture capturing.
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Link: [https://www.microsoft.com/en-us/kinectforwindows/](https://www.microsoft.com/en-us/kinectforwindows/)
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* Perception Neuron:
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Motion capturing system from Perception Neuron that makes it possible to track whole body gestures using inertial measurement units.
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Furthermore the Microsoft kinect Fig. 3 which is a depth camera where whole body gestures can be tracked, also can be used. The last interface presented here is the Perception Neuron which is a motion capturing system that makes it possible to track whole body gestures using inertial measurement units. Furthermore also the HMD maybe will be used to control the quadcopter.
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After the testing the best working interfaces will be used and combined. For that it will be necessary to develop an own algorithm which combines the output data of the interfaces, filter these data to remove outliers and therefore get a robust and stable gesture capturing.
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### 2.3. Tactile feedback:
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* **Goal:**
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Develop a system for tactile feedback for obstacle avoidance. This part is important, because when using the HMD there can be obstacles which are not visible in the viewing direction. With the tactile feedback it then is possible to get warnings of obstacles whether they are visible in the HMD or not. For that an own interface will be developed using vibration motors.
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The third task is to develop a system for tactile feedback for obstacle avoidance using the work from Lukas Haidacher. This part is important, because when using the HMD there can be obstacles which are not visible in the viewing direction. With the tactile feedback it then is possible to get warnings of obstacles whether they are visible in the HMD or not. For that an own interface will be developed using vibration motors. An example for such a system would be the tactile feedback belt from Edwards et al. 2009 in Fig. 4 where also vibration motors are used to give the feedback. Another possibility will be to use the Perception Neuron also for that.
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### 2.1. Problems:
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### 2.2. Goals:
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## 7. References: |
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## 7. References:
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TBA |
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