The design and deployment of decentralized systems can benefit from self-organization
as it introduces key features, such as resilience, scalability, and adaptivity to
dynamic environments. However, whenever self-organization was demonstrated on physical
platforms (e.g., robot swarms), this was performed mostly within controlled laboratory
conditions. The real world comes with severe requirements, calling for robust design
methodologies, their standardization, and validation via benchmarking toolsets. With
this Research Topic, we collect, benchmark, and survey novel approaches to push self-organization
toward real-world applications, focusing on embodied artificial systems, such as multi-robot,
cyber-physical, and socio-technical systems.
We start with six perspective and survey papers that give a good overview of the state
of the art and challenges of real-world implementations.
Gershenson studies the complexity of cyber-physical systems. After reviewing basic
concepts that are useful to design self-organizing systems, he introduces approaches
to implement self-organization in cyber-physical systems. Gershenson reviews three
case studies from different domains. Crowd control is related to a passive control
approach using signs to mediate passenger boarding and descent in Mexico City Metro.
In a traffic light case study, traffic lights and vehicles interact closely as agents,
resulting in a network of streets and crossings with self-organized coordination of
traffic flows. The third case study is related to public transport and addresses the
equal headway instability. Trains use bio-inspired pheromone systems to keep equal
distance to the vehicles in front and behind. The result is a flexible system where
trains can quickly adapt and respond to service delays. Gershenson provides an outlook
for cyber-physical and cyber-social systems controlled by guided self-organization.
Based on the above-mentioned benefits of self-organization the motivation is strong
to apply swarm robotics in industrial applications. However, many industrial applications
still rely on centralized control. In cases where a multi-robot solution is employed,
the main idea of swarm robotics of distributed decision-making is often not implemented.
Schranz et al. provide a collection and categorization of swarm robotic behaviors.
The paper gives a comprehensive overview of research platforms and industrial projects
and products, separated into terrestrial, aerial, aquatic, and outer space. The authors
identify several open issues including dependability, emergent characteristics, security
and safety, and communication as hindrances for the implementation of fully distributed
autonomous swarm systems.
To deploy swarm robots to the physical realm, one requirement is the ability to cope
with environments that lack human infrastructures. Two key mechanisms, namely cognition
and sensing, have to take place “on-board” on the robot and should not be offloaded
to external devices. Physical mobile robots that operate on land do have the required
hardware capabilities for onboard computation and sensing, and have successfully been
used to demonstrate basic collective behaviors and to a more limited extent been used
in real applications. However, Coppola et al. convincingly argue that swarm robotics
approaches so far cannot be applied to Micro Aerial Vehicles (MAVs). The most impressive
MAVs demonstrations have been executed requiring external computation, sensing, or
both. The main challenge is related to local sensing, which they divide into the following
sub-challenges: MAV hardware design, ego-state estimation, intra-swarm relative sensing,
and swarm behaviors. This paper presents how advanced we are in terms of autonomy
of swarms of MAVs, and presents a roadmap to overcome the challenges in the near future.
One of the main challenges for the design of self-organizing systems is the gap between
the rules followed by individual system components and the desired collective behavior
of the system as a whole. Especially for practical application scenarios, it is difficult
to conceive and optimize the system behavior by acting at the level of the individual
rules. The paper by Birattari et al. champions a methodology that optimizes the system
behavior offline (e.g., in simulation) and that ensures sufficient performance when
deployed in the real world. The central aspect is the “class of interest” of the problems
to be addressed. Every new problem instance is sampled from the same class of interest
(e.g., gardening with robot swarms), and the solution is optimized to maximize performance,
according to relevant metrics defined for the given class. It is within the same class
of interest that the offline automatic design approach gives its best results, and
the manifesto highlights the most important questions that should drive future research
in this area.
The following eight papers study concepts, methods, hardware designs, and natural
systems with high potential to support future real-world applications of self-organizing
systems.
There have been many contributions using either simulation or relatively simple robots,
often in controlled environments of limited size. Tarapore et al. question the very
definition of swarm robotics by focusing on the question of how sparse is a robot
swarm for a realistic task. Tarapore et al. argue that real swarm robotics applications
will need to be addressed, and they introduce the idea of “sparse swarm robotics”:
robots are spread over the environment such that the opportunity for communication
must be explicitly addressed, as opposed to being naturally forced in smaller environment
where density is high. They propose a clean and straight-forward formalization of
this problem in mathematical terms. Also, they illustrate the concept of sparse swarm
robotics by describing several realistic problems and their implications, including
a step-by-step description of the specific issues that arise for one such problem.
Considering a monitoring task for soil sampling in a forest, they discuss both low-level
hardware issues and high-level communication/coordination issues.
A particular threat for real-world robot swarms is a possible attack by malicious
agents that could be introduced into the swarm. The paper by Strobel et al. makes
a significant contribution toward the use of swarm robotics in the real world by presenting
a framework for a secure decentralized database. The presented framework uses smart
contracts, a way to decentrally execute programs based on an Ethereum blockchain.
Individual malicious robots aim to disrupt the collective decision-making process
of a simulated swarm of e-puck robots by spreading misinformation. The robot swarm
successfully disregards the wrong information. The authors indicate that blockchain
networks can be used for robot swarms, and the low processing and memory capacity
of swarm robots does not prohibit the use of blockchains in real-world scenarios.
