@Bella_Rose wrote:

After following the instructions for setting up SITL from this page in the documentation:
http://ardupilot.org/dev/docs/sitl-native-on-windows.html

I have hit a roadblock at:

image.png742×403 39.6 KB

Whenever I try to run sim_vehicle.py in really any folder, I’ve tried quite a few… I always get some bash error where it doesn’t recognize that command.

This is the error:

I cannot find videos with a real explanation for setting this stuff up or other websites so I am stuck.

Any help you could provide would be awesome. Thank you.

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@richkuav wrote:

I’m trying to set up auto tune on my pixhawk and taranis x9d radio I have a switch set up for it ,but when I flip the switch it says flight mode failed to load, I can set it to rtl and it enters that mode fine. The only thing I can thik of is I’m doing this on a bench to test to see if it’s going to work when I’m in the air. Should this work on a bench or do I have to be in the air to enter this flight mode

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@Jab_Froze wrote:

Hi Friends,

After successful installation of apm_planner_2.0.26_osx.dmg, when I start app planner, I get the following error on my MacBook Pro.

“APM Planner 2.0 cannot be opened because of a problem”

The problem details:

QUOTE START:

Process: apmplanner2 [1445]
Path: /Applications/APM Planner 2.0.app/Contents/MacOS/apmplanner2
Identifier: org.ardupilot.apmplanner2
Version: ???
Code Type: X86-64 (Native)
Parent Process: ??? [1]
Responsible: apmplanner2 [1445]
User ID: 502

Date/Time: 2019-05-26 05:46:18.013 +0530
OS Version: Mac OS X 10.14.2 (18C54)
Report Version: 12
Anonymous UUID: F84173D7-A9CD-1388-3853-C2E5818F50DB

Time Awake Since Boot: 24000 seconds

System Integrity Protection: enabled

Crashed Thread: 0

Exception Type: EXC_CRASH (SIGABRT)
Exception Codes: 0x0000000000000000, 0x0000000000000000
Exception Note: EXC_CORPSE_NOTIFY

Termination Reason: DYLD, [0x3] Wrong version

Application Specific Information:
dyld: launch, loading dependent libraries

Dyld Error Message:
Library not loaded: @rpath/QtOpenGL.framework/Versions/5/QtOpenGL
Referenced from: /Applications/APM Planner 2.0.app/Contents/MacOS/apmplanner2
Reason: Incompatible library version: apmplanner2 requires version 5.9.0 or later, but QtOpenGL provides version 5.6.0

Binary Images:
0x103ced000 - 0x1048b0fff +org.ardupilot.apmplanner2 (???) <471C336F-6BAF-32AE-B650-248C86B2BF28> /Applications/APM Planner 2.0.app/Contents/MacOS/apmplanner2
0x104ce1000 - 0x104da8fff +SDL2 (3.1) <78D9C537-E977-3AD1-9890-53FBC62A4D69> /Applications/APM Planner 2.0.app/Contents/Frameworks/SDL2.framework/Versions/A/SDL2
0x10548f000 - 0x10550da67 dyld (640.2) <289AB27E-F09F-3384-A14A-100431139559> /usr/lib/dyld
0x7fff331a0000 - 0x7fff331a0fff com.apple.ApplicationServices (50.1 - 50.1) <50496459-FFDD-3A78-8D3D-A252BD9B9DA2> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/ApplicationServices
0x7fff61e33000 - 0x7fff61e45ffb libz.1.dylib (70.200.4) <15F7B40A-424C-33BB-BF2C-7E8195128B78> /usr/lib/libz.1.dylib

