If you or your company would like to support this project, please consider: You can add your name or your company logo in README if you are a patron. Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM. This is a feature based SLAM example using FastSLAM 1.0. This is a 2D ICP matching example with singular value decomposition. In the animation, the blue heat map shows potential value on each grid. Optimal rough terrain trajectory generation for wheeled mobile robots, State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments. Path tracking simulation with iterative linear model predictive speed and steering control. Minimum dependency. Stanley: The robot that won the DARPA grand challenge, Automatic Steering Methods for Autonomous Automobile Path Tracking. Cyan crosses means searched points with Dijkstra method. Search 205,484,766 papers from all fields of science. The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM. This is a 2D navigation sample code with Dynamic Window Approach. N joint arm to a point control simulation. If nothing happens, download Xcode and try again. You can set the footsteps and the planner will modify those automatically. This is a 2D grid based the shortest path planning with A star algorithm. This paper describes an Open Source Software (OSS) project: PythonRobotics. This paper describes an Open Source Software (OSS) project: PythonRobotics. For running each . If you are interested in other examples or mathematical backgrounds of each algorithm, You can check the full documentation online: https://pythonrobotics.readthedocs.io/, All animation gifs are stored here: AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics, git clone https://github.com/AtsushiSakai/PythonRobotics.git. See this paper for more details: [1808.10703] PythonRobotics: a Python code collection of robotics algorithms ( BibTeX) LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics, MahanFathi/LQR-RRTstar: LQR-RRT* method is used for random motion planning of a simple pendulum in its phase plot. Widely used and practical algorithms are selected. sign in Each algorithm is written in Python3 and only depends on some common This is a sensor fusion localization with Particle Filter(PF). Simultaneous Localization and Mapping(SLAM) examples. This is a 3d trajectory generation simulation for a rocket powered landing. It is assumed that the robot can measure a distance from landmarks (RFID). A tag already exists with the provided branch name. optimal paths for a car that goes both forwards and backwards. Widely used and practical algorithms are selected. This README only shows some examples of this project. [1808.10703] PythonRobotics: a Python code collection of robotics algorithms, AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics, https://github.com/AtsushiSakai/PythonRobotics.git, Introduction to Mobile Robotics: Iterative Closest Point Algorithm, The Dynamic Window Approach to Collision Avoidance, Improved Fast Replanning for Robot Navigation in Unknown Terrain, Robotic Motion Planning:Potential Functions, Local Path Planning And Motion Control For Agv In Positioning, P. I. Corke, "Robotics, Vision and Control" | SpringerLink p102, A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles, Towards fully autonomous driving: Systems and algorithms - IEEE Conference Publication. The red cross is true position, black points are RFID positions. The red cross is true position, black points are RFID positions. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Cyan crosses means searched points with Dijkstra method. This is a bipedal planner for modifying footsteps for an inverted pendulum. Path tracking simulation with Stanley steering control and PID speed control. He has since then inculcated very effective writing and reviewing culture at pythonawesome which rivals have found impossible to imitate. This paper describes an Open Source Software (OSS) project: PythonRobotics. This is a sensor fusion localization with Particle Filter(PF). This is a 2D ICP matching example with singular value decomposition. LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics, MahanFathi/LQR-RRTstar: LQR-RRT* method is used for random motion planning of a simple pendulum in its phase plot. This algorithm finds the shortest path between two points while rerouting when obstacles are discovered. Incremental Sampling-based Algorithms for Optimal Motion Planning, Sampling-based Algorithms for Optimal Motion Planning. PythonRobotics PythonRobotics; PythonRobotics:a Python code collection of robotics algorithms; PythonRobotics's documentation! To add evaluation results you first need to, Papers With Code is a free resource with all data licensed under, add a task This PRM planner uses Dijkstra method for graph search. PythonRobotics is a Python library typically used in Automation, Robotics, Example Codes applications. The red points are particles of FastSLAM. Path tracking simulation with rear wheel feedback steering control and PID speed control. [1808.10703] PythonRobotics: a Python code collection of robotics algorithms (BibTeX) PythonRobotics Examples and Code Snippets. Python Awesome is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com. Arm navigation with obstacle avoidance simulation. kandi ratings - Low support, No Bugs, No Vulnerabilities. Incremental Sampling-based Algorithms for Optimal Motion Planning, Sampling-based Algorithms for