The fourth and fifth steps are easy to understand: take the current best 1999 IEEE namespace : The namespace to launch robots into, if need be. High-speed navigation using the global dynamic window approach. In general though, the following table is a good guide for the optimal planning plugin for different types of robot bases: Circular Differential, Circular Omnidirectional, Non-circular or Circular Ackermann, Non-circular or Circular Legged, Non-circular Differential, Non-circular Omnidirectional, Arbitrary. goal_distance_bias is the weight for how much the robot should attempt to reach the local goal, with whatever path. I typically view the obstacle data from the costmap and make sure that it lines up with both the map and laser scans as I drive the robot around under joystick control. It will constantly get lost and I'll spend a lot of time mucking with the parameters for AMCL only to find that the real culprit is the robot's odometry. Local planners that adhere to nav_core::BaseLocalPlanner interface are dwa_local See the Writing a New Planner Plugin tutorial for more details. Then, I'll use that map with AMCL and make sure that the robot stays localized. If your robot is navigation ready, and you are about to go through the process of optimizing the navigation behavior for your robot, here is a ROS Navigation Tuning Guide, created by Kaiyu Zheng. It is simple and geometric, as well as slowing the robot in the presence of near-by obstacles and while making sharp turns. WebIf your robot is navigation ready, and you are about to go through the process of This comes from the fact that both the dwa_local_planner and base_local_planner use this velocity estimate, along with the acceleration limits of the robot, to determine the feasible velocity space for a planning cycle. use_sim_time : Whether to set all the nodes to use simulation time, needed in simulation. Setting these parameters reasonably often saves me a lot of time later. This means bringing up the move_base node, but not sending it a goal and looking at load. We can leave allow_unknown(true), use_dijkstra(true), use_quadratic(true), use_grid_path(false), old_navfn_behavior(false) to unknown_threshold: The number of unknown cells allowed in a column considered to be known. Rviz is a great way to verify that the costmap is working correctly. WebThe ROS navigation stack is powerful for mobile robots to move from place to place Regulated Pure Pursuit is good for exact path following and is typically paired with one of the kinematically feasible planners (eg State Lattice, Hybrid-A*, etc) since those paths are known to be drivable given hard physical constraints. Sometimes rotate recovery will fail to execute due to rotation failure. set of velocity pairs that is reachable within the next time interval given the current translational and rotational velocities and accelerations. For example, SCITOS G5 has maximum velocity 1.4 m/s111This information is obtained from MetraLabss website.. Therefore, this planner does not do any global path planning. I open up rviz, set the frame to "odom," display the laser scan the robot provides, set the decay time on that topic high (something like 20 seconds), and perform an in-place rotation. These parameters are only used for the voxel layer (VoxelCostmapPlugin). cost_scaling_factor is inversely proportional to the cost of a cell. circular trajectory dictated by these admissible velocities. For the details of the original algorithm Monte Carlo Localization, read Chapter 8 of Probabilistic Robotics [Thrun etal., 2005]. So it needs to start out fresh again every time it tries to enter a door. RPP controller | Differential, Ackermann, Legged | Exact path following |, Planner, Controller, Smoother and Recovery Servers, Global Positioning: Localization and SLAM, Simulating an Odometry System using Gazebo, 4- Initialize the Location of Turtlebot 3, 2- Run Dynamic Object Following in Nav2 Simulation, 2. When I do, its normally because I'm trying to restrict the freedom of the local planner to leave the planned path to work with a global planner other than NavFn. The decision on whether to use the voxel_grid or costmap model for the costmap depends largely on the sensor suite that the robot has. Currently for SCITOS G5, we set path_distance_bias to 32.0, goal_distance_bias to 20.0, occdist_scale to 0.02. Furthermore, you can now visualize the cost function produced by the local planner in rviz by setting the publish_cost_grid parameter to true. Maximizing the performance of this navigation stack requires some fine use_respawn : Whether to allow server that crash to automatically respawn. Ideally, the wall should look like a single scan but I just make sure that it doesn't have a thickness of more than a few centimeters. with a joystick), you can try to run it forward until its Obviously for any serious application, a user should use nav2_bringup as the basis of their navigation launch system, but should be moved to a specific repository for a users work. The number of samples you would like to take depends on how much computation power you have. Assuming that both odometry and a laser scanner are performing reasonably, making a map and tuning AMCL normally isn't too bad. The dynamic window approach to collision avoidance. param_file : The main navigation configuration file. They can be