Timed Elastic Band (TEB) locally optimizes the robot's trajectory with respect to execution time, distance from obstacles and kinodynamic constraints at runtime.
teb_local_planner for navigationThis guide is for you if you choose to use teb_local_planner to navigate your robots. For more information, click here.
Filepath: catkin_ws/movel_ai/config/movel/config/
File to modify: base_local_planner_params.yaml
Note that config files for local planner is located in the movel package. In the yaml file itself, you see there are actually 3 local planners included. But we will only use one of them. Uncomment the entire long section under TebLocalPlannerROS.
One good practice will be to include only things you need (uncomment if previously commented) and comment out the rest.
These are the parameters that can be configured. Will explain how to tune these parameters later.
Warn: yaml forbids tabs. Do not use tabs to indent when editing yaml files. Also, check your indentations align properly.
After configuring Seirios to use teb planner, you need to sync the velocity with maximum velocity specified by teb planner.
Filepath: catkin_ws/movel_ai/config/velocity_setter/config/
File to modify: velocity_setter.yaml
Check local_planner match the name of the planner in the base local planner configuration. You are configuring the maximum velocities to be the maximum velocities you specified in teb planner.
rviz: Use the 2D Pose Estimator tool to pinpoint the location of your robot on the map. You can use LaserScan to help you – increase the Size (m) to at least 0.05 and try to match the lines from the LaserScan as closely as possible with the map.
Seirios: Go to Localize. Use either your keyboard or the joystick button to align the laser with the map as closely as possible.
Filepath: catkin_ws/movel_ai/config/movel/config/amcl.yaml
File to modify: amcl.yaml
Amcl configuration file is in the same directory as base_local_planner_params.yaml.
Similarly, there are a lot of paramters in the amcl file although we are only interested in these 2 parameters:
Configure min_particles and max_particles to adjust the precision.
You can increase the values if you want a more precise localization.
However, tune the values with respects to the size of your map. Too many particles on a small map means there are redundant particles which only wastes computing power.
Click here for more information.
teb_local_planner parametersThis section provides some suggestions on what values to give to the long list of parameters for TebLocalPlannerROS. Go back to the same base_local_planner_params.yaml file.
TEB requires a lot of tuning to get it to behave the way you want. Probably a lot of it is through trial and error.
For all kinds of robot tuning, always refer to the robot manufacturer’s configs. Whatever params you set must be within what that is handleable by your robot.
General tip: Make changes to only one/a few parameters at a time to observe how the robot behaves.
Tuneable parameters can be grouped into the following categories
Robot
Goal Tolerance
Trajectory
Obstacles
Optimisation
Tune robot related params with respects to the configurations specified by the manufacturer. i.e. The values set for velocity and acceleration should not exceed the robot's hardware limitations.
Kinematics
vel parameters limits how fast the robot can move. acc parameters limits how fast robot can accelerate. _x specifies linear kinematics whereas _theta specifies angular kinematics.
Footprint Model
You must indicate the type of the robot footprint. Different types include polygon, circular, two_circles, line, point etc. For simplicity sake, footprints are usually circular or polygon. Click here for more footprint information.
radius is required for type: "circular"
vertices is required for type: "polygon"
Specifies how much deviation from the goal point that you are willing to tolerate.
xy_goal_tolerance is the acceptable linear distance away from the goal, in meters.
Should not set xy value too high, else the robot will stop at a very different location. However, some leeway is required to account for robot drift etc.
yaw_goal_tolerance is the deviation in the robot's orientation. i.e. Goal specifies that the robot should face directly in front of the wall but in actual, the robot faces slightly to the left.
Should not give a too tight yaw tolerance, else the robot could be jerking around just to get the orientation right. Which might not be ideal in terms of efficiency.
Decides how the robot should behave in front of obstacles.
Experimentation is required to tune the planner to approach obstacles optimally. A riskier configuration will allow the robot to move in obstacle ridden paths i.e. narrow corridors but it might get itself stuck around obstacles or bump into obstacles. A more conservative configuration might cause the robot to rule out its only available path because it thinks its too close to obstacles.
The tricky part is really to achieve a balance between those two scenarios.
min_obstacle_dist is the minimum distance you want to maintain away from obstacles.
inflation_dist is the buffer zone to add around obstacles.
Click here for more information about obstacle avoidance and penalty. And click here for known problems with regards to obstacle avoidance tuning.
Besides configuring obstacle handling behaviours in local planner, we can also configure the costmaps.
A costmap tells a robot how much does it cost to move the robot to a particular point. The higher the cost, the more the robot shouldn’t go there. Lethal obstacles that could damage the robot will have super high cost.
Filepath: catkin_ws/movel_ai/config/movel/config/
File to modify: costmap_common_params.yaml
File is in the same directory as base_local_planner_params.
There are several layers added into the costmap. Usually we follow the specifications here.
footprintmust match the measurements specified in base_local_planner_params.yaml.