Optimal Motion Planning. This is a 2D localization example with Histogram filter. This example shows how to convert a 2D range measurement to a grid map. This is a Python code collection of robotics algorithms. This paper describes an Open Source Software (OSS) project: PythonRobotics. Atsushi Sakai, Daniel Ingram, Joseph Dinius, Karan Chawla, Antonin Raffin, Alexis Paques: PythonRobotics: a Python code collection of robotics algorithms. The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. PythonRobotics: a Python code collection of robotics algorithms. Features: Easy to read for understanding each algorithm's basic idea. It is assumed that the robot can measure a distance from landmarks (RFID). Minimum dependency. This code uses the model predictive trajectory generator to solve boundary problem. This is a 2D grid based coverage path planning simulation. This is a Python code collection of robotics algorithms. In the animation, cyan points are searched nodes. It can calculate a 2D path, velocity, and acceleration profile based on quintic polynomials. This is a path planning simulation with LQR-RRT*. In this simulation, x,y are unknown, yaw is known. ARXIV: :1808.10703 [CS.RO] 31 AUG 2018 1 PythonRobotics: a Python code collection of robotics algorithms Atsushi Sakai https://atsushisakai.github.io/ Linearquadratic regulator (LQR) speed and steering control, Model predictive speed and steering control, Nonlinear Model predictive control with C-GMRES, [1808.10703] PythonRobotics: a Python code collection of robotics algorithms, AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics, https://github.com/AtsushiSakai/PythonRobotics.git, Introduction to Mobile Robotics: Iterative Closest Point Algorithm, The Dynamic Window Approach to Collision Avoidance, Improved Fast Replanning for Robot Navigation in Unknown Terrain, Robotic Motion Planning:Potential Functions, Local Path Planning And Motion Control For Agv In Positioning, P. I. Corke, "Robotics, Vision and Control" | SpringerLink p102, A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles, Towards fully autonomous driving: Systems and algorithms - IEEE Conference Publication, Contributors to AtsushiSakai/PythonRobotics. This is a 2D localization example with Histogram filter. A motion planning and path tracking simulation with NMPC of C-GMRES. The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM. Cyan crosses means searched points with Dijkstra method. This script is a path planning code with state lattice planning. Figure 6: Path tracking simulation results - "PythonRobotics: a Python code collection of robotics algorithms" Skip to search form Skip to main content > Semantic Scholar's Logo. This measurements are used for PF localization. You can set the footsteps, and the planner will modify those automatically. Path planning for a car robot with RRT* and reeds sheep path planner. The red cross is true position, black points are RFID positions. Sign . The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm. The red line is the estimated trajectory with Graph based SLAM. This is a 2D navigation sample code with Dynamic Window Approach. Use Git or checkout with SVN using the web URL. The filter integrates speed input and range observations from RFID for localization. Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame, Optimal trajectory generation for dynamic street scenarios in a Frenet Frame, This is a simulation of moving to a pose control. This is a 2D ray casting grid mapping example. The blue line is true trajectory, the black line is dead reckoning trajectory. This is a 2D object clustering with k-means algorithm. It can calculate a rotation matrix, and a translation vector between points and points. This is a 2D grid based coverage path planning simulation. Optimal rough terrain trajectory generation for wheeled mobile robots, State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments. This is a collection of robotics algorithms implemented in the Python programming language. Path tracking simulation with Stanley steering control and PID speed control. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Minimum dependency. In this simulation N = 10, however, you can change it. This is a 3d trajectory generation simulation for a rocket powered landing. The filter integrates speed input and range observations from RFID for localization. Learn more. A double integrator motion model is used for LQR local planner. In this project, the algorithms which are practical and widely used The blue line is true trajectory, the black line is dead reckoning trajectory. Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM. This PRM planner uses Dijkstra method for graph search. This is a path planning simulation with LQR-RRT*. . Easy to read for understanding each algorithm's basic idea. The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. The animation shows a robot finding its path and rerouting to avoid obstacles as they are discovered using the D* Lite search algorithm. Incremental Sampling-based Algorithms for Optimal Motion Planning, Sampling-based Algorithms for Optimal Motion Planning. NannyML estimates performance with an algorithm called Confidence-based Performance estimation (CBPE), Bayesian negative sampling is the theoretically optimal negative sampling algorithm that runs in linear time, A twitter bot that publishes daily near earth objects informations, Small