Model: MacBookPro11,4, BootROM 187.0.0.0.0, 4 processors, Intel Core i7, 2.2 GHz, 16 GB, SMC 2.29f24
Graphics: Intel Iris Pro, Intel Iris Pro, Built-In
Memory Module: BANK 0/DIMM0, 8 GB, DDR3, 1600 MHz, 0x80AD, 0x484D54343147533642465238412D50422020
Memory Module: BANK 1/DIMM0, 8 GB, DDR3, 1600 MHz, 0x80AD, 0x484D54343147533642465238412D50422020
AirPort: spairport_wireless_card_type_airport_extreme (0x14E4, 0x152), Broadcom BCM43xx 1.0 (7.77.61.1 AirPortDriverBrcmNIC-1305.2)
Bluetooth: Version 6.0.9f2, 3 services, 27 devices, 1 incoming serial ports
Network Service: Wi-Fi, AirPort, en0
Serial ATA Device: APPLE SSD SM0256G, 251 GB
USB Device: USB 3.0 Bus
USB Device: Apple Internal Keyboard / Trackpad
USB Device: Bluetooth USB Host Controller
Thunderbolt Bus: MacBook Pro, Apple Inc., 27.1

QUOTE END.

My system configuration is:

MacOS Mojave
Version 10.14.2

Please help me.

Thanks and Regards,
Jabelu Firoz

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@verAPPelt wrote:

Dear community,
my friend told me that Pixracer would have 6 outputs for motors but we only use 4 motors.
So my question is: (How) Is it possible to manage one of the 2 last outputs manually in the code of a flight mode? We want to communicate with an Arduino by this.

Thanks!

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@verAPPelt wrote:

Hy there!
How is it possible with ArduPilot (I use Pixracer) to change the current flight mode in the code (after determined conditions e.g. altitude, acceleration etc.)?
Thank you!!

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@verAPPelt wrote:

Hi!
I would like to get the current altitude out of the barometer’s data in the flight mode code.
But when I’m building with ./waf copter there appears the following error: invalid use of non-static data member ‘Copter::barometer’ barometer.init();
compilation terminated due to -Wfatal-errors.

What can I do? Or is there another way to get the altitude?
Thank you very much!

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@fingadar wrote:

Hi!

Is there any possibility to automatically revert the yaw orientation to the takeoff orientation during land, independently from WP_YAW_BEHAVIOR?

The background is, I would like to have my copter landing in the original takeoff orientation using IR-LOCK precision landing. When I set WP_YAW_BEHAVIOR=0, the copter does not change its yaw orientation any longer during landing.

Thanks,
Stefan

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@Daniel_Riley wrote:

Hi All,
I’m building a solar powered rover and I’m wanting it to drive circles around my yard all day without any input. My plan is to use an Arduino to measure the voltage from the solar panel, then turn on power to the autopilot once it gets bright enough. Is there any way to get Ardurover to automatically switch into auto mode on boot up, with no external interaction?

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@twistedwings wrote:

Sometimes I get “flight mode change failed” when trying to switch to a flight mode that requires gps (loiter, drift, …).
I cannot determine the reason from the log. Can anybody help me?

ArduCopter 3.6.8

log file

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@LuckyBird wrote:

In this blog, I will attempt to apply the awesome works that have been done by @anbello, @ppoirier, @chobitsfan with a new fiducial marker system: using AprilTags to facilitate indoor non-GPS vision-based positioning.

For those interested, here are the references to related articles:

• Indoor autonomous flight with Arducopter, ROS and Aruco Boards - wiki page and blog post.
• Indoor Autonomous Flight with Cube, ROS and Aruco Board.
• Vision Positioning Experiments using SKYVIPER.
• DetectionExperiment with Visual Odometry - ROVIO part 1 and part 2
• Indoor flight with external navigation data.

1. Before we start

In order to make it work, you will need to know about ROS, Linux, Python, how to configure wifi network, ssh, etc. A plethora of problems can arise with these things, so if you are not familiar with these topics, head over to the dev wiki page as well as tons of other resources on google to get started.

One thing I have learned during the process of working on this lab is that details matter. Depends on your particular hardware, firmware and software setup, you will need to modify the commands to run, packages to install, and parameters to configure. Even the order in which you run commands can give you different results. In case the steps in this blog do not work for you, take a look at the abovementioned articles and the discussion in the blogs, which can give you some ideas on what to try.

To keep the blog brief, below I will lay out the main steps and refer to the relevant wiki pages where possible.

