4. Robot Swarms (2)
• Robot swarm operates in a self-organized and
distributed manner
• Swarm collective behavior
– Robot-robot interaction
– Robot-environment interaction
4
5. Swarm robotics (Scope)
• Environment characteristics:
– High risk of individual robots failing or getting lost
– Supporting infrastructures are hard or impossible to set up
– Communication range is limited
• System Properties:
– Fault-tolerance
– Scalability
– Flexibility
5
6. Swarm Robotics vs.
Decentralized Multi-Robot Systems
• [Similarities]
– there is no leader
– coordination is obtained via interaction between the
individual robots
• [Differences]
– Emergent behavior
– Huge tiny robots, limited capabilities, limited resources
6
9. Challenge 2. Uncertainty in robot swarm performance
A. Uncertainty emerging from the design of robot swarms:
- Automatic vs semi-automatic design methods
9
The obtained control software strongly depends on the experience
and intuition of the swarm designer (human-in-the-loop).
10. Challenge 2. Uncertainty in robot swarm performance
B. Uncertainty emerging from simulation.
10
Swarm designers have observed a significant performance drop when a control software
designed in simulation is deployed in the target environment.
This drop in performance when moving from simulation to reality is known as a reality gap.
11. Challenge 2. Uncertainty in robot swarm performance
B. Uncertainty emerging from simulation.
11
Reality gap.
12. Challenge 2. Uncertainty in robot swarm performance
C. Uncertainty emerging from the evaluation process.
12
❖ Swarm designers do not usually compare design methods that create
swarm control software.
❖ Most of the works are feasibility studies: feasibility of algorithms is
evaluated.
❖ There is a lack of established practises for evaluating design methods
through empirical assessment. Establishing experimental protocols and
defining benchmark missions is needed.
13. Challenge 3. Offline vs. online design process
13
The offline design process
happens in a simulation
before the robot swarm is
deployed.
The online
design process
happens
directly on the
real robots.
17. R1. A systematic approach to designing
robot swarms
A. Reference model specification.
➢ Notion of the reference model has been implicitly used without a
clear formal definition of what exactly it represents.
➢ Defining robot capabilities in terms of skills and behaviours.
17
18. R1. A systematic approach to designing
robot swarms
B. Mission model specification:
➢ Most of swarm designers specify the goal that should be achieved without
providing details about the context.
➢ Modeling the swarm mission: we need to consider the context in which the
swarm operates and the objective function that should be optimized.
Software engineering to play an important role in identifying patterns, and classes of swarm
missions!
18
19. R2. SwarmOPS and Digital twins for
robot swarms
.
19
SwarmOps:
❖ Applying DevOps principles to the development and operation of robot swarms.
❖ using automation and continuous integration/continuous deployment (CI/CD)
practices to streamline the process of building, testing, and deploying software
updates to the robots in the swarm.
❖ SwarmOps goal: improve the time-to-market and overall performance of the
robot swarms through agility and automation.
Digital twins of the individual robots:
❖ maintenance scheduling: by using the individual robot twin to update
parameters related to known possible faults and thus identifying problems and
fixing those issues before they become catastrophic,
❖ lifetime prediction: including the ability to revise robot swarm lifetime estimate
in service.
❖ performance assurance: to check that any measured deviations from the
swarm specification do not compromise performance to an unacceptable
degree
21. Digital Twins for the robots of Fiorella
saving costs, improves fault tolerance and mission performance.
21
22. R3. The interplay between design and
runtime models
While complex design models can be obtained through an offline
optimization method, online methods are constrained to create runtime
models that should be updated in a distributed way.
It is crucial for system and software engineers to understand which
mission and system aspects can be managed through a design model
and which should be managed through the collection of runtime models
of the individual robots.
Aspects to be considered:
❖ the complexity of the design
versus the complexity of the
runtime model employed directly on the real robots
❖ the granularity of the design and runtime models
❖ the accuracy of the runtime models 22
23. Conclusions
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A well-established software engineering discipline can
make a contribution in:
i. defining formal abstractions for mission and system
specifications.
ii. SwarmOps principles to the development and operation of
robot swarms
iii. identifying and defining benchmarks that are important to
be considered when designing and evaluating swarm
control software.
iv. support swarm roboticists in their efforts to design safer
and more reliable robot swarms that operate together with
people.
24. References
1. Vito Trianni, Marco Dorigo. “Self-organisation and communication in
groups of simulated and physical robots”, Biological Cybernetics, 2006
2. Alexandre Pacheco, Volker Strobel, Andreagiovanni Reina & Marco
Dorigo. “Real-Time Coordination of a Foraging Robot Swarm Using
Blockchain Smart Contracts” ANTS 2022
3. Mauro Birattari, Antoine Ligot, Darko Bozhinoski, Manuele Brambilla,
Gianpiero Francesca, Lorenzo Garattoni, David Garzón Ramos, Ken
Hasselmann, Miquel Kegeleirs, Jonas Kuckling, Federico Pagnozzi, Andrea
Roli, Muhammad Salman, Thomas Stützle. “Automatic off-line design of
robot swarms: a manifesto”, Frontiers in Robotics and AI
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