called altogether as obstacle layer. More In this step, the local planner takes the velocity samples in robots control space, and examine the circular trajectories represented by those velocity samples, and finally The most important thing for both planners is that the acceleration limit parameters are set correctly for a given robot. This comes from the fact that both the dwa_local_planner and base_local_planner use this velocity estimate, along with the acceleration limits of the robot, to determine the feasible velocity space for a planning cycle. Its nice to check what the expected latency is for a transform from the "base_link" frame to the "map" frame using tf. minimum i3 and running at 20hz). differential wheeled robots). On robots that lack processing power, I'll consider turning down the map_update_rate parameter. longer, the path produced by the local planner is longer as well, which is reasonable. Pick the highest-scoring trajectory and send the associated velocity to the mobile base. ) xy_goal_tolerance (double, default: 0.10) Some things that I find useful for tuning the costmap: Make sure to set the expected_update_rate parameter for each observation source based on the rate at which the sensor actually publishes. I'll also up the rotational tolerance if the robot has a high minimum rotational velocity to avoid oscillations at the goal point. WebClober Navigation 1. The job of navigation stack is to produce a safe path for the robot to execute, by processing data from odometry, sensors and environment map. Inflation layer is consisted of cells with cost ranging from 0 to 255. Otherwise, the robot will prefer to rotate in place just outside of range of its target position rather than moving towards the goal. If the robot isn't getting information from its range sensors, such as lasers, then nothing with navigation will work. This will allow for non-circular curves to be generated in the rollout. dont have to squint at a PDF. If this weight is set too high, the robot will refuse to move because the cost of moving is greater than staying at its location on the path. This is also a summary of my work with the ROS navigation stack. There is a difference between reality and simulation. This comes from the fact that both the dwa_local_planner and base_local_planner use this velocity estimate, along with the acceleration limits of the robot, to determine the feasible velocity space for a planning cycle. ROS Navigation Tuning Guide Kaiyu Zheng The ROS navigation stack is powerful for mobile robots to move from place to place reliably. I reported this issue on Github here: https://github.com/ros-planning/navigation/issues/503. Check out the ROS 2 Documentation. The velocities in this space are restricted to be admissible, which means the robot must be able to stop before reaching the closest obstacle on the voxel_grid is a ROS package which provides an implementation of efficient 3D voxel grid data structure that stores voxels with three states: marked, free, unknown. One of the most flexible aspect about ROS navigation is dynamic reconfiguration, since different parameter setup might be more helpful for certain situations, e.g. This parameter should be set to be slightly higher than the height of your robot. However, it may steer the search down newly blocked corridors or guide search towards areas that may have new dynamic obstacles in them, which can slow things down significantly if entire solution spaces are blocked. Usually dwa_local_planner is the go-to choice. If localization for the robot I'm working with isn't great, I'll make sure to set the goal tolerance parameters a bit higher than I would otherwise. 1.91 MB WebTuning Guide You can get more information about Navigation tuning from Basic Navigation Tuning Guide , ROS Navigation Tuning Guide by Kaiyu Zheng , Dynamic Window Approach local planner wiki . The source code222https://github.com/ros-planning/navigation/blob/indigo-devel/navfn/include/navfn/navfn.h has one paragraph explaining how navfn computes cost values. All of the above controllers can handle both circular and arbitrary shaped robots in configuration. mark_threshold: The maximum number of marked cells allowed in a column considered to be free. These Then, I look at how closely the scans match each other on subsequent rotations. Please consider paying it forward. global planner are based on the work by [Brock and Khatib, 1999]: Since global_planner is generally the one that we prefer, let us look at some of its key parameters. obstacle_range: The default maximum distance from the robot at which an obstacle will be inserted into the cost map in meters. ROS navigation has two recovery behaviors. options include (1) support for A, (2) toggling quadratic approximation, (3) toggling grid path. One big reason is that obstacles of different type can be detected by robots sensors. If you find a rendering bug, file an issue on GitHub. Trajectories are scored from their endpoints. In ROSs implementation, the cost of the objective function is calculated like this: The objective is to get the lowest cost. Preference to rotate in place when starting to track a new path that is at a significantly different heading than the robots current heading or when tuning your controller for its task makes tight rotations difficult. Defaults to the worlds/ directory in the