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Please be advised that the code in this tutorial is currently in beta and is under active development.

Autodocking with Nav Stack

Wouldn't it be awesome if after a hard day's work, Stretch would just go charge itself without you having to worry about it? In this tutorial we will explore an experimental code that allows Stretch to locate a charging station and charge itself autonomously. This demo will build on top of some of the tutorials that we have explored earlier like ArUco detection, base teleoperation and using the Nav Stack. Be sure to check them out.

Docking Station

The Stretch Docking Station is a Stretch accessory that allows one to autonomously charge the robot. The top part of the docking station has an ArUco marker number 245 from the 6x6, 250 dictionary. To understand why this is important, refer to this handy guide provided by OpenCV. This marker enables Stretch to accurately locate the docking station in its environment. The docking station also has a Y-shaped recess in its base plate to guide the caster wheel of the robot towards the charging port in case of minor misalignments while backing up. Overall, it's a minimal yet effective way to allow Stretch to charge itself.

Behaviour Trees

Traditionally, high level task planning has been achieved using Finite State Machines or FSMs which break down each functional element of the task into states that have to be traversed in a cyclic manner to accomplish the major task. This approach has recently gone out of vogue in favour of Behavior Trees or BTs. BTs, also known as Directed Acyclic Graphs, have been popularized through their use in the gaming industry to impart complex behaviors to NPCs. BTs organize behaviors in a tree representation where the control flow is achieved not through cyclic state transitions but in a tree traversal fashion. Additionally, conditions and actions form distinct leafs in the tree which results in better modularity. This ensures that augmenting behaviors by way of additional leafs in the tree does not require a restructuring of previous and following leafs but only the final tree graph. This also means that actions in complex behaviors can be developed independently resulting in higher scalability.


We decided to implement this demo using the open-source behvaior trees library called py-trees because of its following features:

  • Open-source
  • Pythonic for quicker adoption and turnaround
  • Well-documented to enable users to self-learn
  • Scalable to work equally well on all levels of our software stack, including ROS 2
  • Well-supported to allow us to continue to build on top for the forseebale future


  1. Since this demo uses the ROS Navigation Stack to navigate to the docking station, it requires a pregenerated map that can be utilized to localize the robot. To know how to generate the map, refer to the Nav Stack tutorial.
  2. To understand the underlying implementation, it is important to review the concept of Behavior Trees. Although this demo does not use some of its more useful features such as a blackboard or tree visualization, a preliminary read on the concept should be sufficient to understand what's happening under the hood.
  3. This demo requires the Behavior Tree library called py-trees to be installed. To do this, execute the following command:
sudo apt install ros-noetic-py-trees-ros ros-noetic-rqt-py-trees

Once you have the above covered, we are ready to setup the demo.

Setup and Launch

The demo requires the docking station to be rested against a wall with the charger connected to the back and the arm to be stowed for safety. It is also necessary for the robot to be close to the origin of the map for the robot to have the correct pose estimate at startup. If not, the pose estimate will have to be supplied manually using the 2D Pose Estimate button in RViz as soon as the demo starts.

Let's stow the arm first:

To launch the demo, execute the following command:

roslaunch stretch_demos autodocking.launch map_yaml:=${HELLO_FLEET_PATH}/maps/<map_name>.yaml

How It Works

Below is a simplified version of the behavior tree we implemented for this demo. Please be advised that the actual implementation has minor variations, but the below image should serve well to understand the control flow. Before moving ahead, we recommend supplementing reading this tutorial with the concept of behaviour trees. Let's dig in!

The root of our tree is the sequence node (right-pointing arrow) below the repeat decorator node (circular arrow). This sequence node succeeds only when all of its children succeed, else it returns a failure.

It has the fallback node (question mark) at the left as its first child. As per rules of tree traversal, this fallback node is the first to be executed. In turn, this fallback nodes has two children - the Predock found? condition node and the Camera scan action node. The Predock found? condition node is a subscriber that waits for the predock pose to be published on a topic called \predock_pose. At the start of the demo, we expect this to fail as the robot does not know where the pose is. This triggers the Camera scan action node which is an action client for the ArucoHeadScan action server that detects the docking station ArUco marker. If this action node succeeds it published the predock pose and the next child of the sequence node is ticked.

The second child of the sequence node is again a fallback node with two children - the At predock? condition node and the Move to predock action node. The At predock? condition node is simply a TF lookup, wrapped in a Behavior Tree class called CheckTF, that checks if the base_link frame is aligned with the predock_pose frame. We expect this to fail initially as the robot needs to travel to the predock pose for this condition to be met. This triggers the Move to predock action node which is an action client for the MoveBase action server from the Nav stack. This action client passes the predock pose as the goal to the robot. If this action succeeds, the robot navigates to the predock pose and the next child of the root node is triggered.

The third child of the root node is the Move to dock action node. This is a simple error-based controller wrapped in a Behavior Tree class called VisualServoing. It's working is explained in the image below. This controller enables the robot to back up and align itself to the docking station in case the navigation stack introduces error in getting to the predock pose.

The fourth and final child of the sequence node is another fallback node with two children - the Charging? condition node and the Move to predock action node with an inverter decorator node (+/- sign). The Charging? condition node is a subscriber that checks if the 'present' attribute of the BatteryState message is True. If the robot has backed up correctly into the docking station and the charger port latched, this node should return SUCCESS and the autodocking would succeed. If not, the robot moves back to the predock pose through the Move to predock action node and tries again.

Code Breakdown

Let's jump into the code to see how things work under the hood. Follow along here (TODO after merge) to have a look at the entire script.

