IEEE Robotics & Automation Magazine - June 2020 - 160

each robot separately; in the other, the operator provided
general guidance to the swarm, which self-organized via a
combination of distributed decision making and consensus
building. We assessed cognitive load via pupillometry for each
condition and perceived task demand and intuitiveness via selfreport. Our results show that implementing higher autonomy
with swarm intelligence can reduce workload, freeing the
operator for other tasks such as overseeing strategy and communication. Future work will further leverage advances in swarm
intelligence for exploration missions.
Multirobot Teams
Extraterrestrial exploration missions are increasingly directed
toward challenging landscapes and environments, such as
mountains, craters, lava tubes, and oceans [1]-[4]. Rovers
have been the most common vehicle choice for planetary
exploration; however, they are designed to operate on relatively flat land [5]. As a result, other types of robots, such as UAVs
and hydrobots, are being proposed for more challenging environments and geographic features, including the atmospheres
and oceans of celestial bodies [6]. UAVs offer advantages over
rovers for exploration where atmospheres are present: they
provide higher resolution data than orbiters [7], have greater
range and mobility [6], and can sample gases at different altitudes, thus also filling a planetary measurement gap [8].
Multirobot teams, perhaps with mixed capacities (that is,
activators, sensors, and communication devices) could also be
used to explore larger areas more effectively than single robots
and could characterize and identify potential landing sites
for manned missions as well as reveal hazardous areas. With
an appropriate interface, a robotic team could conduct

autonomous reconnaissance [9] and increase human situational awareness of mission-critical information. However,
there are substantial technical and human challenges to organizing and controlling multirobot systems.
The ESA recently invited our team (Figure 1) to run an
experiment on the use of a multirobot aerial system for planetary exploration as part of their PANGAEA-X exercise, a test
campaign that brings together astronauts, scientists, engineers,
and operations experts for advancing integrated human and
robotics missions. Participants, including a European astronaut, controlled a heterogeneous fleet of four to six UAVs.
Our first objective was to demonstrate the physical deployment of the UAV team. Long distances generate communication latencies and impose low bandwidth, so we rely on
decentralized control for our robots. All UAVs are replaceable
by any others, improving the robustness of the overall system
to individual robot failures. Our approach is suitable for gathering aerial images and providing operators with a fleet-wide
communication link over kilometers in challenging realworld conditions. These characteristics are made possible by
the combination of several of our core contributions to swarm
robotics and to a novel approach of human-swarm interaction (HSI): our system sees the operator as just another robot.
The PANGAEA-X context presented a rare occasion to
measure human behavior in an operational environment.
Task performance and risk-taking behavior [10] differ in the
field as compared with simulated robotic tasks, likely
because real situations are more engaging and potentially
stressful. Our second objective, therefore, was to study the
human operator as he or she guides the swarm. We addressed
two questions:

Figure 1. The PANGAEA-X field deployment team in Lanzarote, Spain: five engineers, a neuroscientist, five DJI Matrice 100 drones, and
five Pleiades Spiri robots. The volcanic landscape of Lanzarote is similar to the surface of the moon. (From left): Marcel Kaufmann,
Benjamin Ramtoula, David St-Onge, Giovanni Beltrame, Emily B.J. Coffey, and Yanjun Cao. (Source: ESA; used with permission.)

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JUNE 2020

IEEE Robotics & Automation Magazine - June 2020

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