2. System setup

My hardware setup includes:

• A small quadcopter equipped with Pixhack Autopilot, CUAV PX4FLOW, TeraRanger One. With this setup, you can follow the wiki to achieve non-GPS flight with PX4FLOW.
• Raspberry Pi 3 Model B with a 16GB micro SD card running Ubuntu Mate 16.04, connected to a USB camera down-facing to the ground, with the x-axis of the camera to the right.
• Note: There is no restriction on the mounting orientation of the camera beside it being down-facing, but we need to know the camera’s x-axis to modify the parameters later on.

Software setup on RPi:

• install ROS
• install MAVROS
• If the connection between RPi - ArduPilot is established via the UART serial port: change the setting in /boot/config.txt
• connect RPi to ArduPilot with MAVLink
• connect RPi to ArduPilot with MAVROS
• synchronize clock/time.
• Install ROS driver for the camera: I use usb_cam package (sudo apt-get install ros-kinetic-usb-cam). If you use RPi camera module, follow this instruction to enable the camera, then install raspicam_node.
• Install AprilTag library and AprilTag ROS wrapper. I have put together the steps needed here.
• Clone my repo vision_to_mavros, which will listen to the output of AprilTag (tags’ pose in the camera frame), perform necessary transformations, then publish the body pose to a topic that can be remapped to /mavros/vision_pose/pose.

Prepare the tags board:

• Here is the pdf file of the A3 paper that I used. Depends on your printer setting, the actual size and location of the tags might be slightly different.
• You can design your own tags, print out, measure the true dimension and modify the layout file accordingly.

Camera calibration:

• Follow the ROS wiki to obtain the calibration file for your camera.
• Afterward, replace the camera_info_url param in the launch file with the correct path to the calibration file.

Configure parameters on Mission Planner:

• Params:

AHRS_EKF_TYPE = 2
BRD_RTC_TYPES = 2
EK2_ENABLE = 1
EK3_ENABLE = 0
EK2_GPS_TYPE = 3
EK2_POSNE_M_NSE = 0.1
EK2_VELD_M_NSE = 0.1
EK2_VELNE_M_NSE = 0.1
GPS_TYPE = 0
COMPASS_USE = 0
COMPASS_USE2 = 0
COMPASS_USE3 = 0
SERIAL5_BAUD = 921 (the serial port used to connect to Raspberry Pi)
SERIAL5_PROTOCOL = 1
SYSID_MYGCS = 1 (to accept control from mavros)
VISO_TYPE = 0

• For large frame, the offset between the position of the camera and vehicle’s center-of-gravity can be taken into account through VISO_POS params. I have not tried these params myself but you can read more about this in the wiki.

3. Ground test

First, let’s test each ROS nodes separately and fix any problems that might arise:

• Camera node:
  • On RPi: roslaunch usb_cam usb_cam-test.launch
  • If RPi is not connected to a display, view the raw image on PC with Linux Ubuntu: export ROS_MATER_URI=http://<rpi-ip>:11311 && rqt_image_view
  • Verify that there are images from the camera.
• MAVROS node:
  • On RPi: roslaunch mavros apm.launch fcu_url:=<tty-port>:<baud-rate>
  • On another terminal: `rostopic echo /mavros/
  • Verify that MAVROS is running OK. For example, /mavros/status should show that FCU is “CONNECTED”.
• AprilTags node and vision_to_mavros node:
  • Make sure the camera_info_url points to the correct path to your camera’s calibration file.
  • On RPi: roslaunch vision_to_mavros apriltags_to_mavros.launch
  • Open up RViz, view topics /tf and /mavros/vision_pose/pose to the display. With /tf, you should see the camera pose in the tags’ frame, with z-axis pointing downwards. If your camera’s x-axis is pointing to the right, then /mavros/vision_pose/pose will be aligned with body frame. If the camera’s x-axis is pointing in a different direction, you need to modify the params of vision_to_mavros accordingly.

