Otherwise, the robot will prefer to rotate in place just outside of range of its target position rather than moving towards the goal. 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. The green line is the global path produced by global_planner. 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. 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'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. This is a sanity check that sensor data is making it into the costmap in a reasonable way. Astrophysical Observatory. Take a look at figure 17. 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. function that depends on (1) the progress to the target, (2) clearance from obstacles, and (3) forward velocity to produce the optimal velocity pair. map : The filepath to the map to use for navigation. Rviz is a great way to verify that the costmap is working correctly. headless : Whether or not to launch the Gazebo front-end alongside the background Gazebo simulation. Want to hear about new tools we're making? If youre willing to contribute this work back to the community, please file a ticket or contact a maintainer! It basically means how frequent should the points on this trajectory be examined (test if they intersect with any obstacle or not). inflation_radius and cost_scaling_factor are the parameters that determine the inflation. In ROSs implementation, the cost of the objective function is calculated like this: The objective is to get the lowest cost. The ROS navigation stack is powerful for mobile robots to move from place to place reliably. their default values. The next test is a sanity check on odometry for translation. For something like a roomba, I might go as high as 0.1 meters in resolution to reduce the computational load. 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. Sign up to our mailing list for occasional updates. Nav2s launch files are made to be very configurable. raytrace_range: The default range in meters at which to raytrace out obstacles from the map using sensor data. I typically use tf_monitor to look at the delay for the system and set the parameter conservatively off of that. This consists of three component checks: range sensors, odometry, and localization. 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. Accurate trajectory simulation also depends on having reasonable velocity estimates from odometry. Sometimes, its useful to be able to run navigation solely in the odometric frame. These trajectories are also generated via plugins that can be replaced, but support out of the box Omni and Diff robot types within the valid velocity and acceleration restrictions. If these parameters are off, I'd expect sub-optimal behavior from a robot. 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. Setting it higher will make the decay curve more steep. 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. International Conference on. Local planners that adhere to nav_core::BaseLocalPlanner interface are dwa_local One who is sophomoric about the concepts and reasoning may try things randomly, and wastes a lot of time. in obstacle layer and voxel layer? 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. 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. After setting dwa to true, I'll also make sure to update the vx_samples parameter to something between 8 and 15 depending on the processing power available. end point. Once I'm satisfied that the robot satisfies the prerequisites for navigation, I like to make sure that the costmap is setup and configured properly. cost = COST_NEUTRAL + COST_FACTOR * costmap_cost_value. This guide is meant to assist users in tuning their navigation system. speed reaches constant, and then echo the odometry data. 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. This is a really easy way to get things up and running if you want to tune navigation independent of localization performance. Its a good idea to check the system load when the navigation stack is running only the costmap. It will allow search to be weighted towards freespace far before the search algorithm runs into the obstacle that the inflation is caused by, letting the planner give obstacles as wide of a berth as possible. Setting these parameters reasonably often saves me a lot of time later. y velocity should be set to zero for non-holonomic robot (such as We observed some weird behavior of the navigation stack. Setting these parameters reasonably often saves me a lot of time later. 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. This parameter should be set to be slightly higher than the height of your robot. Clear costmap recovery is basically reverting the local costmap to have the same state as the global costmap. Set the transform_tolerance parameter appropriately for the system. I actually rarely find myself changing the path_distance_bias and goal_distance_bias parameters on the planners very much. Ok, on to tips for the planners: If the robot I'm working with has low acceleration limits, I make sure that I'm running the base_local_planner with dwa set to false. ROS navigation has two recovery behaviors. backing off). These are simply the default and available plugins from the community. It's free to sign up and bid on jobs. Robot operating system (ros): The complete reference (volume 1). WebIs the robot navigation ready? 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 . Eventually it passes this valid goal as a plan to the local planner or controller (internally). The node move_base is where all the magic happens in the ROS Navigation Stack. WebThis tutorial provides step-by-step instructions for how to get the navigation stack Usually for safety, we want to have the footprint to be slightly larger Once I'm satisfied that the robot satisfies the prerequisites for navigation, I like to make sure that the costmap is setup and configured properly. Global costmap is generated by inflating the obstacles on the map provided to the navigation stack. information on other planners will be provided later. 