When developing the swarm robot controller and hardware, it is difficult to anticipate
all future situations that this robot swarm may experience. Hunt claims that nature
provides an example solution that we can follow: phenotypic plasticity. The idea is
to train robot swarms in (simulated) heterogeneous environments, for example, using
methods of evolutionary computation. The general swarm robot design should allow for
flexibility such that they can be adapted and shaped ideally in three dimensions:
behavioral, physiological, and morphological plasticity. Behavioral plasticity of
the swarm members introduces diversity that can be exploited, for example, to increase
fault tolerance and decision accuracy. Physiological plasticity in robots could be
modes of operation that have different energy consumption. Morphological plasticity
could be known implementations of self-assembling swarm robots. In summary, Hunt opens
a door to more flexible and dynamic ways of drafting, developing, and optimizing robot
swarms for the real world mainly based on a systematic behavioral and morphological
diversity.
Rausch et al. propose an empirical case study of the impact of network topology over
the spread of information in a robot swarm. Specifically, they consider the possible
benefits of scale-free communication topology. They experimentally show that there
is actually a trade-off in using scale-free (rather than random) topology: information
spreads faster, enabling quicker reactions to changes in dynamic environments, but
at the cost of a decreased stability as the emergence of consensus is hindered by
communication pathways of different lengths.
To ensure a smooth transition from lab to market, it is necessary to recognize user
needs and to evaluate the acceptability of robot swarms. The paper by Carrilo-Zapata
et al. conducts a study against three application domains wherein robot swarms are
considered as game changing tools. The mutual shaping methodology proposed entails
a bi-directional knowledge exchange between swarm designers and final users, raising
awareness of the possibility offered by the technology but also allowing to collect
important design and interaction features that can drive the deployment. Overall,
the study reveals that robot swarms can play an important role within the considered
application domains, above all when they work in support of human operations, rather
than as entire replacements.
Another important hurdle to deploying swarms in the physical realm is robustness.
Contrary to the adage “there is safety in numbers,” robustness is not an inherent
benefit of robot swarms that results from redundancy. Robustness is a challenging
design goal, made complex by the interplay between the benefits of redundancy and
the need for scalability. Wilson et al. argue that achieving robustness through redundancy
involves a careful co-design of hardware, fabrication processes, and control software.
To investigate this idea, the authors present an approach to achieving robustness
that involves a novel hardware-software co-design of a modular robotic platform called
“DONUts” (Deformable Self-Organizing Nomadic Units). The modules are inexpensive,
flexible printed circuit boards, and designed to move as a collective through magnetic
interaction. Wilson et al. study several control strategies that explore the design
space of inter-module connectivity to shed light on the interplay between robustness,
scalability, and controllability.
Nave et al. investigate on a biological model related to social insects—the tower
building behavior of red imported fire ants. Results show that individuals moving
under the influence of local attraction can form large towers. The system shows a
sudden density-dependent phase transition as the attraction parameter is varied. The
resulting towers of simulated agents are constantly rebuilt and move over time—a feature
that has to be considered for robotic applications. There is for future robotic studies,
where robots build towers out of themselves in a manner similar as the fire ants.
In a real-world application, a tower of robots could be useful for seeing over obstacles,
providing scaffolding for climbing, or marking a location of interest. Robotic tower-builders
would need capabilities for sensing neighbors, climbing onto and off one another,
and supporting appropriate loads. Building such robotic tower-builders would be an
interesting step for future robotics research.
While engineers take robots to the real world to automate tasks currently done by
humans or impossible for humans, biologists take robots to the real world to study
animal behavior. Yang et al. study a robotics-based experimental test paradigm where
a robotic replica is used to influence the behavior of Zebrafish. Two setups were
studied. In the individual training condition, a single fish learned to open the correct
of two doors by itself. In the social training condition, a fish observes the replica
approaching both doors with the correct one opening after a certain period of time.
Main contributions are the technical innovation of this robot-supported experiment
and the negative result indicating that there is no improvement by social learning.
Yang et al. claim that their setup can generalize to other species, such as guppies
and mollies but also insects, mammals, and even invertebrates. It seems promising
that with ongoing technological progress we will see more of these bio-hybrid systems
with robots and animals interacting closely in the real world.
In summary, all the above papers that study an engineering approach to take self-organizing
robots to the field, struggle with a technological bottleneck: local sensing, coordinated
actuation, and means of communication that work reliably in field environments. This
is a common challenge of robotics and will require designing smart control algorithms
with minimal requirements for sensing, actuation, and communication. Common to all
papers in this Research Topic are deviations between model abstractions and the physical
realm. We still do not know well-enough what deviations are caused by which abstraction
in swarm and multi-robot models and simulations. The intrinsic stochastic nature of
self-organizing systems adds to this challenge. In future work, this will require
an effort toward more robust hardware, as well as verifiable swarm and robot behaviors
to achieve certification. Our Research Topic covers a wide range of fields, concepts,
and methods that will hopefully help to kick our robots out of the lab, pushing toward
a novel “field swarm robotics,” to establish cyber-physical systems in the wild and
to design distributed systems for radically novel applications using self-organization
in the physical realm.
Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to
the work, and approved it for publication.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.