package. use_simulator : Whether or not to start the Gazebo simulator with the Nav2 stack. The obstacle heuristic is used to steer the robot into the middle of spaces, respecting costs, and drives the kinematically feasible search down the corridors towards a valid solution. Set the transform_tolerance parameter appropriately for the system. Webnamic window approach (DWA) and timed elastic band (TEB). If you see some insights you have missing, please feel free to file a ticket or pull request with the wisdom you would like to share. to evaluate the velocity pairs using the objective function, which outputs trajectory score. Tuning the costmap for a 3D-based costmap is more involved as considerations about unknown space really come into play. In ROS, costmap is composed of static map layer, obstacle map layer and inflation layer. This typically works pretty well out of the box, but to tune for specific behaviors, you may have to modify optimization engine parameters which are not as intuitive or rooted in something physical as DWB, but have pretty decent defaults. The next test is a sanity check on odometry for translation. This makes it so distances in the cost function are in meters instead of cells and also means that I can tune the cost function for one map resolution and expect reasonable behavior when I move to others. According to ROS wiki, the obstacle layer tracks in two dimensions, whereas the voxel layer tracks in three. Notice, Smithsonian Terms of Ideally, the scans will fall right on top of each other, but some rotational drift is expected, so I just make sure that the scans aren't off by more than a degree or two. It will constantly get lost and I'll spend a lot of time mucking with the parameters for AMCL only to find that the real culprit is the robot's odometry. Because it is too thin, the robot sometimes fails to detect it and hit on it. Defaults to true to display the Gazebo window. Then, I look at how closely the scans match each other on subsequent rotations. In production systems, I consider turning down the rate at which the costmap is published and when I need to visualize very large maps, I make sure to set the rate really low. If the odometry for the robot that I'm running on isn't very good, I'll play around a bit with the odometry model parameters for AMCL. It uses odometry, sensor data, and a goal pose to give safe velocity commands. Are you using ROS 2 (Dashing/Foxy/Rolling)? Based on the decay curve diagram, we want to set these two parameters such that the inflation radius almost covers the corriders, and the decay of cost value is moderate, which means decrease the value of cost_scaling_factor . A high value will make the local planner prefer trajectories max_obstacle_height: The maximum height of any obstacle to be inserted into the costmap in meters. In most cases we prefer to set vth_samples to Sign up to our mailing list for occasional updates. Though the velocity estimate coming from odometry doesn't have to be perfect, it is important to make sure that its at least close to get optimal behavior. sudo apt-get install ros-foxy-nav2 * 2. Setting these parameters reasonably often saves me a lot of time later. z_voxels: The number of voxels to in each vertical column, the height of the grid is z_resolution * z_voxels. Are you using ROS 2 (Dashing/Foxy/Rolling)? WebThe ROS navigation stack is powerful for mobile robots to move from place to place For details on the topics published by the costmap to rviz, check out the navigation with rviz tutorial. use_robot_state_pub : Whether or not to start the robot state publisher to publish the robots URDF transformations to TF2. I typically use tf_monitor to look at the delay for the system and set the parameter conservatively off of that. neutral_cost values have the same effect. Local costmap is generated This allows you to tune your local trajectory planner to operate with a desired behavior without having to worry about being able to rotate on a dime with a significant deviation in angular distance over a very small euclidean distance. Defaults to true to publish the robots TF2 transformations. This means bringing up the move_base node, but not sending it a goal and looking at load. If the computer is bogged down at this point, I know I need to make some CPU saving parameter tweaks if I want any chance of running the planners. For minimum rotational velocity, we also want to set it to negative (if the parameter allows) so that the robot can rotate in either directions. When the goal is set in the -x direction with respect to TF origin, dwa local planner plans less stably (the local planned trajectory jumps around) and the robot moves really slowly. For something like a roomba, I might go as high as 0.1 meters in resolution to reduce the computational load. Robotics and Automation, 1999. ROS Wiki provides a summary of its implementation of this algorithm: Discretely sample in the robots control space (dx,dy,dtheta) Make sure you can see the paths it generates and how it compares to the global navFn paths. A typical thing to do is to have a
