State-of-the-art intelligent flight control systems in unmanned aerial vehicles. An application of reinforcement learning to aerobatic helicopter flight. Work fast with our official CLI. Posted on June 16, 2019 by Shiyu Chen in Paper Reading UAV Control Reinforcement Learning Motivation. Autopilot systems are typically composed of an "inner loop" providing stability and control, while an "outer loop" is responsible for mission-level objectives, e.g. However more sophisticated control is required to operate in unpredictable, and harsh environments. 2001. 11/13/2019 ∙ by Eivind Bøhn, et al. At a Despite the promises offered by reinforcement learning, there are several challenges in adopting reinforcement learn-ing for UAV control. This will create an environment named env which Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. Model parameters are stored on the overall control server, and drones provide real-time information back to the server while the server sends back the decision. 2018-09-12 1 System Introduction. flight control firmware Neuroflight. may need to change the location of the Gazebo setup.sh defined by the To use Dart with Gazebo, they must be installed from source. Autopilot systems are typically composed of an "inner loop" providing stability and control, while an "outer loop" is responsible for mission-level objectives, e.g. Surace, L., Patacchiola, M., Battini Sonmez, E., Spataro, W., & Cangelosi, A. In this paper, we present a novel developmental reinforcement learning-based controller for … Posted on May 25, 2020 by Shiyu Chen in UAV Control Reinforcement Learning Simulation is an invaluable tool for the robotics researcher. Multiple agents share the same parameters. provide four modules: A flight controller, a flight control tuner, environment In [27], using a model-based reinforcement learning policy to control a small quadcopter is explored. UAV-motion-control-reinforcement-learning, download the GitHub extension for Visual Studio, my_policy_net_pg.ckpt.data-00000-of-00001, uav-rl-policy-gradients-discrete-fly-quad.py. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing. 2 Our Intention. reset functions. Take special note that the test_step_sim.py parameters are using the containers Browse our catalogue of tasks and access state-of-the-art solutions. Thanks goes to these wonderful people (emoji key): Want to become a contributor?! has not been verified to work for Ubuntu. ... Our manuscript "Reinforcement Learning for UAV Attitude Control" as been accepted for publication. Paper Reading: Reinforcement Learning for UAV Attitude Control. model to the simulation. minimum the aircraft must subscribe to motor commands and publish IMU messages, Topic /aircraft/command/motor The ISAE-SUPAERO Reinforcement Learning Initiative (SuReLI) is a vibrant group of researchers thriving to design next generation AI. The simplest environment can be created with. Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of … 07/15/2020 ∙ by Aditya M. Deshpande, et al. Learn more. For example to run four jobs in parallel execute. Surveys of reinforcement learning and optimal control [14,15] have a good introduction to the basic concepts behind reinforcement learning used in robotics. Note, this script may take more than an hour to execute. The future work on the quasi-distributed control framework can be divided as follows: More sophisticated control is required to operate in unpredictable and harsh environments. This will install the Python dependencies and also build the Gazebo plugins and More recently, [28] showed a generalized policy that can be transferred to multiple quadcopters. a different location other than specific in install_dependencies.sh), you Posted on May 25, 2020 by Shiyu Chen in UAV Control Reinforcement Learning Simulation is an invaluable tool for the robotics researcher. unsupervised learning seems to be more promising to solve more complex control problems as they arise in robotics or UAV control. More than 50 million people use GitHub to discover, fork, and contribute to over 100 million projects. vehicle (UAV) is still an open problem. Each model.sdf must declare the libAircraftConfigPlugin.so plugin. Use Git or checkout with SVN using the web URL. To fly manually, you need remote control or RC. GitHub Projects. Yet previous work has focused primarily on using RL at the mission-level controller. ∙ University of Nevada, Reno ∙ 0 ∙ share . If everything is OK you should see the NF1 quadcopter model in Gazebo. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL) which has had success in other applications such as robotics. Reinforcement Learning Edit on GitHub We below describe how we can implement DQN in AirSim using an OpenAI gym wrapper around AirSim API, and using stable baselines implementations of standard RL algorithms. This a summary of our IJCAI 2018 paper in training a quadcopter to learn to track.. 1. to each .so file in the build directory. Dec 2018. Get the latest machine learning methods with code. November 2018 - Flight controller synthesized with GymFC achieves stable Google protobuf aircraft digital twin API for publishing control Reinforcement Learning. More recently, [28] showed a generalized policy that can be transferred to multiple quadcopters. Introduction The number of applications for unmanned aerial vehicles (UAVs) is widely increasing in the civil arena such as surveillance [1,2], delivery of goods … Flexible agent interface allowing controller development for any type of flight control systems. Paper Reading: Reinforcement Learning for UAV Attitude Control. These platforms, however, are naturally unstable systems for which many different control approaches have been proposed. Abstract Unmanned aerial vehicles (UAV) are commonly used for search and rescue missions in unknown environments, where an exact mathematical model of the environment may not be available. In this contribution we are applying reinforce-ment learning (see e.g. To increase flexibility and provide a universal tuning framework, the user must Dream to Control: Learning Behaviors by Latent Imagination. Deep Reinforcement Learning Applications to Multi-Drone Coordination ... Federated and Distributed Deep Learning for UAV Cooprative Communications; Medical A.I. Details of the project and its architecture are best described in Wil Koch's Note 2: A more detailed article on drone reinforcement learning can be found here. September 2018 - GymFC v0.1.0 is released. However more sophisticated control is required to operate in unpredictable, and harsh environments. Google Scholar Digital Library; J. Andrew Bagnell and Jeff G. Schneider. GymFC is flight control tuning framework with a focus in attitude control. Contribute to macamporem/UAV-motion-control-reinforcement-learning development by creating an account on GitHub. For Ubuntu, install Docker for Ubuntu. Deep Reinforcement Learning (DRL) for UAV Control in Gazebo Simulation Environment. If you deviate from this installation instructions (e.g., installing Gazebo in Use Git or checkout with SVN using the web URL. GymFC was first introduced in the manuscript "Reinforcement learning for UAV attitude control" in which a simulator was used to synthesize neuro-flight attitude controllers that exceeded the performance of a traditional PID controller. [HKL11]: Reinforcement Learning Algorithms for UAV Control The dynamic system of UAV has high nonlinearity and instability which makes generating control policy for this system a challenging issue. Title: Reinforcement Learning for UAV Attitude Control. 4.1.1 Deep reinforcement learning based intelligent reflecting surface for secure wireless communications. }, year={2019}, volume={3}, pages={22:1-22:21} } Collecting large amounts of data on real UAVs has logistical issues. August 2019 - GymFC synthesizes neuro-controller with. GymFC was first introduced in the manuscript "Reinforcement learning for UAV attitude control" in which a simulator was used to Digital twin independence - digital twin is developed external to GymFC for tuning flight control systems, not only for synthesizing neuro-flight Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization Eivind Bøhn 1, Erlend M. Coates 2;3, Signe Moe , Tor Arne Johansen Abstract—Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby download the GitHub extension for Visual Studio, Merge branch 'master' into all-contributors/add-varunag18, Updating contributors for all-contributors integration, Flight Controller Synthesis via Deep You will also have to manually install the Gazebo plugins by executing. know and we will add it below. UAV autonomous control on the operational level. By inheriting FlightControlEnv you now have access to the step_sim and Reinforcement learning for UAV attitude control - CORE Reader To enable the virtual environment, source env/bin/activate and to deactivate, deactivate. For why Gazebo must be used with Dart see this video. The 2018 International Conference on Unmanned Aircraft Systems (ICUAS). If nothing happens, download the GitHub extension for Visual Studio and try again. GitHub Profile; Supaero Reinforcement Learning Initiative. (Optional) It is suggested to set up a virtual environment to install GymFC into. By default it will run make with a single job. 1.6 Federated Learning 1.6.1 Why federated learning is right for you Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Previous work focused on the use of hand-crafted geometric features and sensor-data fusion for identifying a fiducial marker and guide the UAV toward it. ... PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control. GymFC. Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy optimization. gym-fixed-wing. modules for users to mix and match. Overview: Last week, I made a GitHub repository public that contains a stand-alone detailed python code implementing deep reinforcement learning on a drone in a … 3d reconstruction is performed using pictures taken by drones. can be done with GymFC. In Advances in Neural Information Processing Systems. Course project is an opportunity for you to apply what you have learned in class to a problem of your interest in reinforcement learning. Replace by the external ip of your system to allow gymfc to connect to your XQuartz server and to where you cloned the Solo repo. An example configuration may look like this, GymFC communicates with the aircraft through Google Protobuf messages. The Fixed-Wing aircraft environment is an OpenAI Gym wrapper for the PyFly flight simulator, adding several features on top of the base simulator such as target states and computation of performance metrics. In [27], using a model-based reinforcement learning policy to control a small quadcopter is explored. In this work, we present a high-fidelity model-based progressive reinforcement learning method for control system design for an agile maneuvering UAV. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. GitHub is where the world builds software. GymFC requires an aircraft model (digital twin) to run. examples/ directory. Reinforcement Learning Edit on GitHub We below describe how we can implement DQN in AirSim using an OpenAI gym wrapper around AirSim API, and using stable baselines implementations of standard RL algorithms. Learning Unmanned Aerial Vehicle Control for Autonomous Target Following Siyi Li1, Tianbo Liu2, Chi Zhang1, Dit-Yan Yeung1, Shaojie Shen2 1 Department of Computer Science and Engineering, HKUST 2 Department of Electronic and Computer Engineering, HKUST fsliay, czhangbr, dyyeungg@cse.ust.hk,ftliuam, eeshaojieg@ust.hk ... control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL) which has had success in other applications such as robotics. This will take a while as it compiles mesa drivers, gazebo and dart. April 2018 - Pre-print of our paper is published to. will be ignored by git. python3 -m venv env. Reinforcement learning for UAV attitude control - CORE Reader In allows developing and testing algorithms in a safe and inexpensive manner, without having to worry about the time-consuming and expensive process of dealing with real-world hardware. June 2019; DOI: 10.1109/ICUAS.2019.8798254. Posted on June 16, 2019 by Shiyu Chen in Paper Reading UAV Control Reinforcement Learning Motivation. PID gains using optimization strategies such as GAs and PSO. The constraint model predictive control through physical modeling was done in [ 18 ]. It has been tested on MacOS 10.14.3 and Ubuntu 18.04, however the Gazebo client Autopilot systems for UAVs are predominately implemented using Proportional, Integral Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. Work fast with our official CLI. Cyber Phys. Aircraft agnostic - support for any type of aircraft just configure number of If your build fails GymFC is the primary method for developing controllers to be used in the worlds this class e.g.. For simplicity the GymFC environment takes as input a single aircraft_config which is the file location of your aircraft model model.sdf. In this paper, by taking the energy constraint of UAV into consideration, we study the age-optimal data collection problem in UAV-assisted IoT networks based on deep reinforcement learning (DRL). Get the latest machine learning methods with code. This repository includes an experimental docker build in docker/demo that demos the usage of GymFC. Keywords: UAV; motion planning; deep reinforcement learning; multiple experience pools 1. GymFC was first introduced in the manuscript "Reinforcement learning for UAV attitude control" in which a simulator was used to synthesize neuro-flight attitude controllers that exceeded the performance of a traditional PID controller. Introduction. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL) which has had success in other applications such as robotics. ∙ 18 ∙ share . Bibliographic details on Reinforcement Learning for UAV Attitude Control. ∙ SINTEF ∙ 0 ∙ share . Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization. Debugging Attitude Estimation; Intercepting MavLink Messages; Rapid Descent on PX4 Drones; Building PX4; PX4/MavLink Logging; MavLink LogViewer; MavLinkCom; MavLink MoCap; ArduPilot. Reinforcement Learning for UAV Attitude Control @article{Koch2019ReinforcementLF, title={Reinforcement Learning for UAV Attitude Control}, author={William Koch and Renato Mancuso and R. West and Azer Bestavros}, journal={ACM Trans. A universal flight control tuning framework. In allows developing and testing algorithms in a safe and inexpensive manner, without having to worry about the time-consuming and expensive process of dealing with real-world hardware. Remote Control#. NOTE! build directory will contain the built binary plugins. GitHub is where people build software. Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. For Mac, install Docker for Mac and XQuartz on your system. You signed in with another tab or window. interface, and digital twin. (Note: for neuro-flight controllers typically the GymFC expects your model to have the following Gazebo style directory structure: where the plugin directory contains the source for your plugins and the Autonomous helicopter control using reinforcement learning policy search methods. edit/development mode. It is recommended to give Docker a large part of the host's resources. The OpenAI environment and digital twin models used in Wil Koch's thesis can be found in the If you plan to modify the GymFC code you will need to install in using an RL policy with a weak attitude controller, while in [26], attitude control is tested with different RL algorithms. We investigate three learning modes of the PDP: inverse reinforcement learning, system identification, and control/planning, respectively. If nothing happens, download GitHub Desktop and try again. Implemented in 2 code libraries. ... View on Github. 01/16/2018 ∙ by Huy X. Pham, et al. Unmanned Aerial Vehicles (UAVs), or drones, have recently been used in several civil application domains from organ delivery to remote locations to wireless network coverage. Learn more. variable SetupFile in gymfc/gymfc.ini. ∙ 70 ∙ share . Autopilot systems for UAVs are predominately implemented using Proportional, Integral Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. signals and subscribing to sensor data. 4.1.2 Intelligent reflecting surface assisted anti-jamming communications: A fast reinforcement learning approach. If you have created your own, please let us Deep reinforcement learning for UAV in Gazebo simulation environment. If you are using external plugins create soft links From the project root run, If you don't have one then you can use APIs to fly programmatically or use so-called Computer Vision mode to move around using keyboard.. RC Setup for Default Config#. For reinforcement learning tasks, which break naturally into sub-sequences, called episodes , the return is usually left non-discounted or with a … If you have sufficient memory increase the number of jobs to run in parallel. In this work, we study vision-based end-to-end reinforcement learning on vehicle control problems, such as lane following and collision avoidance. ArduPilot SITL Setup; AirSim & ArduPilot; Upgrading. To install GymFC and its dependencies on Ubuntu 18.04 execute. way-point navigation. Our work relies on a simulation-based training and testing environment for The authors in [12, 13] used backstepping control theory, neural network [14, 15], and reinforcement learning [16, 17] to design the attitude controller of an unmanned helicopter. are running a supported environment for GymFC. model for testing. No description, website, or topics provided. The challenge is that deep reinforce-ment learning algorithms are hungry for data. The use of unmanned aerial vehicles … Since the projects initial release it has matured to become a modular Building Gazebo from source is very resource intensive. (RL), which has had success in other applications, such as robotics. flight in. To fly manually, you need remote control or RC. However, more sophisticated control is required to operate in unpredictable and harsh environments. Browse our catalogue of tasks and access state-of-the-art solutions. framework GymFC is flight control tuning framework with a focus in attitude control. See . Remote Control#. Please use the following BibTex entries to cite our work. GymFC runs on Ubuntu 18.04 and uses Gazebo v10.1.0 with Dart v6.7.0 for the backend simulator. This environment allows for training of reinforcement learning controllers for attitude control of fixed-wing aircraft. You will see the following error message because you have not built the If nothing happens, download Xcode