We start off by importing the dependencies. The ones of interest are those relating to py-trees and the various behaviour classes in autodocking.autodocking_behaviours, namely, MoveBaseActionClient, CheckTF and VisualServoing. We also created custom ROS action messages for the ArucoHeadScan action defined in the action directory of stretch_demos package.

import py_trees
import py_trees_ros
import py_trees.console as console
import rospy
import sys
import functools
from autodocking.autodocking_behaviours import MoveBaseActionClient
from autodocking.autodocking_behaviours import CheckTF
from autodocking.autodocking_behaviours import VisualServoing
from stretch_core.msg import ArucoHeadScanAction, ArucoHeadScanGoal
from geometry_msgs.msg import Pose
from sensor_msgs.msg import BatteryState
import hello_helpers.hello_misc as hm

The main class of this script is the AutodockingBT class which is a subclass of HelloNode.

class AutodockingBT(hm.HelloNode):

The create_root() method is where we construct the autodocking behavior tree. As seen in the figure above, the root node of the behavior tree is a sequence node called autodocking_seq_root. This sequence node executes its child nodes sequentially until either all of them succeed or one of them fails. It begins by executing its first child node called dock_found_fb.

The dock_found_fb node is a fallback node which starts executing from the left-most child node and only executes the following child node if the child node preceeding it fails. This is useful for executing recovery behaviors in case a required condition is not met. Similarly, at_predock_fb and charging_fb are also fallback nodes.

    def create_root(self):
        # behaviours

        autodocking_seq_root = py_trees.composites.Sequence("autodocking")
        dock_found_fb = py_trees.composites.Selector("dock_found_fb")
        at_predock_fb = py_trees.composites.Selector("at_predock_fb")
        charging_fb = py_trees.composites.Selector("charging_fb")

The node predock_found_sub is a behavior node which is a child of the dock_found_fb fallback node. This node subscribes to the /predock_pose topic to check for incoming messages. It returns SUCCESS when a predock pose is being published. At the start of the demo, since the robot likely does not have the docking station in its view, no messages are received on this topic. The fallback to this condition would be to scan the area using the head camera. The head_scan_action action node sends a goal to the ArucoHeadScan server to look for the marker number 245 at a camera tilt angle of -0.68 rads through ArucoHeadScanGoal(). If this action returns SUCCESS, we start receiving the predock_pose.

        predock_found_sub = py_trees_ros.subscribers.CheckData(

        aruco_goal = ArucoHeadScanGoal()
        aruco_goal.aruco_id = 245
        aruco_goal.tilt_angle = -0.68
        aruco_goal.publish_to_map = True
        aruco_goal.fill_in_blindspot_with_second_scan = False
        aruco_goal.fast_scan = False
        head_scan_action = py_trees_ros.actions.ActionClient( # Publishes predock pose to /predock_pose topic and tf frame called /predock_pose

Next, we want to move to the predock_pose. We do this by passing the predock pose as a goal to the Move Base action server using the predock_action. This is followed by the dock_action action node which uses a mock visual servoing controller to back up into the docking station. This action uses the predock pose to align the robot to the docking station. Internally, it publishes Twist messages on the /stretch/cmd_vel topic after computing the linear and angular velocities based on the postional and angular errors as defined by the simple controller in the image above.

        predock_action = MoveBaseActionClient(
        invert_predock = py_trees.decorators.SuccessIsFailure(name='invert_predock', child=predock_action)

        dock_action = VisualServoing(

Finally, we define the is_charging_sub behavior node which, like the predock_found_sub, subscribes to the \battery topic and checks for the present attribute of the BatteryState message to turn True. If this behavior node returns SUCCEES, the root node returns SUCCEESS as well.

        is_charging_sub = py_trees_ros.subscribers.CheckData(

Once we have defined the behavior nodes, the behavior tree can be constructed using the add_child() or add_children() methods. The root node is then returned to the caller.

        autodocking_seq_root.add_children([dock_found_fb, at_predock_fb, dock_action, charging_fb])
        dock_found_fb.add_children([predock_found_sub, head_scan_action])
        charging_fb.add_children([is_charging_sub, invert_predock])
        return autodocking_seq_root

The main() method is where the behavior tree is ticked. First, we create an instance of the BehaviorTree class using the root of the tree we created in the create_root() method. The tick_tock() method then ticks the behavior nodes in order until the root either returns a SUCCESS or a FAILURE.

    def main(self):
        Entry point for the demo script.
        hm.HelloNode.main(self, 'autodocking', 'autodocking')

        root = self.create_root()
        self.behaviour_tree = py_trees_ros.trees.BehaviourTree(root)
        rospy.on_shutdown(functools.partial(self.shutdown, self.behaviour_tree))
        if not self.behaviour_tree.setup(timeout=15):
            console.logerror("failed to setup the tree, aborting.")

        def print_tree(tree):
            print(py_trees.display.unicode_tree(root=tree.root, show_status=True))

                # period_ms=500,
                # number_of_iterations=py_trees.trees.CONTINUOUS_TICK_TOCK,
                # pre_tick_handler=None,
                # post_tick_handler=print_tree
        except KeyboardInterrupt:

Results and Expectations

This demo serves as an experimental setup to explore self-charging with Stretch. Please be advised that this code is not expected to work perfectly. Some of the shortcomings of the demo include:

  • The aruco detection fails often and the user might be required to experiment with different locations for the docking station and lighting for better results
  • The controller implementation is not robust to erroneous predock pose supplied by the camera and friction introduced by floor surfaces like carpet
  • The current design of the docking station is minimal and it is recommended that users find ways to stick the station to the floor to prevent it from moving while docking

Users are encouraged to try this demo and submit improvements.