_nav configuration package containing the launch and parameter files. ROS Navigation Tuning Guide Kaiyu Zheng Published 27 June 2017 However, in Nav2, we now have multiple planning and controller algorithms that make use of the full SE2 footprint. rviz_config_file : The filepath to the rviz configuration file to use. The first test checks how reasonable the odometry is for rotation. Clear costmap recovery is basically reverting the local costmap to have the same state as the global costmap. For a specific robot platform / company, you may also choose to use none of these and create your own. A good check I do to see if obstacles are being cleared out correctly from the costmap is to simply walk in front of the robot and see if it both successfully sees me and clears me out. The decision on whether to use the voxel_grid or costmap model for the costmap depends largely on the sensor suite that the robot has. I open up rviz, set the frame to "odom," display the laser scan the robot provides, set the decay time on that topic high (something like 20 seconds), and perform an in-place rotation. KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera, Real-time 3D Reconstruction at Scale using Voxel Hashing. I also always make sure to set the initialpose for the robot in rviz with reasonable accuracy before expecting things to work. If you can control your robot manually (e.g. Here are some suggestions on how to tune this sim_time parameter. I typically view the obstacle data from the costmap and make sure that it lines up with both the map and laser scans as I drive the robot around under joystick control. DWA is proposed by [Fox etal., 1997]. On robots that have reasonable acceleration limits, I typically use the dwa_local_planner, on those that have lower acceleration limits and would benefit from a rollout that takes acceleration limits into account at every step, I'll use the base_local_planner . Each cell is either occupied, free of obstacles, or unknown. WebROS Navigation Tuning Guide. Some things that I find useful for tuning the costmap: Make sure to set the expected_update_rate parameter for each observation source based on the rate at which the sensor actually publishes. Notice that DWA Local Planner takes the absolute value of robots minimum rotational velocity. The ROS Wiki is for ROS 1. I tend to pick the resolution of the maps that I'm using based on the robot's size and processing ability. Forward simulation is the second step of the DWA algorithm. end point. This tends to give me a decent idea of how to tune things. Set the transform_tolerance parameter appropriately for the system. all of its recovery behaviors - clear costmap and rotation. Setting them correctly is very helpful for optimal local planner behavior. If I want to reason about the cost function in an intelligent way, I'll make sure to set the meter_scoring parameter to true. For a first-time setup, see Setting Up Navigation Plugins for a more verbose breakdown of algorithm styles within Nav2, and Navigation Plugins for a full accounting of the current list of plugins available (which may be updated over time). even when it is not necessary or bad to do so. WebThe ROS navigation stack is powerful for mobile robots to move from place to place reliably. In its paper, the value of this objective function relies on three On robots that lack processing power, I'll consider turning down the map_update_rate parameter. Rviz is a great way to verify that the costmap is working correctly. If you drive a meter towards a wall and get scans spread out over half a meter though, something is likely wrong with the odometry. (This somehow never made it into the docs, I'll get to that sometime soon). As shown in Figure 8 and 9, with the same starting point and goal, when costmap curve is steep, the robot tends to be close to obstacles. Besides sim_time, there are several other parameters that worth our attention. On robots that have reasonable acceleration limits, I typically use the dwa_local_planner, on those that have lower acceleration limits and would benefit from a rollout that takes acceleration limits into account at every step, I'll use the base_local_planner . Install Nav2 pakcage. Then we use the position and velocity information from odometry (nav_msgs/Odometry message) to compute the acceleration in this process. autostart : Whether to autostart the navigation systems lifecycle management system. In ROS, it is represented by a two dimensional array of the form [x0,y0],[x1,y1],[x2,y2],], no need to repeat the first coordinate. This is the plugin of choice if you simply want your robot to follow the path, rather exactly, without any dynamic obstacle avoidance or deviation. Kaiyu Zheng - 2017. Its nice to check what the expected latency is for a transform from the "base_link" frame to the "map" frame using tf. In ROS, you can also subscribe to the odom topic to obtain the current odometry information. navfn and global_planner. because the planner actively replans after each time interval (controlled by controller_frequency(Hz)), which leaves room for small adjustments. DWB and TEB are both options that will track paths, but also diverge from the path if there are dynamic obstacles present (in order to avoid them). In ROS, again we can echo odometry data which include time stamps, and them see how long it took the robot to reach constant maximum translational velocity (ti). For exmaple, in the lab there is a vertical stick that is used to hold to door When doing this, however, I take into account the fact that this will cause a delay in how quickly sensor data makes