and try again. To test everything is installed correctly run. The goal is to provide a collection of open source If nothing happens, download GitHub Desktop and try again. runtime, add the build directory to the Gazebo plugin path so they can be found and loaded. The easiest way to install the dependencies is with the provided install_dependencies.sh script. For example this opens up the possibilities for tuning Deep Reinforcement Learning and Control Spring 2017, CMU 10703 Instructors: Katerina Fragkiadaki, Ruslan Satakhutdinov Lectures: MW, 3:00-4:20pm, 4401 Gates and Hillman Centers (GHC) Office Hours: Katerina: Thursday 1.30-2.30pm, 8015 GHC ; Russ: Friday 1.15-2.15pm, 8017 GHC }, year={2019}, volume={3}, pages={22:1-22:21} } [7]) where a simple reward function judges any generated control action. Implemented in 2 code libraries. In this contribution we are applying reinforce-ment learning (see e.g. Also the following error message is normal. 2017. 1.5 Reinforcement Learning. Distributed deep reinforcement learning for autonomous driving is a tutorial to estimate the steering angle from the front camera image using distributed deep reinforcement learning. If nothing happens, download the GitHub extension for Visual Studio and try again. Reinforcement Learning for UAV Attitude Control William Koch, Renato Mancuso, Richard West, Azer Bestavros Boston University Boston, MA 02215 fwfkoch, rmancuso, richwest, bestg@bu.edu Abstract—Autopilot systems are typically composed of an “inner loop” providing stability and control… Reinforcement Learning for UAV Attitude Control @article{Koch2019ReinforcementLF, title={Reinforcement Learning for UAV Attitude Control}, author={William Koch and Renato Mancuso and R. West and Azer Bestavros}, journal={ACM Trans. check dmesg but the most common reason will be out-of-memory failures. controllers but also tuning traditional controllers as well. *Co-first authors. your installed version. More sophisticated control is required to operate in unpredictable and harsh environments. messages. 1--8. If you want to create an OpenAI gym you also need to inherit 12/14/2020 ∙ by András Kalapos, et al. The NF1 racing thesis "Flight Controller Synthesis Via Deep Reinforcement Learning". Remote control or RC the journal ACM Transactions on Cyber-Physical systems are naturally unstable systems for which different. Controller Synthesis Via deep reinforcement learning approach: UAV ; motion planning deep. Configuration data offered by reinforcement learning for UAV control reinforcement learning and control. More sophisticated control is required to operate in unpredictable and harsh environments to,. Users to mix and match collection of open source modules for users to mix and.. Xquartz on your system for training of reinforcement learning for UAV attitude control on... High-Fidelity model-based progressive reinforcement learning for UAV Cooprative communications ; Medical A.I the client. Aditya M. Deshpande, et al gains using optimization strategies such as robotics done in [ 18 ] forward... Easiest way to install GymFC into a dummy plugin allowing us to arbitrary. 'S resources for further testing read examples/README.md ( SuReLI ) is utilized for UAV attitude control as! Python dependencies and also build the Gazebo plugins by executing may 25, 2020 by Chen. Thriving to design next generation AI Multi-Drone Coordination... Federated and Distributed deep learning for UAV attitude control platforms! Gains using optimization strategies such as robotics, Battini Sonmez, E., Spataro, W. &! ), which has had success in other applications, such as lane following and collision avoidance,... Have to manually install the Gazebo plugins by executing optimally acquire rewards hour to execute is for. Fork, and harsh environments surace, L., Patacchiola, M., Battini Sonmez E.., Battini Sonmez, E., Spataro, W., & Cangelosi, a thesis `` controller... Keywords: UAV ; motion planning ; deep reinforcement learning based intelligent reflecting surface for wireless! And try again Upgrading APIs ; Upgrading Settings ; Contributed Tutorials search.! A single job learning modes of the project and its architecture are best described Wil! Will add it below opens up the possibilities for tuning PID gains optimization! The image and test test_step_sim.py using the web URL learning of quadcopter control in unmanned aerial vehicles, which had... - support for any type of aircraft just configure number of jobs to run agile! Commands and publish IMU messages, Topic /aircraft/command/motor message type MotorCommand.proto success in other applications, such as GAs PSO. The following BibTex entries to cite our work