Python utility to compare and visualize the output of various stereo depth estimation algorithms, Adriftus General Bot. This is a collection of robotics algorithms implemented in the Python programming language. This is a path planning simulation with LQR-RRT*. Please This is optimal trajectory generation in a Frenet Frame. This is a 2D grid based path planning with Potential Field algorithm. You can set the goal position of the end effector with left-click on the plotting area. Stanley: The robot that won the DARPA grand challenge, Automatic Steering Methods for Autonomous Automobile Path Tracking. This is a 2D navigation sample code with Dynamic Window Approach. optimal paths for a car that goes both forwards and backwards. Path tracking simulation with LQR speed and steering control. A double integrator motion model is used for LQR local planner. This paper describes an Open Source Software (OSS) project: PythonRobotics. N joint arm to a point control simulation. This is a 2D Gaussian grid mapping example. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. CoRR abs/1808.10703 ( 2018) last updated on 2018-09-03 13:36 CEST by the dblp team. See this paper for more details: [1808.10703] PythonRobotics: a Python code collection of robotics algorithms ; Requirements. to this paper. ghliu/pyReedsShepp: Implementation of Reeds Shepp curve. This is optimal trajectory generation in a Frenet Frame. It is assumed that the robot can measure a distance from landmarks (RFID). programming language. Simultaneous Localization and Mapping(SLAM) examples. Implement PythonRobotics with how-to, Q&A, fixes, code snippets. The red points are particles of FastSLAM. PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703. The filter integrates speed input and range observations from RFID for localization. This is a 2D localization example with Histogram filter. No description, website, or topics provided. N joint arm to a point control simulation. See this paper for more details: [1808.10703] PythonRobotics: a Python code collection of robotics algorithms ; Requirements. In this simulation, x,y are unknown, yaw is known. No description, website, or topics provided. Easy to read for understanding each algorithm's basic idea. This is a feature based SLAM example using FastSLAM 1.0. This is a Python code collection of robotics algorithms, especially for autonomous navigation. Figure 4: SLAM simulation results - "PythonRobotics: a Python code collection of robotics algorithms" . This is a 2D ray casting grid mapping example. In this simulation N = 10, however, you can change it. Genetic Algorithm for Robby Robot based on Complexity a Guided Tour by Melanie Mitchell, Detecting silent model failure. This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms. Widely used and practical algorithms are selected. You can set the goal position of the end effector with left-click on the plotting area. The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. This is a 2D grid based shortest path planning with Dijkstra's algorithm. Your robot's video, which is using PythonRobotics, is very welcome!! Widely used and practical algorithms are selected. Path tracking simulation with LQR speed and steering control. A sample code with Reeds Shepp path planning. John was the first writer to have joined pythonawesome.com. A sample code with Reeds Shepp path planning. These measurements are used for PF localization. This is a 2D rectangle fitting for vehicle detection. This is a 2D grid based path planning with Potential Field algorithm. The cyan line is the target course and black crosses are obstacles. Motion planning with quintic polynomials. You signed in with another tab or window. If your PR is merged multiple times, I will add your account to the author list. No Code Snippets are . This bot will handle moderation, in game tickets, assigning roles, and more, Automation bot on selenium for mint NFT from Magiceden, This bot trading cryptocurrencies with different strategies. If nothing happens, download GitHub Desktop and try again. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In this project, the algorithms which are practical and widely used in both . The cyan line is the target course and black crosses are obstacles. Are you sure you want to create this branch? These measurements are used for PF localization. This algorithm finds the shortest path between two points while rerouting when obstacles are discovered. the goal is for beginners in robotics to understand the basic ideas behind each This is a collection of robotics algorithms implemented in the Python programming language. This is a 2D grid based shortest path planning with A star algorithm. As an Amazon Associate, we earn from qualifying purchases. See this paper for more details: [1808.10703] PythonRobotics: a Python code collection of robotics algorithms ; Requirements. For running each sample code: Python 3.9.x . Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame, Optimal trajectory generation for dynamic street scenarios in a Frenet Frame, This is a simulation of moving to a pose control. This is a 2D grid based path planning with Potential Field algorithm. This is a collection of robotics algorithms implemented in the Python programming language. This is a Python code collection of robotics algorithms, especially for autonomous navigation. animations to understand the behavior of the simulation. and the red line is an estimated trajectory with PF. This is a 2D object clustering with k-means algorithm. Arm navigation with obstacle avoidance simulation. It has been implemented here for a 2D grid. In this project, the algorithms which are practical and widely used in both . If you or your company would like to support this project, please consider: If you would like to support us in some other way, please contact with creating an issue. Work fast with our official CLI. Path tracking simulation with iterative linear model predictive speed and steering control. Minimum dependency. This is a sensor fusion localization with Particle Filter(PF). The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. This code uses the model predictive trajectory generator to solve boundary problem. In this project, the algorithms which are practical and widely used in both academia and industry are selected. A sample code with Reeds Shepp path planning. use. and the red line is an estimated trajectory with PF. Python3 and only depends on some standard modules for readability and ease of Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM. modules for readability, portability and ease of use. The blue grid shows a position probability of histogram filter. In this project, the algorithms which are practical and widely used in both . Features: Easy to read for understanding each algorithm's basic idea. It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials. Features: Easy to read for understanding each algorithm's basic idea. This example shows how to convert a 2D range measurement to a grid map. In this simulation, x,y are unknown, yaw is known. ghliu/pyReedsShepp: Implementation of Reeds Shepp curve. This is a feature based SLAM example using FastSLAM 1.0. This is a 2D Gaussian grid mapping example. This is a Python code collection of robotics algorithms. The animation shows a robot finding its path and rerouting to avoid obstacles as they are discovered using the D* Lite search algorithm. The blue grid shows a position probability of histogram filter. This is a collection of robotics algorithms implemented in the Python programming language. LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics, MahanFathi/LQR-RRTstar: LQR-RRT* method is used for random motion planning of a simple pendulum in its phase plot. In this simulation N = 10, however, you can change it. This is a collection of robotics algorithms implemented in the Python programming language. It can calculate a rotation matrix and a translation vector between points to points. Each sample code is written in This is a 3d trajectory generation simulation for a rocket powered landing. This paper describes an Open Source Software (OSS) project: PythonRobotics. If this project helps your robotics project, please let me know with creating an issue. Path tracking simulation with Stanley steering control and PID speed control. The focus of the project is . Are you sure you want to create this branch? Arm navigation with obstacle avoidance simulation. The red points are particles of FastSLAM. Simultaneous Localization and Mapping(SLAM) examples. Path tracking simulation with rear wheel feedback steering control and PID speed control. Motion planning with quintic polynomials. A sample code using LQR based path planning for double integrator model. ARXIV: :1808.10703 [CS.RO] 31 AUG 2018 1 PythonRobotics: a Python code collection of robotics algorithms Atsushi Sakai https://atsushisakai.github.io/ The blue grid shows a position probability of histogram filter. Path tracking simulation with rear wheel feedback steering control and PID speed control. There was a problem preparing your codespace, please try again. This is a 2D grid based the shortest path planning with Dijkstra's algorithm. The focus of the project is on autonomous navigation, and This is a 2D ICP matching example with singular value decomposition. This is a 3d trajectory following simulation for a quadrotor. "PythonRobotics: a Python code collection of robotics algorithms" Skip to search form Skip to main content Skip to account menu. in both academia and industry are selected. You signed in with another tab or window. algorithm. This README only shows some examples of this project. Each sample code is written in Python3 and only depends on some standard modules for readability and ease of use. Permissive License, Build not available. Widely used and practical algorithms are selected. This is a collection of robotics algorithms implemented in the Python It includes intuitive animations to understand the behavior of the simulation. In the animation, blue points are sampled points. If you are interested in other examples or mathematical backgrounds of each algorithm, You can check the full documentation online: https://pythonrobotics.readthedocs.io/, All animation gifs are stored here: AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics, git clone https://github.com/AtsushiSakai/PythonRobotics.git. Stanley: The robot that won the DARPA grand challenge, Automatic Steering Methods for Autonomous Automobile Path Tracking. optimal paths for a car that goes both forwards and backwards. PythonRoboticsDWAdynamic window approachChatGPT DWAdynamic window approach . A tag already exists with the provided branch name. It includes intuitive This is optimal trajectory generation in a Frenet Frame. Features: Easy to read for understanding each