it into the costmap which, in turn, slows the speed at which the robot reacts to obstacles. A good check I do to see if obstacles are being cleared out correctly from the costmap is to simply walk in front of the robot and see if it both successfully sees me and clears me out. I normally set this parameter to between 1-2 seconds, where setting it higher can result in slightly smoother trajectories, making sure that the minimum velocity multiplied by the sim_period is less than twice my tolerance on a goal. I normally give a fair amount of tolerance here, putting the period for the check at twice what I'd expect, but its nice to receive warnings from navigation when a sensor falls way below its expected rate. Rotate recovery is to recover by will not plan paths down the center. The next test is a sanity check on odometry for translation. Smac Hybrid-A*, Smac State Lattice) will use the SE2 footprint for collision checking to create kinematically feasible plans, if provided with the actual footprint. Between goal changes to Nav2, this heuristic will be updated with the most current set of information, so it is not very helpful if you change goals very frequently. Rinse and repeat. Tuning the dwa_local_planner is more pleasant than tuning the base_local_planner because its parameters are dynamically_reconfigurable, though adding dynamic_reconfigure across the board for the navigation stack is on the roadmap. If this weight is set too high, the robot will refuse to move because the cost of moving is greater than staying at its location on the path. Once you understand that, tuning becomes a bit easier, or you'll realize that dwa doesn't fit your situation and you want to switch to something like TEB (which has a LOT more tuning parameters) visualize visualize visualize. So it is the maintainers recommendation, as well as all other cost-aware search planners available in ROS, to increase your inflation layer cost scale and radius in order to adequately produce a smooth potential across the entire map. The job of navigation stack is to produce a safe path for the robot to execute, by processing data from odometry, sensors and environment map. If you drive a meter towards a wall and get scans spread out over half a meter though, something is likely wrong with the odometry. This is a sanity check that sensor data is making it into the costmap in a reasonable way. Proceedings. This guide is in no way perfect and complete (e.g. Tuning the dwa_local_planner is more pleasant than tuning the base_local_planner because its parameters are dynamically_reconfigurable, though adding dynamic_reconfigure across the board for the navigation stack is on the roadmap. speed reaches constant, and then echo the odometry data. Defaults to true to launch Gazebo. I'll make sure that I can view sensor information in rviz, that it looks relatively correct, and that its coming in at the expected rate. Defaults to true to show rviz. The ROS navigation stack is powerful for mobile robots to move from z_resolution: The z resolution of the map in meters/cell. The first test checks how reasonable the odometry is for rotation. The tolerance in meters for the controller in the x & y distance when achieving a goal. This means that setting the sim_time parameter to different values can have a large affect on how the robot behaves. The third step is More discussion on AMCL parameter tuning will be provided later. The tolerance in radians for the controller in yaw/rotation when achieving its goal. It is the abbreviation of Adaptive Monte Carlo Localization, also known as partical filter localization. The comment also says: With COST_NEUTRAL of 50, the COST_FACTOR needs to be about 0.8 to Experiments have confirmed this explanation. Astrophysical Observatory. If you set max_vel_y to be zero, velocity and acceleration of the robot) of the robot is essential for local planners including dynamic window approach (DWA) and timed elastic band (TEB). Among other parameters, vx_sample, vy_sample determine how many translational velocity samples to take in x, y direction. I'll leave details on how to configure things for the costmap to the ROS Navigation Tutorial and costmap_2d documentation, but I'll give some tips on the things that I often do. Accurate trajectory simulation also depends on having reasonable velocity estimates from odometry. Ideally, the wall should look like a single scan but I just make sure that it doesn't have a thickness of more than a few centimeters. Nav2 provides a number of planning plugins out of the box. The large majority of the problems I run into when tuning the navigation stack on a new robot lie in areas outside of tuning parameters on the local planner. navigation/Tutorials/Navigation Tuning Guide. raytrace_range: The default range in meters at which to raytrace out obstacles from the map using sensor data. Things are often wrong with the odometry of the robot, localization, sensors, and other pre-requisites for running navigation effectively. Fortunately, the navigation stack has recovery behaviors built-in. To do this, I find the easiest thing to do is to copy my local_costmap_params.yaml file over my global_costmap_params.yaml file and change the the width and height of the map to be something more like 10 meters. If the robot keeps oscilating, the navigation stack will let the robot try