Sign up or log in to customize your list. Here's how it works: Anybody can ask a question The best answers are voted up and rise to the top I am having some issues with the ARDrone Parrot 2.0 and hope someone else may be running into the same thing. While hovering, the drone is (seemingly) randomly losing altitude then recovering . It is doing so while not being commanded any velocity inputs and should hold altitude. We are using the drivers from the ardrone_autonomy (dev_unstable branch) on github. We are able to watch the PWM outputs being sent to the motor and they are dropping from the hover command do a small value before exponentially returning to the hover value when this drop occurs. The issue could be a communication between the IMU and the onboard controller or on our software control implementation. Has anyone seen a similar problem or suggestions to test/troubleshoot what is happening? I have not used the ARDrone but have experience with height hold on another autopilot.

Without the further information, a quick google search found a possible firmware issue with ARDrone in this thread. If you are using the onboard ultrasound sensor then as I mentioned in my post on How can I detect the edge of a table?. The ultrasound sensors can jump to zero sporadically for part of a second and this could cause the ARDrone to change altitude and then jump back to the real value. Sign up or log in Sign up using Google Sign up using Email and Password Post as a guest By posting your answer, you agree to the privacy policy and terms of service. Not the answer you're looking for? Browse other questions tagged ros quadcopter pwm or ask your own question.Why we are interested in AR-Parrot Drone 2 and quadrotor use for our lab? The MURO lab uses the AR-Parrot Drone 2, a quadcopter with strong ROS communication capabilities and multiple cameras. We are using AR-Parrot Drone 2's so that we can provide ourselves with a flexible testbed for

implementing various multi-agent robotic algorithms, such as swarming, formation control, cyclic pursuit, and so on. gives us more relaxed dynamic constraints because they are omnidirectional, meaning that we can now work along the z-axis allowing us to extend experiments for our 3D multi-agent algorithms. The MURO lab's research is mostly on multi-agent robotics with either unicycle or omni-directional dynamics so quadrotors is a perfect fit for our lab. The MURO lab utilizes ROS (Robot Operating System) which is a software library which allows for strong communication capabilities accross a wifi network using a hybrid systems that run linux, android, windows or Max OS. The AR-Parrot Drone 2 is an even stronger fit for our lab because of the extensive ROS package "Ardrone Autonomy" developed by AutonomyLab, making us capable of controlling quadcopters with the same algorithms we use for the turtlebots, or any other system in the future.

The quadcopters also come equipped with both a front and bottom facing camera. We currently utilize the front facing camera to run ORB-SLAM (Oriented FAST andparrot ar drone en chile Rotated BRIEF - Simultaneous Localization and Mapping), a monocular SLAM algorithmparrot ar drone bee which provides a local point cloud map of the quadcopters surroundings and onlineparrot ar drone 2ehands estimations of the quadcopters location with respect to the map. parrot ar drone velocity developed modifications to ORB-SLAM that allows us to run multiple Quadcopterseb games parrot ar drone

capable exploring and localizing in the same map. The AR-Parrot Drone 2's video stream and ROS capabilities make it possible for the quadcopters toparrot ar drone d&r build and share a map, where they can execute location based multi-agent robot algorithms unrestricted to lab the lab environment. The AR-Parrot Drone 2's are capable of being used outside of the lab due to their ability to localize themselves, meaning we don't need extra equipment such as external cameras for localization. In the future we are interested in using the AR-Parrot Drone 2's outside or in large indoor areas with walls and obsticles which will add complexity to ourIt’s now our tradition to issue virtual machines with ROS pre-installed. We have already done it for ROS Fuerte and for ROS Groovy. Today it is ROS Hydro’s turn. We have chosen to stick with Ubunutu 12.04. This is because 12.04 is an LTS version, which means Long-Term Support.

The Ubuntu community will be maintaining it for 5 years. The ROS Hydro virtual machine comes in a single file .ova of approx. 3.7GB. You can launch it using VirtualBox, the very same way as for previous ROS virtualizations. The .ova file complies with the Open Virtualization Format (OVF), thus it can be used with other virtualization tools. We set Unbuntu to automatically log in the unique admin user: The login is a reference to the V.I.K.I character from the I, Robot movie. It stands for Virtual Interactive Kinetic Intelligence, which is the AI that controls the building of the international robot company: United States Robots (U.S.R). To remain compatible with our tutorial on ROS networking, we have named of the machine C3PO after the humanoid robot from Star Wars (BTW Episode VII is expected for December 18, 2015).An autonomous flight library for the ARDrone, built on top of Instead of directly controlling the drone speed, you can use Autonomy to plan and execute missions by describing the path, altitude and

orientation the drone must follow. If you are a #nodecopter enthusiast, then this library will enable you to focus on higher level use cases and experiments. You focus on where you want to go, the library takes your drone there. This work is based on the Visual Navigation for Flying Robots course. WARNING: This is early work. Autonomous means that this library will move your drone automaticaly to reach a given target. There isn't much security in place yet, so if you do something wrong, you may have your drone fly away :-)Experiment with this library in a closed/controlled environment before going in the wild !! Extended Kalman Filter leveraging the onboard tag detection as the observation source for an Extended Kalman Filter. This provides much more stable and usable state estimate. Camera projection and back-projection to estimate the position of an object detected by the camera. Currently used to estimate a tag position in the drone coordinate system based on its detection

by the bottom camera. PID Controler to autonomously control the drone position. Mission planner to prepare a flight/task plan and then execute it. VSLAM to improve the drone localization estimates. Object tracking to detect and track objects in the video stream. This module exposes a high level API to plan and execute missions, by focusing on where the drone should go instead of its low-level movements. Here is a simple example, with the drone taking off, travelling alongs a 2 x 2 meters square ane then landing. Log the mission data, csv formatted, in the given file. Makes it really usefull to debug/plot the state and controller behavior.The callback has the form function(err,result) and will be triggered in case of error or at the end of the mission. Add a takeoff step to the mission. Add a movement step to the mission. The drone will move in the given direction by the distance (in meters) before proceeding to next step.

The drone will also attempt to maintain all other degrees of freedom. Add a altitude step to the mission. Will climb to the given height before proceeding to next step. Add a rotation step to the mission. Will turn by the given angle (in Deg) before proceeding to the next step. Add a hover step to the mission. Will hover in place for the given delay (in ms) before proceeding to next step. Add a wait step to the mission. Will wait for the given delay (in ms) before proceeding to next step.Will go the given position before proceeding to next step. The position is a Controller goal such as {x: 0, y: 0, z: 1, yaw: 90}. Add a task step to the mission. Will execute the provided function before proceeding to the next step. A callback argument is passed to the function, it should be called when theWill execute the provided function before proceeding to the next step. Add a zeroing step to the mission. This will set the current position/orientation as the base state of the kalman filter (i.e. {x: 0, y:0, yaw:0}).

If you are not using a tag as your base position, it is a good idea to zero() after takeoff. This module exposes a high level API to control the position. It is built using an Extended Kalman Filter to estimate the position and a PID controller to move the drone to a given target. The easiest way to try the Controller is to play with the repl provided in the examples: Copyright (c) 2013 by Laurent Eschenauer laurent@eschenauer.be Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in