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. If you find a rendering bug, file an issue on GitHub. parameter. the velocity space within the dynamic window. 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. 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. 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. 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. Are you using ROS 2 (Dashing/Foxy/Rolling)? Smac Hybrid-A*, Smac State Lattice), this is not a necessary behavioral optimization. Therefore, we may need to figure out a more robust solution. I'll also up the rotational tolerance if the robot has a high minimum rotational velocity to avoid oscillations at the goal point. not many moving things or changes, not using live sensor updates in the global costmap, etc) environments when planning across large spaces to singular goals. If your robot is truly circular, continue to use the robot_radius parameter. In its paper, the value of this objective function relies on three inflation_radius controls how far away the zero cost point is from the obstacle. Credit to Ramkumar Gandhinathan and Lentin Or, you can move your robot on a piece of large paper, then draw the contour of the base. you should take a look at the resolution of your laser scanner, because when creating the map using gmapping, if the laser scanner has lower resolution than your desired use_namespace : Whether or not to launch robots into this namespace. While its true that you can simply inflate a small radius around the walls to weight against critical collisions, the true value of the inflation layer is creating a consistent potential field around the entire map. This will allow for non-circular curves to be generated in the rollout. amcl is a ROS package that deals with robot localization. Voxels are 3D volumetric cubes (think 3D pixel) which has certain relative position in space. around obstacles and the planner will then treat (for example) the Within the circular robot regime, the choice of planning algorithm is dependent on application and desirable behavior. Experiments show that increasing this 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. unknown_threshold: The number of unknown cells allowed in a column considered to be known. Finally, the Rotation Shim Plugin helps assist plugins like TEB and DWB (among others) to rotate the robot in place towards a new paths heading before starting to track the path. For something like a roomba, I might go as high as 0.1 meters in resolution to reduce the computational load. obstacle_range: The default maximum distance from the robot at which an obstacle will be inserted into the cost map in meters. Setting them correctly is very helpful for optimal local planner behavior. This section concerns with synchro-drive robots. If you can control your robot manually (e.g. A good blog on voxel ray tracing versus polygong ray tracing: http://raytracey.blogspot.com/2008/08/voxel-ray-tracing-vs-polygon-ray.html. The first test checks how reasonable the odometry is for rotation. 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. in navfn. On robots that lack processing power, I'll consider turning down the map_update_rate parameter. Tuning the costmap for a 3D-based costmap is more involved as considerations about unknown space really come into play. there is no need to have velocity samples in y direction since there will be no usable samples. w"~k%FNuZ* W%]Efw+ O{:TC/ UW=M@>^=|:T`b. The default value of 0.025 is generally enough for turtlebot-sized mobile base. A good test for the whole system is to make sure that laser scans and the map can be visualized in the "map" frame in rviz and that the laser scans match up well with the map of the enviroment. So it needs to start out fresh again every time it tries to enter a door. This is the main file used for simulating the robot and contains the following configurations: slam : Whether or not to use AMCL or SLAM Toolbox for localization and/or mapping. the endpoint of the trajectory, ) to local goal % Do several trails and take the average. Webnamic window approach (DWA) and timed elastic band (TEB). Defaults to true to publish the robots TF2 transformations. Things are often wrong with the odometry of the robot, localization, sensors, and other pre-requisites for running navigation effectively. 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. 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. There has been quite a few research around online 3D reconstruction with the depth cameras via voxels. This tends to give me a decent idea of how to tune things. The result is an awkward, stuttering, or whipping around behavior when your robots initial and path headings are significantly divergent. First, I'll run either gmapping or karto and joystick the robot around to generate a map. This means bringing up the move_base node, but not sending it a goal and looking at load. The next test is a sanity check on odometry for translation. neutral_cost values have the same effect. If the robot keeps oscilating, the navigation stack will let the robot try its recovery behaviors. Cham: Springer International Publishing. The third step is In most cases we prefer to set vth_samples to For voxel layer, this is basically the height of the voxel grid. sudo apt-get install ros-foxy-nav2 * 2. The publish_frequency parameter is useful for visualizing the costmap in rviz. There are a number of important parameters that should be set as good as possible. This will allow for non-circular curves to be generated in the rollout. This sometimes leads me to find issues with how transforms are being published for a given robot. Currently for SCITOS G5, we set path_distance_bias to 32.0, goal_distance_bias to 20.0, occdist_scale to 0.02. 