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Hamish Willee
GitHub
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# Connecting an RC Receiver to a PX4 Linux-based Autopilot
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This topic shows how to setup a PX4 Linux-based autopilot to connect and use a [supported RC receiver](../getting_started/rc_transmitter_receiver.md) on any serial port.
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S.Bus 이외의 RC 유형의 경우 수신기는 직렬 포트에 직접 연결하거나 USB-TTY 직렬 케이블(예: PL2302 USB-직렬 TTL 변환기)을 통하여 USB에 연결할 수 있습니다.
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:::info
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For an S.Bus receiver (or encoder - e.g. from Futaba, RadioLink, etc.) you will usually need to connect the receiver and device via a [signal inverter circuit](#signal_inverter_circuit), but otherwise the setup is the same.
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:::
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Then [Start the PX4 RC Driver](#start_driver) on the device, as shown below.
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<a id="start_driver"></a>
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## 드라이버 시작
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To start the RC driver on a particular UART (e.g. in this case `/dev/ttyS2`):
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```sh
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rc_input start -d /dev/ttyS2
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```
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For other driver usage information see: [rc_input](../modules/modules_driver.md#rc-input).
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<a id="signal_inverter_circuit"></a>
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## 신호 반전 회로(S.Bus 전용)
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S.Bus is an _inverted_ UART communication signal.
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일부 직렬 포트/비행 컨트롤러는 반전된 UART 신호를 읽을 수 있지만, 대부분은 신호 반전을 복원하기 위하여 수신기와 직렬 포트 사이에 신호 인버터 회로가 필요합니다.
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:::tip
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This circuit is also required to read S.Bus remote control signals through the serial port or USB-to-TTY serial converter.
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:::
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이 섹션에서는 적절한 회로를 만드는 방법을 설명합니다.
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### 필수 부품
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- 1x NPN 트랜지스터 (예: NPN S9014 TO92)
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- 1x 10K 저항
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- 1x 1K 저항
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:::info
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Any type/model of transistor can be used because the current drain is very low.
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:::
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### 회로 구성도/연결
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아래에 설명(그리고 회로 구성도)하는 바와 같이 회로 소자를 연결하십시오:
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- S.Bus 신호선 → 1K 저항 → NPN 트랜지스터 베이스
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- NPN 트랜지스터 방출 → GND
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- 3.3VCC → 10K 저항 → NPN 트랜지스터 컬렉션 → USB-to-TTY rxd
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- 5.0VCC → S.Bus VCC
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- GND → S.Bus GND
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아래 이미지에서는 빵판에서 연결된 모습을 보여줍니다.
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# 움직임 감지(Motion Capture)기술을 활용한 비행 (VICON, Optitrack)
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:::warning
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**WORK IN PROGRESS**
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This topic shares significant overlap with [External Position Estimation (ROS)](../ros/external_position_estimation.md).
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:::
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Indoor motion capture systems like VICON, NOKOV and Optitrack can be used to provide position and attitude data for vehicle state estimation, orto serve as ground-truth for analysis.
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The motion capture data can be used to update PX4's local position estimate relative to the local origin. Heading (yaw) from the motion capture system can also be optionally integrated by the attitude estimator.
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Pose (position and orientation) data from the motion capture system is sent to the autopilot over MAVLink, using the [ATT_POS_MOCAP](https://mavlink.io/en/messages/common.html#ATT_POS_MOCAP) message. See the section below on coordinate frames for data representation conventions. The [mavros](../ros/mavros_installation.md) ROS-Mavlink interface has a default plugin to send this message. They can also be sent using pure C/C++ code and direct use of the MAVLink library.
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## Computing Architecture
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It is **highly recommended** that you send motion capture data via an **onboard** computer (e.g Raspberry Pi, ODroid, etc.) for reliable communications. The onboard computer can be connected to the motion capture computer through WiFi, which offers reliable, high-bandwidth connection.
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Most standard telemetry links like 3DR/SiK radios are **not** suitable for high-bandwidth motion capture applications.
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## Coordinate Frames
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This section shows how to setup the system with the proper reference frames. There are various representations but we will use two of them: ENU and NED.
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- ENU is a ground-fixed frame where **X** axis points East, **Y** points North and **Z** up. The robot/vehicle body frame is **X** towards the front, **Z** up and **Y** towards the left.
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- NED has **X** towards North, **Y** East and **Z** down. The robot/vehicle body frame has **X** towards the front, **Z** down and **Y** accordingly.
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Frames are shown in the image below. NED on the left, ENU on the right:
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With the external heading estimation, however, magnetic North is ignored and faked with a vector corresponding to world _x_ axis (which can be placed freely at mocap calibration); yaw angle will be given respect to local _x_.
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:::warning
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When creating the rigid body in the motion capture software, remember to first align the robot with the world **X** axis otherwise yaw estimation will have an initial offset.
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:::
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## Estimator Choice
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EKF2 is recommended for GPS-enabled systems (LPE is deprecated, and hence no longer supported or maintained).
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The Q-Estimator is recommended if you don't have GPS, as it works without a magnetometer or barometer.
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See [Switching State Estimators](../advanced/switching_state_estimators.md) for more information.
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### EKF2
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The ROS topic for motion cap `mocap_pose_estimate` for mocap systems and `vision_pose_estimate` for vision.
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Check [mavros_extras](http://wiki.ros.org/mavros_extras) for further info.
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## 시험
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## 문제 해결
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