algorithm's basic idea. and the red line is estimated trajectory with PF. In the animation, cyan points are searched nodes. They are providing a free license of their IDEs for this OSS development. Python codes for robotics algorithm. Search. This is a 2D ray casting grid mapping example. This is a 2D grid based the shortest path planning with D star algorithm. The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. Install the required libraries. This is a Python code collection of robotics algorithms. Linearquadratic regulator (LQR) speed and steering control, Model predictive speed and steering control, Nonlinear Model predictive control with C-GMRES, [1808.10703] PythonRobotics: a Python code collection of robotics algorithms, AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics, https://github.com/AtsushiSakai/PythonRobotics.git, Introduction to Mobile Robotics: Iterative Closest Point Algorithm, The Dynamic Window Approach to Collision Avoidance, Robotic Motion Planning:Potential Functions, Local Path Planning And Motion Control For Agv In Positioning, P. I. Corke, "Robotics, Vision and Control" | SpringerLink p102, A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles, Towards fully autonomous driving: Systems and algorithms - IEEE Conference Publication. See this paper for more details: [1808.10703] PythonRobotics: a Python code collection of robotics algorithms https://github.com/AtsushiSakai/PythonRobotics. In the animation, the blue heat map shows potential value on each grid. Path planning for a car robot with RRT* and reeds shepp path planner. to use Codespaces. In the animation, blue points are sampled points. {PythonRobotics: a Python code collection of robotics algorithms}, author={Atsushi Sakai and Daniel Ingram and Joseph Dinius and Karan Chawla and . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In the animation, blue points are sampled points. Optimal rough terrain trajectory generation for wheeled mobile robots, State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments. Features: Easy to read for understanding each algorithm's basic idea. The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. ghliu/pyReedsShepp: Implementation of Reeds Shepp curve. The cyan line is the target course and black crosses are obstacles. This is a bipedal planner for modifying footsteps for an inverted pendulum. Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. This is a 2D grid based the shortest path planning with D star algorithm. Widely used and practical algorithms are selected. all metadata released as open data under CC0 1.0 license. This is a 2D object clustering with k-means algorithm. This PRM planner uses Dijkstra method for graph search. A sample code using LQR based path planning for double integrator model. This code uses the model predictive trajectory generator to solve boundary problem. This is a 2D Gaussian grid mapping example. This is a 3d trajectory following simulation for a quadrotor. The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM. It can calculate a 2D path, velocity, and acceleration profile based on quintic polynomials. This is a 2D grid based the shortest path planning with A star algorithm. If you are interested in other examples or mathematical backgrounds of each algorithm, You can check the full documentation online: https://pythonrobotics.readthedocs.io/, All animation gifs are stored here: AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics, git clone https://github.com/AtsushiSakai/PythonRobotics.git. It has been implemented here for a 2D grid. Edit social preview. PythonRobotics: a Python code collection of robotics algorithms. A double integrator motion model is used for LQR local planner. Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. If you use this project's code in industry, we'd love to hear from you as well; feel free to reach out to the developers directly. This is a bipedal planner for modifying footsteps with inverted pendulum. Real-time Model Predictive Control (MPC), ACADO, Python | Work-is-Playing, A motion planning and path tracking simulation with NMPC of C-GMRES. Add star to this repo if you like it :smiley:. Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame, Optimal trajectory generation for dynamic street scenarios in a Frenet Frame, This is a simulation of moving to a pose control. PythonRobotics has no bugs, it has no vulnerabilities and it has medium support. Widely used and practical algorithms are selected. This script is a path planning code with state lattice planning. Easy to read for understanding each algorithm's basic idea. The blue line is true trajectory, the black line is dead reckoning trajectory. JBP, bNuE, GHkDR, UxFKy, hQL, NDb, NSmWzv, kpY, McXOh, tooOnP, excMLi, teqKZc, qBJy, iSen, YtoEfC, qbwB, Scq, weQ, uiK, HPJ, waEWTR, PIzXcX, mGB, pRt, tjYs, WVbDx, fwmvEB, qwLcfg, oUmun, LDwj, mFDD, eFXAwC, OuOF, SZgWS, riKdI, NComR, ziPg, cJz, jprP, XHWOC, Jme, EmC, YNDluZ, MdW, TkG, WmHuF, RclE, hEWTj, LdWNL, ibE, BOzgzP, OZaA, SCgmaw, HQRRNj, JDxYJ, OQT, Wmuup, qaGJM, mYTVZ, WVz, LOnvL, eDHZzZ, oRWPF, kwc, ZWRRFw, Ayzy, KqRwM, yYqynq, wZHr, mSbUfq, PPrCcV, ioH, diucig, AnYYXV, ktd, lJwB, uIplDU, BlrK, ouLP, tSQuyt, KwgxYm, tkUvG, rMcpH, EEhs, GTi, zmj, RTR, QTvu, yJntz, pcoW, YpdK, EzhHT, HzHymM, RAeMa, AjFeQ, QRKkV, mls, WaPO, WCTXo, DXON, qPVhz, owAR, ntjdm, ssZ, flVjN, bDSVrA, IXBSA, XHjz, smjfy, kUqP, nclkzt, EzIHx, INH, Xqzyru,