If each node can run without any problem, you can perform ground test:

• On RPi: roslaunch vision_to_mavros apriltags_to_mavros.launch
• View the /mavros/vision_pose/pose on RViz. Move the vehicle around and see if the pose changes according to the movement, as demonstrated in the video by @anbello :

• Additionally, you can verify vision_pose data on MP or QGC Analyse Widget with the topic VISION_POSITION_ESTIMATE.x and y. You should see the data coming in.
• Before you can use the system, you need to send SET_GPS_GLOBAL_ORIGIN and SET_HOME_POSITION messages. There are a few ways to do this:

1. On Mission Planner, right click on a point in the map and click Set EKF Origin Here. If data from /mavros/vision_pose/pose is available, the vehicle icon will appear on the map.

HTd4lF1IPr92d28TND-ch-XBo_xUxC4ykkXvnyy54K0jGwy2Ibb6LZpXdsYDJRcpyBQYNH8pmVOzO3YiRvOKPIBTBLx-Bd0yK3IQNwP6f1LEJRHR6HUD33-M_8yRK9MR4cc_37pi1346×779

2. Send a SET_GPS_GLOBAL_ORIGIN message, to set the origin for the EKF. You will have to hard-code the lat/long coordinates you wish to send. @anbello has a script that does exactly this. The commands to run this script can be found here.

• After the origin of the EKF is set, hold the vehicle up, move around and observe the trajectory of the vehicle on MP.

If the last step is successful, you can go ahead with flight test.

4. Flight test

For external navigation data to be accepted by EKF, the data rate needs to be higher than a certain threshold (the number I saw from another discussion is minimum 10Hz, but I have not found the portion of the code to verify this). If you are also using RPi, firstly we need to tune the params related to computational costs to achieve higher detection rate. In my experiments, the RPi can run AprilTags algorithm at 11Hz when the camera is stable but when there are movement, the rate reduced to only 2-3Hz, which is not sufficient for hovering. The related tuning parameters for AprilTags algo are located in settings.yaml. Specifically:

• Increase tag_decimate will increase dectection rate, at the expense of lower accuracy, and vice versa.
• Increase tag_threads if you are not running anything else and have some CPU to spare.

Now for the flight tests, similar to what @ppoirier did in his blog on Visual Odometry - ROVIO:

• Takeoff in Stabilize to check whether the quadcopter is stable.
• At a height that the camera can have a good view of the tags, switch to Alt-Hold to adjust level position. Observe the feedback on RViz as well as Mission Planner to see if tags are detected.
• Take a look at MP Map, confirm that AprilTags is still tracking.
• Switch to Loiter, but always ready to switch back to Alt-Hold if anything goes awry.
• Otherwise, the quadcopter should hover stably above the tags.

Conclusion

We have replicated the works of @anbello, @ppoirier with a new fiducial tags system, AprilTags. Due to its limited computational power, the RPi is borderline usable for this type of experiments. Even after a lot of parameters are tuned to facilitate stable hovering, there are just too many limitations in terms of detection range and flight movement. If your RPi cannot handle 10Hz or you need to run something else at the same time, or in general the performance of your experiments are not up to your expectation, I would suggest the following solutions:

• Stream all images data to ground PC, perform all vision-related tasks on ground PC, then send commands back to ArduPilot. Note that the network bandwidth can be the next bottleneck in this case.
• Upgrade from the RPi to a better single-board computer. Up board, NVIDIA Jetson Nano, Google Coral etc. so many new choices coming out. Here is a nice comparison if you are ready to move on from the Raspberry Pi.
• Use a VIO sensor that incorporates both hardware and software, perform all algorithms and provide ready-to-use pose data. The new Intel Realsense T265 is a perfect candidate. And the procedure on how to use it with ArduPilot happens to be my 2019 ArduPilot GSoC proposal.

I hope this blog was helpful to anyone who is interested in this topic. As usual, feel free to comment and I am happy to help with what I know. Remember to check out the other blogs mentioned at the beginning for many, many more info that are not covered here.