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. This problem is not that unavoidable, all of its recovery behaviors - clear costmap and rotation. If I'm satisfied that things up through the costmap are behaving satisfactorily, I'll move on to tuning parameters for the local planner. 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. 1.91 MB Defaults to true to launch Gazebo. Static map layer directly interprets the given static SLAM map provided to the navigation stack. 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. If the robot isn't getting information from its range sensors, such as lasers, then nothing with navigation will work. If the robot I'm using just has a planar laser, I always use the costmap model for the map. Besides these parameters, there are three other unlisted parameters that actually determine the quality of the planned The publish_frequency parameter is useful for visualizing the costmap in rviz. Otherwise, the robot will prefer to rotate in place just outside of range of its target position rather than moving towards the goal. This can be over-ridden on a per-sensor basis. Rotational velocity (rad/s) is equivalent as angular velocity; its maximum value is the angular velocity of the robot when it is rotating in place. 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. Experiments have confirmed this explanation. 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. than the robots real contour. grass)555 mentioned in Using Robots in Hazardous Environments by Boudoin, Habib, pp.370. setting the sim_time to a very high value (>=5.0) will result in long curves that are not very flexible. whole width of a narrow hallway as equally undesirable and thus It uses odometry, sensor data, and a goal pose to give safe velocity commands. Nav2 allows users to specify the robots shape in 2 ways: a geometric footprint or the radius of a circle encompassing the robot. Are you using ROS 2 (Dashing/Foxy/Rolling)? If I've decided to track unknown space with a robot, mostly these are robots that are using the voxel_grid model for the costmap, I make sure to look at the unknown space visualization to see that unknown space is being cleared out in a reasonable way. For details on the topics published by the costmap to rviz, check out the navigation with rviz tutorial. Nav2 provides a number of controller plugins out of the box. Given the cost function in meters, I can compute the tradeoff in cost of moving 1 meter towards the goal balanced against how far away I am from the planned path. GitHub - ros-planning/navigation_tutorials: Tutorials about using the ROS Navigation stack. Smacs Hybrid-A* and State Lattice Planners provide an option, cache_obstacle_heuristic. Discard illegal trajectories (those that collide with obstacles). If I've decided to track unknown space with a robot, mostly these are robots that are using the voxel_grid model for the costmap, I make sure to look at the unknown space visualization to see that unknown space is being cleared out in a reasonable way. A typical thing to do is to have a _nav configuration package containing the launch and parameter files. I typically use tf_monitor to look at the delay for the system and set the parameter conservatively off of that. More For something like a roomba, I might go as high as 0.1 meters in resolution to reduce the computational load. Hope this guide is helpful. 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. In ROS navigation stack, local planner The advantage is that the robot would prefer to move in the middle of obstacles. If COST_FACTOR is higher, cost values will have a plateau It is also useless if you have too many voxels in a column but not enough resolution, because each vertical column has a limit in height. Yet, it is not necessary to do a lot of dynamic reconfiguration. The tolerance in meters for the controller in the x & y distance when achieving a goal. 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. This parameter can be set separately for local costmap and global costmap. Theta*, Smac 2D-A*, or NavFn), you may continue to use the circular footprint, since these planners are not kinematically feasible and will not make use of the SE2 footprint anyway. Its nice to check what the expected latency is for a transform from the "base_link" frame to the "map" frame using tf. Then, I'll drive the robot straight at the wall and look at the thickness of the wall as reported by the aggregated laser scans in rviz. 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. Furrer, F., Burri, M., Achtelik, M., and Siegwart, R. (2016). ROS Navigation Tuning Guide Kaiyu Zheng Published 27 June 2017 The second step is basically obliterating velocities (i.e. trajectory in obstacle cost (0-254). Some controllers when heavily tuned for accurate path tracking are constrained in their actions and dont very cleanly rotate to a new heading. These parameters are straightforward to understand. Defaults to map.yaml in the packages maps/ directory. WebClober Navigation 1. For example, if you have a very long but skinny robot, the circular assumption wouldnt allow a robot to plan into a space only a little wider than your robot, since the robot would not fit length-wise. First, I'll run either gmapping or karto and joystick the robot around to generate a map. One who is sophomoric about the concepts and reasoning may try things randomly, and wastes a lot of time. 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. Navigation in an unknown environment without a map. TEB is pretty good at handling dynamic situations well with other moving agents in the scene, but at a much higher compute cost that makes it largely unsuitable for smaller compute platform robots (e.g. Set the transform_tolerance parameter appropriately for the system. It is the abbreviation of Adaptive Monte Carlo Localization, also known as partical filter localization. 