IEEE Robotics & Automation Magazine - September 2018 - 55

memory

memory

Ball

ll

ball_detection

following
look_at

Ball

walk_to

walk_to

look_at

Control

ball_detection

Ba

Middleware

(a)

Figure 3. The flows of information are monitored during runtime
to implement sensory-motor couplings, here for having the
robot tracking the ball.

memory

In turn, following corresponds to looking at the ball
while walking toward it (as long as the distance between the
ball and the robot is significant, as expressed by the evaluation
implementing the Python function far):

ball_detection
walk_to
head_search
(b)

following:
looking
walk_to, whenever far

memory

and looking at the ball corresponds to either centering
the detected ball in the center of vision ( look_at )
or performing a rapid head-search motion ( h e a d _
search ):

ball_detection
turning head_search
(c)
memory

sit

(d)
Figure 2. Some possible patterns of activation of the tree
presented in Figure 1 where the robot is (a) walking toward the
ball when it sees it; (b) walking toward the last known position
of the ball while searching for it with its head; (c) performing
a full body search; and (d) sitting in a safe position when its
battery runs low. Black is used to indicate activation of nodes
and blue activation of leaf nodes. For brevity, only activated leaf
nodes are labeled.

looking:
look_at, whenever time_ago <1,
priority of 2
head_search, priority of 1

It can be noted that the Python code corresponding to leaf
nodes and evaluations can be reused not only across programs but also several times in the same script. For example,
the head_search node is used in two branches: looking
and searching.
The full behavior is robust, as the robot either 1) walks
toward the ball when it sees it, 2) keeps walking toward the
last known position of the ball while searching for it with its
head, 3) stops walking and performs a full body search, or 4)
sits in a safe position if at any time its battery runs low. Related patterns of activation in the behavior tree are displayed in
Figure 2.
Reactivity
The keyword targeting relates sensory information
corresponding to the detected ball to the following
node. Figure 3 shows the resulting flows of information that are applied at runtime when the following
subtree is activated. Information encoded by the
september 2018

*

IEEE ROBOTICS & AUTOMATION MAGAZINE

*

55



IEEE Robotics & Automation Magazine - September 2018

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - September 2018

Contents
IEEE Robotics & Automation Magazine - September 2018 - Cover1
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