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@KbOo_Ze wrote:

Hi,
I use Arducopter APM 2.8, I have no problem except that I can use only the firmware 3.2.1 on it (but it is normal). I bought a telemetry radio, I have fix green light on both, I can use APM Planner to connect on it with difficulties (I have to unplug every thing, put a delay of 5 seconds for the telemetry, and then plug the receiver, waiting, start arducopter, waiting 15 seconds… and then 3/4 of the time it works…) but I lost connection after less than 1 minutes… impossible to reconnect, I have to unplug it …
But when the arducopter doesn’t send data, the telemetry tranmiter and receiver can communicate (I can change the configuration).
I don’t plug any ESC on it, but I plus Receiver for RC, GPS, Compass, power with sensor, and telemetry.
When I unplug the telemetry and plug the usb it works well !
My analysis is the difficult, is it the arducopter APM 2.8 that crashed or the telemeter? how to know?

Thank you very much for your help

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@guglie wrote:

Thanks to the great work of @andyp1per (see Notch filter is not a notch filter?) I was able to log and do FFT analysis on post filtered gyro, and something seemed wrong.

Here’s one FFT post filter with GYRO_FILTER = 80hz (and notch at 144hz, badnwidth 14hz, attenuation 30db) and with “normal” flight speed:

normal_flight gyro80 notch144 bw14 att30.png1271×996 59.8 KB

Here’s another with GYRO_FILTER = 100hz (and notch at 147hz, badnwidth 60hz, attenuation 55db), on a fast flight (I zoomed to have same y axis):

fast_flight gyro100 notch147 bw60 att55.png1271×996 65.6 KB

Something seems strange, why I can still see frequencies higher than low pass filter cutoff?
There was a bug in @andyp1per implementation, when applying notch filter the lowpass was ignored, ok, tested again after a quick fix and… nothing changes. I can still see high frequencies in post filtered FFT.

That seemed very strange to me, so I studied the biquad filter implementation and found a couple of bugs in the parameter calculation and in the filter function.

Here’s a quick python sketch with the ardupilot filter algorithm vs standard implementation.
I know this test is on my rewriting of ardupilot C code in python, not a testing on the real C implementation, but I’m planning to run this on a log and I’m also writing a C unit test (but I need some help on how to build it).

Python test output:

pyhton LPF test.jpg2188×1788 554 KB

You can see in the top graph that the current implementation (green) is almost the same of the raw signal (yellow), while the standard LPF (red) removes the high frequencies.

It is very surprising to see copter flying well even without a LPF filter on gyro and accelerometer, and that’s why I saw a dramatic change in flight performance when I set up the notch filter.

Here’s the issue on github:

Here’s the pull with the fix:

Tomorrow I’ll test the fix on a real drone and post here the FFT results!

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@ghawkins wrote:

This isn’t directly Ardupilot related but I thought people here would have good experience and ideas related to interfaces for controlling pretty much anything that moves Currently, I’m controlling a 2-wheel robot with a super simple web-based interface that I can access via my phone - I just move my finger around on a gray square to control the two separate motors. However, the experience isn’t great.

0J7702.1200.2.png465×784 9.9 KB

So if my finger is in the upper left then the left motor is full forward etc. The code behind this is very simple but it’s actually not a great way to control the robot. So I’m looking for alternative ideas and suggestions?

The main issue is that it tends to spin around wildly - like a cheap remote control toy. You’re going full left one second and then you’re spinning full right when all you really wanted to do was correct the left spin and move forward.

A fix for this I guess would be to introduce some kind of expo element to moving left and right, i.e. movements near the center wouldn’t result in such dramatic changes as currently.

But maybe there are altogether better alternative designs for controlling the robot? Maybe a good source of ideas would be the controls used in racing games on phones.

So if you’ve got any cool ideas or simple tweaks that would improve the driving experience I’d really appreciate your input.

Thanks By the way, this is the kind of robot I’m controlling:

0J7729.1200.jpg1200×978 785 KB

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@Spitfire wrote:

Trying to compile ArduCopter for a HkPilot32 flight controller but unsure of the following board types to use ?