You can choose to add some or all of the layers in the common_costmap_params into global_costmap_params and local_costmap_params.
For the costmap layers that you have decided to use, you must mount the layers into global_costmap_params.yaml and local_costmap_params.yaml as plugins. Click here for more information on the difference between global and local costmaps.
The most important thing in this section is to get the robot's footprint right.
Tip: Tune the teb_local_planner params first. Modify the costmap only if you need more fine tuning after that.
This layer inflates the margin around lethal obstacles and specifies how much bigger you want to inflate the obstacles by.
inflation_radius is the radius, in meters, to which the map inflates obstacle cost value. Usually it is the width of the bot, plus some extra space.
cost_scaling_factors is the scaling factor to apply to cost values during inflation.
Additional information to configure it correctly (directly lifted from here)
The
inflation_radiusis actually the radius to which the cost scaling function is applied, not a parameter of the cost scaling function. Inside the inflation radius, the cost scaling function is applied, but outside the inflation radius, the cost of a cell is not inflated using the cost function.You'll have to make sure to set the inflation radius large enough that it includes the distance you need the cost function to be applied out to, as anything outside the inflation_radius will not have the cost function applied.
For the correct
cost_scaling_factor, solve the equation there ( exp(-1.0 * cost_scaling_factor * (distance_from_obstacle - inscribed_radius)) * (costmap_2d::INSCRIBED_INFLATED_OBSTACLE - 1)), using your distance from obstacle and the cost value you want that cell to have.
Ideally, we want to set these two parameters such that the inflation layer almost covers the corridors. And the robot is moving in the center between the obstacles. (See figure below)
The obstacle layer marks out obstacles on the costmap. It tracks the obstacles as registered by sensor data.
For 2D mapping, laser_scan_sensor must be selected.
obstacle_range is the maximum distance from the robot that an obstacle will be inserted into the costmap. A value of 10 means the costmap will mark out obstacles that are within 10 meters from the robot.
raytrace_range is the range in meters at which to raytrace out obstacles. The value must be set with respects to your sensors.
max_obstacle_height is the maximum height of obstacles to be added to the costmap. Increase this number if you have very tall obstacles. The value must be set with respects to your sensors.
Voxel layer parameters
Voxels are 3D cubes that has a relative position in space. Can be used for 3D reconstruction with depth cameras. Ignore the parameters below if you are not using 3D. More information here.
origin_z is the z-origin of the map (meters)
z_resolution is the height of the cube
z_resolutioncontrols how dense the voxels is on the z-axis. If it is higher, the voxel layers are denser. If the value is too low (e.g. 0.01), you won’t get useful costmap information. If you set z resolution to a higher value, your intention should be to obtain obstacles better, therefore you need to increasez_voxelsparameter which controls how many voxels in each vertical column. 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.
z_voxels is the number of voxels
Low obstacle layer is obstacle layer, but with additional parameters. Change your observation_sources to the topic that you want to take in data from.
Use obs_cloud if you want a PointCloud (3D) or mock_scan if you want a LaserScan (2D). Or you can specify your own method.
There are only two important parameters to note.
min_obstacle_height is the height of the obstacle below the base of the robot. Examples includes stairs. Specify this param so that the robot can detect obstacles below its base link and prevent it from falling into the pit...
max_obstacle_height is the maximum height of obstacle. It is usually specified as the height of the robot.
You WILL definitely be encountering some of the issues. Because param tuning requires experimentation, it is suggested you test out your values in the console first before modifying the yaml files.
rosrun rqt_reconfigure rqt_reconfigure
List of known issues and possible fixes is not exhaustive. There will be many more issues yet to be encountered when extensively using the robot. Thus it is good practice to keep a list of problems and solutions so you know how to deal with the problem should it come up again.
Issue #1: Problems getting the robot to go to the other side of the narrow path.
It is noted from observations that the robot will be returning goal failure, or that robot will get itself uncomfortably close to walls and get stuck there.
Issue #2: Problems getting the robot to turn 90 degrees.
The planner somehow disregards the wall and direct the robot to go through it instead of making a 90 degree turn around the wall. Though it seems most robots are smart enough to recognize they cannot go through walls and will either abort the goal or get stuck trying.
Solution #1: Reducing the velocity and acceleration. The intuition is that by slowing down the movement and rotation of the robot, the planner might have more time to react to the obstacle and plan accordingly.
Solution #2: (For robots that refuse to go to the other side of the corridor.)
Shrink the inflation_radius in both costmaps so that it does not cover the corridor. (Right figure) Noted with observation that if the radius covers the entire corridor, robot may refuse to move.
Check that the robot's footprint size is correct and check that the costmaps aren't blocking the paths.
External resources if you need more information about planners and tuning planners.
Literally a footprint. Ideally, configure the footprint to be slightly bigger than the actual measurement of the robot. footprint_model should be configured with respects to the robot's measurements.