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 it is higher, the voxel layers are denser. rviz_config_file : The filepath to the rviz configuration file to use. Therefore, this planner does not do any global path planning. use_simulator : Whether or not to start the Gazebo simulator with the Nav2 stack. With the above understanding, let us look into the parameters for the obstacle layer666Some explanations are directly copied from costmap2d ROS Wiki. whether its location is an obstacle. For minimum translational velocity, we want to set it to a large negative value I'll set up rviz the same way with the robot a few meters away from a wall. This is a really easy way to get things up and running if you want to tune navigation independent of localization performance. 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 Translational velocity (m/s) is the velocity when robot is moving in a straight line. dwa_local_planner uses Dynamic Window Approach (DWA) algorithm. xy_goal_tolerance (double, default: 0.10) when robot is close to the goal. The run-time of the feasible planners are typically on par (or sometimes faster) than their holonomic counterparts, so dont let the more recent nature of them fool you. navigation/Tutorials/Navigation Tuning Guide. As such, I always run two sanity checks to make sure that I believe the odometry of a robot. A good test for the whole system is to make sure that laser scans and the map can be visualized in the "map" frame in rviz and that the laser scans match up well with the map of the enviroment. For details on the topics published by the costmap to rviz, check out the navigation with rviz tutorial. Check out the ROS 2 Documentation. Accurate trajectory simulation also depends on having reasonable velocity estimates from odometry. Trajectories are scored from their endpoints. Through experiments, we observed that the longer the value of sim_time, the heavier the computation load becomes. Some of the most popular tuning guides for ROS Navigation / Nav2 even call this out specifically that theres substantial benefit to creating a gentle potential field across the width of the map - after inscribed costs are applied - yet very few users do this in practice. To determine the footprint of a robot, the most straightforward way is to refer to the drawings of your robot. Besides, you can manually take a picture of the top view of its base. Therefore, tuning the parameters for the local costmap is crucial for optimal behavior of DWA local planner. If these parameters are off, I'd expect sub-optimal behavior from a robot. If I'm using a low resolution for CPU reasons, I'll sometimes up the sim_granularity parameter a bit to save some cycles. As mentioned above, costmap parameters tuning is essential for the success of local planners (not only for DWA). It will prevent a number of stuck robot situations that could have been easily avoided. Trajectories are scored from their endpoints. A lower value means higher frequency, which requires more computation power. _planner, eband_local_planner and teb_local_planner. use_rviz : Whether or not to launch rviz for visualization. Obstacle map layer includes 2D obstacles and 3D obstacles (voxel layer). 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. At this point, the robot may just give up because it has tried 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). This sometimes leads me to find issues with how transforms are being published for a given robot. better chance for the local planner to find a path. 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. 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. 0.01), all the voxels will be put together and thus you wont get useful costmap information. If you have more than enough computation power, If you provide a footprint of your robot, it will be used to make sure trajectories are valid and it is recommended you do so. Defaults to nav2_params.yaml in the packages params/ directory. The fourth and fifth steps are easy to understand: take the current best In both simulation and reality, the robot gets stuck and gives up the goal. On robots that lack processing power, I'll consider turning down the map_update_rate parameter. See the Writing a New Planner Plugin tutorial for more details. Maximizing the performance of this navigation stack This 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 This will allow the planners and controllers to plan or create trajectories into tighter spaces. I also always make sure to set the initialpose for the robot in rviz with reasonable accuracy before expecting things to work. Set the transform_tolerance parameter appropriately for the system. 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. Next, we will look at parameters in forward simulation, trajectory scoring, costmap, and so on. High-speed navigation using the global dynamic window approach. differential wheeled robots). This tuning guide is a perpetual work in progress. It also performs ray tracing, which is discussed next. Evaluate (score) each trajectory resulting from the forward simulation, using a metric that incorporates characteristics such as: proximity to obstacles, proximity to the goal, proximity to the global path, and speed. To obtain maximum rotational velocity, we can control the robot by a joystick and rotate the robot 360 degrees after the robots speed reaches constant, and time this movement. be higher than translational velocity samples, because turning is generally a more complicated condition than moving straight ahead. Click To Get Model/Code. For each sampled velocity, perform forward simulation from the robots current state to predict what would happen if the sampled velocity were applied for some (short) period of time. to path from % This will allow for non-circular curves to be generated in the rollout. It can be used to be associated with data or properties of the volume near it, e.g. For lethal_cost, setting it to a low value may result 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. 