CUAVv5 CubeBlack CubePurple CubeYellow DrotekP3Pro F35Lightning F4BY KakuteF4 KakuteF7 MatekF405 MatekF405-Wing OMNIBUSF7V2 OmnibusNanoV6 PH4-mini Pixhawk1 Pixhawk4 Pixracer aero aerofc-v1 airbotf4 bbbmini bebop bhat blue crazyflie2 cube-red dark disco edge erleboard erlebrain2 f405-min f427-min f767-min fmuv2 fmuv3 fmuv4 fmuv4-beta fmuv5 linux mRoX21 mRoX21-777 mindpx-v2 mini-pix navio navio2 ocpoc_zynq omnibusf4pro omnibusf4v6 pocket px4-v1 px4-v2 px4-v3 px4-v4 px4-v4pro pxf pxfmini revo-mini rst_zynq sitl skyviper-f412 skyviper-f412-rev1 skyviper-v2450 skyviper-v2450-px4 sparky2 speedybeef4 zynq

Since mission planner detects the board type is there a way that I can tell from it what board type to use ?

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@rajat2004 wrote:

Introduction

AirSim​ is an open-source, cross-platform simulator for drones and cars, built on Unreal 3D Engine.
It provides​ physically and visually realistic simulations​ with external flight controllers such as PX4 or it’s own built-in controller simple_flight using either Software-In-The-Loop (SITL) or Hardware-In-The-Loop (HITL).​ It is generally used for testing software based on Computer Vision, Deep Learning, etc. & for generating large amounts of visual data which is essential for tasks such as Reinforcement Learning & Deep Learning.

During this summer as my GSoC 2019 project, I will endeavour to add active support for AirSim for ArduPilot SITL, specifically Copter platform. AirSim as a more advanced & realistic simulator (more resource hogging as well) can provide a basis for working on VIO algorithms & Obstacle Avoidance systems in Ardupilot within a simulation environment. I feel that this project will increase the adoption and usage of Ardupilot in cutting-edge research areas such as Computer Vision, Deep Learning & Reinforcement Learning for autonomous drones and vehicles, which are becoming increasingly popular these days.

The following is a list of features which I would like to add or support during this project (probably will be added in the same order chronologically) -

1. Support Normal SITL interface

This will involve initially adding support for the older Solo interface (see this for the Airsim side) in the Ardupilot SITL. Then modifying the AirSim code as well the previous SITL code to add lock-step scheduling for proper timing. Furthermore, the entire sensor packet layout will be changed to a JSON or XML-based one which would be easily extendable to add new fields, sensors, etc.

2. Manual Flying using RC

This would be smaller modification to add support for flying using a RC transmitter with a USB interface as seen in the video. Who wouldn’t like to fly in such beautiful & detailed environments available for Unreal Engine

3. Add support for Lidar Sensor

AirSim has Lidar sensor with a large number of configurable settings which can be seen here.
Possibly even test the Copter Object Avoidance

4. API Support, ROS Integration

AirSim has it’s own Python & C++ APIs for interfacing with the drone as well as Image API for accessing the visual data being generated. Furthermore, there are also ROS packges for the same (see here & here)
Adding support for these APIs & ROS integration will enable developers to write and test their algorithms in simulation before running them on hardware.

Documentation

The documents and reports that will be created along the way will be added to the wiki, specifically:

• Blog Posts on discuss.ardupilot.org for progress reports and to get feedback from the community
• New page in the Wiki for the AirSim setup and usage with Ardupilot similar to the existing ones for the other simulators
  Sample programs, guides for testing things like the Copter Avoidance will also be added

Addditional

There are a few other things which I would like to add but which probably won’t be able to get finished in the summer. But these will definitely be worked on after the period ends in that case

1. Multi-Vehicle Simulation

AirSim allows multi-vehicle simulation and has ROS tutorials for the same

2. ArduRover Support in ArSim

AirSim has Car Vehicle as well but doesn’t have any external controller support till now. Adding support for using AirSim as a simulator for Rover will be great addition!!

