IEEE Robotics & Automation Magazine - June 2020 - 162

stigmergy) and interact with their neighbors. Buzz is highly
portable because it runs within a minimalist virtual machine
that works on most computing systems. It merges bottom-up
behavior development (i.e., assigning tasks to specific robots)
with top-down programs controlling the whole swarm. The
developer using Buzz can implement high-level coordination
algorithms while still considering the specificity of each of the
units in a heterogeneous team, such that the same code can be
deployed on any autonomous robot. The algorithms implemented in Buzz for this experiment were tested in a robot
operating system (ROS) environment with the Gazebo simulator before field deployment. The Buzz virtual machine [14]
and its ROS integration illustrated in Figure 2-including the
script files used in this article-are freely available online [15].

Communication With Neighbors
In critical application scenarios, it can be complicated to maintain a reliable connection to all of the robots in a team. The
multiple challenges may include 1) large areas to cover, 2) limited one-hop communication ranges, and 3) the (human)
command center being potentially located in a remote area for
safety or operational reasons. Although a central control architecture requires a link to each robot, decentralized paradigms
can support many other sparse network topologies. Decentralized control requires only one of the robots to be within
communication range to the ground station to send commands and receive status updates (using Wi-Fi mesh, Xbee,
Zigbee, and so on). The information can then be propagated,
gossip-like, from robot to robot [as shown in Figure 3(d)].
Mission

Team
Communication

Cognitive Load Metrics
(a)

(b)
Pupillometry

Explore the Area and
Reveal Ground Features

TLX Questionnaire

Human Input: Waypoints

Human Input: Deployment Hotspots
(c)

(c)

1

3
2

Convex Region
(e)

Voronoi Tessellation

Local Information Exchange
(d)

(f)

Safeguards
(g)

Geofence

Turned Off

Take Off

Land

Go
Home

Idle

Deploy

Waypoint

State Machine

Figure 3. The experiment overview. (a) The objective and self-assessed metrics of the operator cognitive load. (b) The simulated radio
chatter used to increase operator task load. (c) The operator inputs following the two modes (hotspots for self-deployment and waypoints
for individual control). (d) The decentralized communication infrastructure. (e) The convex region computation on each robot from the
user hotspots input. (f) The location goals computed from the tesselation of the region of interest. (g) The safety features implemented
(geofence set before the mission and a state machine with consensus mechanism over transitions). (Source: Freepik from Flaticon.)

162

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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JUNE 2020



IEEE Robotics & Automation Magazine - June 2020

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