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 you are willing to chip in, some ideas are in https://github.com/ros-planning/navigation.ros.org/issues/204, but wed be open to anything you think would be insightful! takes in odometry messages (odom topic) and outputs velocity commands (cmd_vel topic) that controls the robots motion. Then pick some vertices and use rulers to figure out their coordinates. occdist_scale is the weight for how much the robot should attempt to avoid obstacles. 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 tend to pick the resolution of the maps that I'm using based on the robot's size and processing ability. This means bringing up the move_base node, but not sending it a goal and looking at load. 1999 IEEE Note that the voxel grid is not recreated when updating, but only updated unless the size of local costmap is changed. These two layers are responsible for marking obstacles on the costmap. In ROS navigation, we need to know translational and rotational velocity and acceleration. 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. in failure to produce any path, even when a feasible path is obvious. 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. 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. This means that setting the sim_time parameter to different values can have a large affect on how the robot behaves. Besides sim_time, there are several other parameters that worth our attention. Setting minimum velocity is not as formulaic as above. If I'm using a low resolution for CPU reasons, I'll sometimes up the sim_granularity parameter a bit to save some cycles. Think of it like a 2D cost-aware search to prime the planner about where it should go when it needs to expend more effort in the fully feasible search / SE2 collision checking. The parameters for ROSs recovery behavior can be left as default in general. Assuming that both odometry and a laser scanner are performing reasonably, making a map and tuning AMCL normally isn't too bad. This article intends to guide the reader through the process of fine tuning navigation parameters. 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. This is also a summary of my work with the ROS navigation stack. On robots that lack processing power, I'll consider turning down the map_update_rate parameter. 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. This is the simplest one. Setting cost_factor to too low or too high lowers Then, I'll use that map with AMCL and make sure that the robot stays localized. 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. Turning the path_distance_bias parameter up, will make the robot follow the path more closely at the expense of moving towards the goal quickly. The origin of the coordinates should be the center of the robot. represents a circular trajectory that is optimal for robots local condition. This means that setting the sim_time parameter to different values can have a large affect on how the robot behaves. 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. Obstacles are marked (detected) or cleared (removed) based on data from robots sensors, which has topics for costmap to subscribe to. Discard illegal trajectories (those that collide with obstacles). I also always make sure to set the initialpose for the robot in rviz with reasonable accuracy before expecting things to work. This tends to give me a decent idea of how to tune things. Robots that can rotate in place, such as differential and omnidirectional robots. Rinse and repeat. Default false and uses global namespace for single robot. After a few experiments we observed that when cost_factor = 0.55, neutral_cost = 66, and lethal_cost = 253, the global path is quite desirable. rotating 360 degrees in place. navfn uses Dijkstras algorithm to find a global path with minimum cost between start point and There are three global planners that adhere to nav_core::BaseGlobal Wiki: ja/navigation/Tutorials/Navigation Tuning Guide (last edited 2013-05-05 19:10:52 by EisokuKuroiwa), Except where otherwise noted, the ROS wiki is licensed under the. z_resolution: The z resolution of the map in meters/cell. This tends to give me a decent idea of how to tune things. Wiki: cn/navigation/Tutorials/Navigation Tuning Guide (last edited 2017-07-31 15:38:44 by stevenli), Except where otherwise noted, the ROS wiki is licensed under the, https://github.com/zkytony/ROSNavigationGuide. vth_sample controls the number of rotational velocities samples. To avoid giving up, we used SMACH to continuously try different recovery behaviors, with additional ones such as setting a temporary goal that is very close to the robot, and returning to some previously visited pose (i.e. DWA is proposed by [Fox etal., 1997]. TEB on the other hand implements an optimization based approach, generating a graph-solving problem for path tracking in the presense of obstacles. The most important thing for both planners is that the acceleration limit parameters are set correctly for a given robot. Ok, on to tips for the planners: If the robot I'm working with has low acceleration limits, I make sure that I'm running the base_local_planner with dwa set to false. y velocity is the velocity in the direction perpendicular to that straight movement. In complicated indoor environments, this planner is not very practical. An annoying thing about robot navigation is that the robot may get stuck. 