relies on a simulation-based training testing! Gazebo plugin path so they can be found and loaded for the backend simulator to modify the GymFC you. Of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards flight control systems an. Sdf declares all the visualizations, geometries and plugins for the aircraft NF1 model! Provide a collection of open source modules for users to mix and match december 2018 Pre-print... Paper, we present a novel developmental reinforcement learning of quadcopter control [ 7 ] ) where simple. Inverse reinforcement learning for UAV attitude control an aircraft model ( digital twin independence - digital twin DQN is! E., Spataro, W., & Cangelosi, a 26 ], attitude.., this script may take more than 50 million people use GitHub to discover,,... Control policy of a quadcopter UAV with Thrust Vectoring Rotors focused on the of... At the mission-level controller logistical issues agent interface allowing controller development for any of... Marker and guide the UAV toward it different RL algorithms environments and learning to! Trials & A/B tests, and 11 with reinforcement learning for uav attitude control github aircraft through google messages. By Git will forward to XQuartz: example usage, run the image and test using. On your installed version on Cyber-Physical systems a supported environment for GymFC Pre-print of our paper is to... Tested on MacOS 10.14.3 and Ubuntu 18.04 and uses Gazebo v10.1.0 with Dart see this.! As it compiles mesa drivers, Gazebo and Dart fast reinforcement learning and optimal control [ ]... Large amounts of data on real UAVs has logistical issues vision-based end-to-end reinforcement learning see... Statisticsclose star 0 call_split 0 access_time 2020-10-29. more_vert dreamer emoji key ): Want to become contributor. Acquire rewards to fly manually, you need a model for reinforcement learning for uav attitude control github testing examples/README.md. Coordination... Federated and Distributed deep learning for UAV in Gazebo Simulation.... Posted on June 16, 2019 by Shiyu Chen in UAV control reinforcement learning '',... And contribute to over 100 million projects and Sreenatha G. reinforcement learning for uav attitude control github 20,... and G.! Aircraft agnostic - support for any type of flight control systems is an area. For Visual Studio, my_policy_net_pg.ckpt.data-00000-of-00001, uav-rl-policy-gradients-discrete-fly-quad.py 16, 2019 by Shiyu Chen in paper Reading: reinforcement learning UAV. Configure number of jobs to run 7 ] ) where a simple reward function any! Deep reinforcement learning ; multiple experience pools 1 learning approach architecture are best described in Wil Koch's thesis flight. And Sreenatha G. Anavatti M. Deshpande, et al your installed version they!, are naturally unstable systems for which many different control approaches have been proposed and to deactivate deactivate... Journal ACM Transactions on Cyber-Physical systems, which has had success in other applications, such as robotics environment for. State-Of-The-Art solutions contribution we are applying reinforce-ment learning ( see e.g plugins and messages for,... The dependencies is with the provided install_dependencies.sh script Initiative ( SuReLI ) is utilized for UAV control reinforcement policy... Named env which will be ignored by Git of control policy of a quadcopter with... Plugins and messages systems ( ICUAS ) the classical PID controller all the visualizations, and... Are running a supported environment for GymFC to design next generation AI taken by drones is the primary for... How to optimally acquire rewards attitude control source modules for users to and! Supported environment for GymFC optimal control [ 14,15 ] have a good introduction to the ACM! Synthesized with GymFC achieves stable flight in while in [ 27 ], using model-based... Backend simulator for data ensure you are using external plugins create soft links to each.so file the... Gazebo and Dart experience pools 1 acquire rewards let us know and will! Than 50 million people use GitHub to reinforcement learning for uav attitude control github, fork, and Atari game.. Communications ; Medical A.I next generation AI a contributor? learning attitude control of Fixed-Wing aircraft guide. As lane following and collision avoidance reset functions PyBullet Gym environments for and! Key ): Want to become a contributor? posted on June 16 2019! By inheriting FlightControlEnv you now have access to the step_sim and reset functions innovation has been tested on 10.14.3. Parallel execute attitude flight control tuning framework with a focus in attitude.!
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