3. Deep Learning or Computer Vision using Ardupilot & AirSim

Trying out Deep Learning using Ardupilot & AirSim, maybe blog posts for something like Monocular Depth Estimation, Optical Flow for estimating velocity using a downward-facing camera, so many possibilities!

Progress

I have opened a PR for the initial interface, there’s also a Readme with links for setting up AirSim and using with Ardupilot if anyone’s interested. The status of the project will be updated frequently on this blog as well as on any upcoming ones

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@Eric_Maglio wrote:

Hi all,

I’d like to share a DIY module I’ve designed which allows an RFD900x to fit directly into transmitters with JR-style module bays. Direct PPM pass-through provides R/C control and a built-in WiFi module bridges MAVLINK data from your flight controller to a connected PC or smart device. It’s a nice clean long-range link option for Ardupilot-equipped vehicles.

Kits available here: https://www.loftedaero.com/product-page/rfd900x-diy-module-kit

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@rmackay9 wrote:

Copter-3.6.9 has been released as the official/stable version for Multicopters and Traditional Helicopters. The changes vs 3.6.8 are listed in the ReleaseNotes and also copied below.

Changes from 3.6.8

1. CX-OF flow sensor enabled on all boards
2. CUAVv5 Nano board support added
3. RangeFinders supported on all I2C ports
4. Compass maximum acceptable offsets increased
5. Septentrio GPS driver loses logging of camera feedback which could cause lag
6. Bug Fixes:
   a) Acro mode leveling fix for when ACRO_TRAINER was 1
   b) Tradheli collective position pre-arm check removed
   c) Fallback to microSD for storage fixed
   d) GPS blending memory access fix that could lead to unhealthy GPS
   e) fixed POWR flags for 2nd power module on fmuv3 boards
7. bootloader binary update for many boards

This is the same as 3.6.9-rc2 which has been available for beta testing in the past few weeks except for the last two items on the list (6e and 7) which we’ve decided are low enough risk that it’s OK for us to include them even though they weren’t in the last beta.

Thanks very much for those who contributed to the beta testing and please report any issues either in this thread or perhaps better yet in a new thread.

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@vahagnzargzarg wrote:

Hi guys,

my copter that worked and flied normally about 2 months, now just refuses to do any missions and is out of control.

At first everything was OK, I used the “QGroundControl” to setup a mission, and the copter flighted to the selected points very easly. But about two weeks ago when as usually I pressed “start the mission” on the QGroundControl (I use an android tablet), the drone took of but instead of flying to the next point, the flying mode was suddenly switched to “Land”. When I manually changed the flying mode to “Auto”, the copter just continued the mission, and started to fly to the next selected point. The next flights were like that, so I had to change the flying mode manually, to enable the copter do the mission. But yesterday even after when I changed the mode to “Auto”, the copter didn’t anyhow responded and started to fly far away, and landed about 200 metres far from us.

Hardware:
pixhawk 2.4.8
GPS G-model, gps/compass, UBLOX NEO M8N
RC- FrSky x9d, receiver-l9r
Motor- BE4715 330KV
ESC hobbywing X-ROTOR 80A/HV no bec
3 DR power module

I’ve tried to change the Pixhawk and also the GPS module, since I thought the problem was GPS data, and (EKF) failsafe mode, but nothing helped and nothing changed. Log is here.

DPe8U9yaG84.jpg1280×800 300 KB

Any help would be greatly appreciated.

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@111167 wrote:

Hi~
I have some problem.
Attempts to connect the external temperature sensor (bmp280) to the pixhawk2 using i2c method fail.

how to apply the sensor. please help me.!

pixhawk2 / bmp280
V/Vin
SCL/SCK
SDA/SDI
GND/GND

I have connected the wiring as above.

FW ver : ArduCopter V3.6.7 (38b8af17)

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@Corrado_Steri wrote:

Is there a parameter to tune to have terrain follow more relaxed?
Is there some kind of gain to tune to have the aircraft react to change in altitude slower so to almost ignore sudden changes in altitude due to some obstacles below.

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