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 . Defaults to true to show rviz. Besides, z_resolution controls how dense the voxels is on the z-axis. But when the goal is set in the +x direction, dwa local planner is much more stable, and the robot can move faster. Many users and ecosystem navigation configuration files the maintainers find are really missing the point of the inflation layer. Actually, these parameters also present Default true to use single process Nav2. WebThe ROS navigation stack is powerful for mobile robots to move from place to place A high value for this If I'm using a low resolution for CPU reasons, I'll sometimes up the sim_granularity parameter a bit to save some cycles. use_robot_state_pub : Whether or not to start the robot state publisher to publish the robots URDF transformations to TF2. Each velocity sample is simulated as if it is applied to the robot for a set time interval, controlled by sim_time(s) The ROS Wiki is for ROS 1. For safety, we prefer to set maximum translational and rotational velocities to be lower than their actual maximum values. If I've decided to track unknown space with a robot, mostly these are robots that are using the voxel_grid model for the costmap, I make sure to look at the unknown space visualization to see that unknown space is being cleared out in a reasonable way. Its nice to check what the expected latency is for a transform from the "base_link" frame to the "map" frame using tf. Particles are all sampled randomly initially. Footprint is the contour of the mobile base. 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. Then, I'll use that map with AMCL and make sure that the robot stays localized. Given the cost function in meters, I can compute the tradeoff in cost of moving 1 meter towards the goal balanced against how far away I am from the planned path. This consists of three component checks: range sensors, odometry, and localization. If the robot isn't getting information from its range sensors, such as lasers, then nothing with navigation will work. 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. Often, I'll have a lot of trouble getting a robot to localize correctly. This localization technique works like this: Each sample stores a position and orientation data representing the robots pose. 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. In this video I show a couple important parameters when tuning the Navigation Stack of a mobile robot using ROS. In ROS navigation stack, local planner takes in odometry messages (odom topic) and outputs velocity Max/min velocity and acceleration are two basic parameters for the mobile base. map resolution, there will be a lot of small unknown dots because the laser scanner cannot cover that area, as in Figure 10. Other controllers have a spiral out behavior because their sampling requires some translational velocity, preventing it from simply rotating in place. For exmaple, in the lab there is a vertical stick that is used to hold to door WebThe ROS navigation stack is powerful for mobile robots to move from place to place reliably. and a local planner. The ROS navigation stack is powerful for mobile robots to move from cost_scaling_factor is inversely proportional to the cost of a cell. Among other parameters, vx_sample, vy_sample determine how many translational velocity samples to take in x, y direction. This will by no means cover all of the parameters (so please, do review the configuration guides for the packages of interest), but will give some helpful hints and tips. Since the planning problem is primarily driven by the robot type, the table accurately summarizes the advice to users by the maintainers. Defaults to the worlds/ directory in the package. This is a guide for ROS navigation parameters tuning. Hopefully it is helpful for you to understand concepts and reasonings behind different components in ROS navigation stack and how to tune them well. It is also a summary for part of my research work. Pick the highest-scoring trajectory and send the associated velocity to the mobile base. ) Fortunately, the navigation stack has recovery behaviors built-in. For very large open spaces, its fine to have 0-cost areas in the middle, but for halls, aisles, and similar; please create a smooth potential to provide the best performance. kill off bad trajectories) that are not admissible. The kinematically feasible planners (e.g. ensure the input values are spread evenly over the output range, 50 The dynamics (e.g. This guide is in no way perfect and complete (e.g. 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 . The dynamic window approach to collision avoidance. Sometimes rotate recovery will fail to execute due to rotation failure. The comment also says: With COST_NEUTRAL of 50, the COST_FACTOR needs to be about 0.8 to As such, I always run two sanity checks to make sure that I believe the odometry of a robot. This does require a bit of tuning for a given platform, application, and desired behavior, but it is possible to tune DWB to do nearly any single thing well. by inflating obstacles detected by the robots sensors in real time. 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. KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera, Real-time 3D Reconstruction at Scale using Voxel Hashing. Install Nav2 pakcage. In Figure 14, inflation_radius = 0.55, cost_scaling_factor = 5.0; In Figure 15, inflation_radius = 1.75, cost_scaling_factor = 2.58. 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. Here are some of them. 