See also

The FAQ from the py_trees package.

Parameter/Remap Proliferation

You can imagine once you have 50+ re-usable behaviours in a tree for move_base, odometry, … that the need for remaps in the behaviour tree launcher will become exceedingly large. In these situations it is more convenient to provide a single point of call for behaviour tree configuration. Load all your remappings into a single namespace on the ROS parameter server and apply them as you instantiate behaviours. A typical tree namespace on the ROS parameter server might look something like:

/tree/topics/odom                 /gopher/odom
/tree/topics/pose                 /gopher/pose
/tree/services/get_global_costmap /move_base/global/get_costmap
/tree/dyn_reconf/max_speed        /trajectory_controller/max_speed

With an example instantiation of a move base client behaviour:

odometry_topic=rospy.get_param("~topics/odom", "/odom")
pose_topic=rospy.get_param("~topics/pose", "/pose")
move_base = some_navi_package.MoveBaseClient(odometry_topic, pose_topic)

Do I need to Continuously Tick-Tock?

Not at all - you can set your own pace. This can be useful if you wish to tick only when an external trigger is received (a common trick to minimise cpu usage in games).

rate = rospy.Rate(10)
while True:
    if some_external_trigger:

or even better, apply conditions to block the tick.

Control-Level Decision Making

Our first use case never intended the behaviour trees for use by control level applications. So it was surprising when the control engineers started requesting to use the behaviour trees for handling the state changes in docking, elevator entry and extensions/replacements for finishing and recovery in place of move base (not that none of these require low-latency reactivity).

In hindsight, this makes good sense. Prior to the behaviour trees there was a different state machine implementation in each controller - having just one decision making engine with shared code and design patterns is more efficient. It also drives the design of the controllers to be more granular, which in turn, permits more freedom for customisation of the scenario at the behaviour tree level.