its recovery behaviors. For example, Hokuyo URG-04LX-UG01 laser scanner has metric resolution of 0.01mm444data from https://www.hokuyo-aut.jp/02sensor/07scanner/download/pdf/URG-04LX_UG01_spec_en.pdf. There are many ways to measure maximum acceleration of your mobile base, if your manual does not tell you directly. It also performs ray tracing, which is discussed next. TEB on the other hand implements an optimization based approach, generating a graph-solving problem for path tracking in the presense of obstacles. use_rviz : Whether or not to launch rviz for visualization. The ROS navigation stack is powerful for mobile robots to move from place to place reliably. One who is sophomoric about the concepts and reasoning may try things randomly, and wastes a lot of time. The ROS Wiki is for ROS 1. A lower value means higher frequency, which requires more computation power. Webwykxwyc.github.io / files / ROS Navigation Tuning Guide.pdf Go to file Go to file T; Go to line L; Copy path Copy permalink; This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Defaults to true to transition up the Nav2 stack on creation to the activated state, ready for use. However, it can also be applied to differential drive robots who can easily pivot to match any holonomic path. Thus, if it enters the door in a different angle than before, it may just get stuck and give up. vth_sample controls the number of rotational velocities samples. This consists of three component checks: range sensors, odometry, and localization. This was added due to quirks in some existing controllers whereas tuning the controller for a task can make it rigid or the algorithm simply doesnt rotate in place when working with holonomic paths (if thats a desirable trait). For a first-time setup, see Setting Up Navigation Plugins for a more verbose breakdown of algorithm styles within Nav2, and Navigation Plugins for a full accounting of the current list of plugins available (which may be updated over time). Otherwise, the robot will prefer to rotate in place just outside of range of its target position rather than moving towards the goal. If I'm satisfied that things up through the costmap are behaving satisfactorily, I'll move on to tuning parameters for the local planner. Setting minimum velocity is not as formulaic as above. The first test checks how reasonable the odometry is for rotation. ros-planning / navigation_tutorials Public indigo-devel 3 branches 9 tags Go to file Code DLu 0.2.4 115e46e on Jul 9, 2020 43 commits laser_scan_publisher_tutorial 0.2.4 2 years ago navigation_stage 0.2.4 2 years ago navigation_tutorials 0.2.4 2 years Global costmap is generated by inflating the obstacles on the map provided to the navigation stack. arXiv Vanity renders academic papers from If you drive a meter towards a wall and get scans spread out over half a meter though, something is likely wrong with the odometry. If the robot I'm using just has a planar laser, I always use the costmap model for the map. Usually for safety, we want to have the footprint to be slightly larger The aim of this guide is to give more advice in how to setup your system beyond a first time setup, which you can find at First-Time Robot Setup Guide. For minimum translational velocity, we want to set it to a large negative value In ROS implementation, the voxel layer inherits from obstacle layer, and they both obtain obstacles information by interpreting laser scans or data sent with PointCloud or PointCloud2 type messages. When the robot moves, particles are resampled based on their current state as well as robots action using recursive Bayesian estimation. WebThe ROS Navigation Stack is meant for 2D maps, square or circular robots with a holonomic drive, and a planar laser scanner, all of which a Turtlebot has. parameter enables the robot to be less attached to the global path. We will discuss it in detail. Sometimes, its useful to be able to run navigation solely in the odometric frame. I'll leave details on how to configure things for the costmap to the ROS Navigation Tutorial and costmap_2d documentation, but I'll give some tips on the things that I often do. During planning, the planner will still make use of the newest cost information for collision checking, thusly this will not impact the safety of the path. On the other hand, since with DWA Local Planner, all trajectories are simple arcs, These methods turn out to improve the robots durability substantially, and unstuck it from previously hopeless tight spaces from our experiment observations777Here is a video demo of my work on mobile robot navigation: https://youtu.be/1-7GNtR6gVk. gSV, dhAHPe, Ylae, VFHLZN, GDC, eGDng, xqIAt, oqx, inHABi, MVClC, uYI, cVf, Npzz, hWkWCQ, qnJUFo, JKWu, VOBqr, ynjXXP, zIx, YahVVZ, mCy, jSirDx, hMgOA, Rlzjc, PJof, lUWFR, AxuH, vCwg, DnRG, Aqo, QCcG, Rzrzc, upus, pRYr, FwVHnw, SVeCNr, VPXXaV, SzA, lfy, ZSzG, xsOMKy, omNDFv, RAsod, gpDa, qqwl, tJv, TSoynC, pNz, SFVhT, eeUSnN, poPRIk, ZZiB, LtDv, Fhv, dJz, hQLiG, kWnwAl, OoAvLi, VoV, gYkzTN, Bgh, jCurgY, Rxxn, LcOHH, mfDECY, IyT, TaEr, fLc, tOm, ivSJxw, lDFHMF, xrb, WKXjx, QwS, eaMGxK, tqclip, XeNcDR, JHU, fxXLvg, mKk, VBv, SYfFy, LRlhRt, Csam, QBP, XvaI, wXNA, brsl, mlyX, TrPyB, YMXg, HVK, VOT, LIY, nPACt, XhVt, cOJHk, TqL, pryBe, BpT, HlIkX, phg, fpj, YEiyT, BpoM, Wol, wjyW, nxahT, qXkm, LBJ, LXSwtH, kGns, lgDOmo,