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. We will discuss it in detail. WebIf your robot is navigation ready, and you are about to go through the process of Configure Costmap Filter Info Publisher Server, 0- Familiarization with the Smoother BT Node, 3- Pass the plugin name through params file, 3- Pass the plugin name through the params file, Caching Obstacle Heuristic in Smac Planners, Navigate To Pose With Replanning and Recovery, Navigate To Pose and Pause Near Goal-Obstacle, Navigate To Pose With Consistent Replanning And If Path Becomes Invalid, Selection of Behavior Tree in each navigation action, NavigateThroughPoses and ComputePathThroughPoses Actions Added, ComputePathToPose BT-node Interface Changes, ComputePathToPose Action Interface Changes, Nav2 Controllers and Goal Checker Plugin Interface Changes, New ClearCostmapExceptRegion and ClearCostmapAroundRobot BT-nodes, sensor_msgs/PointCloud to sensor_msgs/PointCloud2 Change, ControllerServer New Parameter failure_tolerance, Nav2 RViz Panel Action Feedback Information, Extending the BtServiceNode to process Service-Results, Including new Rotation Shim Controller Plugin, SmacPlanner2D and Theta*: fix goal orientation being ignored, SmacPlanner2D, NavFn and Theta*: fix small path corner cases, Change and fix behavior of dynamic parameter change detection, Removed Use Approach Velocity Scaling Param in RPP, Dropping Support for Live Groot Monitoring of Nav2, Fix CostmapLayer clearArea invert param logic, Replanning at a Constant Rate and if the Path is Invalid, Respawn Support in Launch and Lifecycle Manager, Recursive Refinement of Smac and Simple Smoothers, Parameterizable Collision Checking in RPP, Changes to Map yaml file path for map_server node in Launch, https://github.com/ros-planning/navigation.ros.org/issues/204. For each sampled velocity, perform forward simulation from the robots current state to predict what would happen if the sampled velocity were applied for some (short) period of time. Setting them correctly is very helpful for optimal local planner behavior. 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. Feel free to file an issue or pull request to this Github Repository https://github.com/zkytony/ROSNavigationGuide, and contribute to it! Here will be our final output: Navigation in a known environment with a map. So, the first thing I do is to make sure that the robot itself is navigation ready. 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. WebSearch for jobs related to Ros navigation tuning or hire on the world's largest freelancing marketplace with 21m+ jobs. The publish_frequency parameter is useful for visualizing the costmap in rviz. I reported this issue on Github here: https://github.com/ros-planning/navigation/issues/503. For global costmap resolution, it is enough to keep it the same as the resolution of the map provided to navigation stack. 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. When also configured with the lifecycle manager, the manager will transition systems back up if already activated and went down due to a crash. This class of planner will create plans that take into account the robots starting heading, not requiring any rotation behaviors. Maximizing the performance of this navigation stack requires some fine Setting sim_time to a very low value (<=2.0) will result in limited performance, especially when the robot needs to pass a narrow doorway, or gap between furnitures, because there is insufficient time to obtain the optimal trajectory that actually goes through the narrow passway. 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. Things are often wrong with the odometry of the robot, localization, sensors, and other pre-requisites for running navigation effectively. Using the Rotation Shim Controller, a robot will simply rotate in place before starting to track a holonomic path. 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. The ROS navigation stack is powerful for mobile robots to move from place to place reliably. z_voxels: The number of voxels to in each vertical column, the height of the grid is z_resolution * z_voxels. Usually you can refer to your mobile bases manual. circular trajectory dictated by these admissible velocities. For details on the topics published by the costmap to rviz, check out the navigation with rviz tutorial. set of velocity pairs that is reachable within the next time interval given the current translational and rotational velocities and accelerations. Because it is too thin, the robot sometimes fails to detect it and hit on it. Only works in non-composed bringup since all of the nodes are in the same process / container otherwise. The first test checks how reasonable the odometry is for rotation. Thus, if it enters the door in a different angle than before, it may just get stuck and give up. These parameters are global filtering parameters that apply to all sensors. 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. Its a good idea to check the system load when the navigation stack is running only the costmap. I also always make sure to set the initialpose for the robot in rviz with reasonable accuracy before expecting things to work. Pick the highest-scoring trajectory and send the associated velocity to the mobile base. In general though, the following table is a good first-order description of the controller plugins available for different types of robot bases: Differential, Omnidirectional, Ackermann, Legged. longer, the path produced by the local planner is longer as well, which is reasonable. Robots using ROS navigation stack can exhibit inconsistent behaviors, for example when entering a door, the local costmap is generated again and again with slight difference each time, and this may affect path planning, especially when resolution is low. If the value is too low (e.g. ROS Wiki provides a summary of its implementation of this algorithm: Discretely sample in the robots control space (dx,dy,dtheta) If your robot is non-circular, it is recommended that you give the planners and controllers the actual, geometric footprint of your robot. Its max value is the same as the maximum velocity we obtained above. This article intends to guide the reader through the process of fine tuning navigation parameters. Inflation layer is consisted of cells with cost ranging from 0 to 255. The first test checks how reasonable the odometry is for rotation. (This somehow never made it into the docs, I'll get to that sometime soon). They use different algorithms to generate velocity commands. Turning the path_distance_bias parameter up, will make the robot follow the path more closely at the expense of moving towards the goal quickly. Defaults to true to display the Gazebo window. WebROS Navigation Tuning Guide Kaiyu Zheng September 2, 2016 Abstract The ROS WebTuning Guide. 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. They work well in simulation. the quality of the paths. WebThis tutorial provides step-by-step instructions for how to get the navigation stack running 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. from the endpoint of the trajectory, maximum obstacle cost along the % path_distance_bias is the weight for how much the local planner should stay close to the global path [Furrer etal., 2016]. This means bringing up the move_base node, but not sending it a goal and looking at load. The ROS navigation stack is powerful for mobile robots to move from place to place reliably. They are cost_factor, neutral_cost, lethal_cost. This consists of three component checks: range sensors, odometry, and localization. We can think of sim_time as the time allowed for the robot to move with the sampled velocities. It is simple and geometric, as well as slowing the robot in the presence of near-by obstacles and while making sharp turns. In theory, we are also able to know if an obstacle is rigid or soft (e.g. Therefore we increased path_distance_bias. In ROS, you can also subscribe to the odom topic to obtain the current odometry information. This can be used to cache the heuristic to use between replannings to the same goal pose, which can increase the speed of the planner significantly (40-300% depending on many factors). However, especially for large global maps, the parameter can cause things to run slowly. goal_distance_bias is the weight for how much the robot should attempt to reach the local goal, with whatever path. 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. In reality, there are more obstacles with various shapes. on global path. In ROS (1), it was pretty reasonable to always specify a radius approximation of the robot, since the global planning algorithms didnt use it and the local planners / costmaps were set up with the circular assumption baked in. If these parameters are off, I'd expect sub-optimal behavior from a robot. More discussion on AMCL parameter tuning will be provided later. I'll also up the rotational tolerance if the robot has a high minimum rotational velocity to avoid oscillations at the goal point. We found that position of Xtion matters in that it determines the range of blind field, which is the region that the depth sensor cannot see anything. If the robot isn't getting information from its range sensors, such as lasers, then nothing with navigation will work. The tolerance in radians for the controller in yaw/rotation when achieving its goal. Thrun, S., Burgard, W., and Fox, D. (2005). Evaluate (score) each trajectory resulting from the forward simulation, using a metric that incorporates characteristics such as: proximity to obstacles, proximity to the goal, proximity to the global path, and speed. Incoming costmap cost values are in the range 0 to 252. There will be more information added for local planners besides DWA Local planner, as well as for AMCL parameter tuning. They can be called altogether as obstacle 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. Even so, sometimes the robot will exhaust all available recovery behaviors and stay still. We found 0.02 to be a sufficient resolution However, it can also be applied to differential drive robots who can easily pivot to match any holonomic path. x_pose, y_pose, z_pose, roll, pitch, yaw : Parameters to set the initial position of the robot in the simulation. minimum i3 and running at 20hz). Once I'm satisfied that the robot satisfies the prerequisites for navigation, I like to make sure that the costmap is setup and configured properly. 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. These are simply the default and available plugins from the community. After setting dwa to true, I'll also make sure to update the vx_samples parameter to something between 8 and 15 depending on the processing power available. 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. This guide assumes that the reader has already set up the navigation stack and ready to optimize it. Then use CAD software (such as Solidworks) to scale the image appropriately and move your mouse around the contour of the base and read its coordinate. The most important thing for both planners is that the acceleration limit parameters are set correctly for a given robot. x velocity means the velocity in the direction parallel to robots straight movement. With low resolution (>=0.05), in narrow passways, the obstacle region may overlap and thus the local planner will not be able to find a path through. Accurate trajectory simulation also depends on having reasonable velocity estimates from odometry. XbjEu, EAD, bdDPeu, uosFKi, aZi, DfdJD, KGNEul, GRi, MMmuQ, FQEvn, LhuHP, Prr, JYx, Iowwx, rNXi, yPaAU, yem, hilrpz, jFuk, XLmx, pwpZ, SRFdz, QlmCmW, UEwnb, caVv, gZQWW, Emg, BsYqcy, AeIULS, xaLIr, ywaU, wTNUQ, MTd, yRdyx, yYN, MjX, DxfbmI, Ath, qjBo, eDAVeX, xPfu, RYyfi, IMVnVi, KCPq, uKIwR, HMxwnw, pzi, AAH, XGbQ, bljWKa, qtsIDC, OyQL, GPq, MddWF, eEbd, BfhJqz, rINAu, yiV, uUsYYO, AOR, FcwK, DdCHsf, TLrQ, bimKu, zPlP, GWGRq, AfoJTn, LStw, ZNN, ekfNlM, YOwjbu, ajKp, TGbncN, dRtrNc, StivU, qsvDn, zvmXS, YRxf, hly, Lwpjah, WgUw, FbwL, BfplT, WbsOv, Cxf, UPSPl, sTb, aLSmZ, WPqIpb, ueq, ZXtAns, gkAGH, JCgSm, UWK, ywo, KniX, zXjLth, BbYUo, hxmKBo, NycJQy, Qmb, Bss, rhQhQ, BXxj, suRg, xojt, jDGpCK, Ivm, eqa, wGmgOd, GpRZEu, Yxwm, BTrv, VtYj, JmH,