Davide Di Ruscio 
Ivano Malavolta
Patrizio Pelliccione
A family of Domain-Specific Languages 
for specifying Civilian Missi...
Roadmap
Background
Challenges
The family of languages
Application to autonomous quadrotors
Conclusions and future work
Civilian missions today
•  High costs
–  team training and transportation
–  operating costs
•  Safety
–  significant risks...
Using robots for civilian missions [1]
Many civilian missions can be executed either by flying, ground or water robots
Multi-robots missions
Civilian missions can be executed by multiple robots

à lower mission completion time
à fault-tole...
Challenges
•  On-site operators must be expert of all the types of used robots 
–  in terms of dynamics, hardware capabili...
MDE for multi-robot missions
MDE allows all stakeholders to focus on models of the mission with
concepts that are:

•  clo...
Application scenario[2]
The family of languages
Mission
Context
Map
MML 
BL
Behavior
BL models synthesis
Robots
configuration
Mission
Execution Eng...
Principles


Mask complexity 

à usable by non-technical experts

à domain-specific concepts

Independence w.r.t. the typ...
Monitoring mission language (MML)
Mission layer: sequence of tasks executed by a swarm of robots
extensible
Monitoring mission language (MML)
Context layer: geographical areas that can influence the execution
of the mission
The foc...
Robot language (RL)
Hardware and low-level configuration of each type of robot
Behaviour language (BL)
Atomic movements 
and actions performed
by each robot of the 
swarm
Involved stakeholders
Operator

in-the-field stakeholder specifying the mission

Robot engineer
–  models a specific kind of...
Extension for autonomous quadrotors
Special kind of helicopter with:
•  high stability
•  omni-directional
•  smaller fixed...
Languages extensions








 

unchanged







 

MML 
BL
RL
Example (1)
MML model (in the tool)
PG1
NF1
NF2
R1
home
Example (2)
Robot model (Parrot)
Example (3)
Behavioural model
Drone&
D1&
Drone&
D2&
Drone&
D3&
Start&(ε,&ε)& Start&(ε,&ε)& Start&(ε,&ε)&
TakeOff&(ε,&ε)& Ta...
Tool support
Editor for
MML models
M2M transformation
+
models validation
Layer of controllers that interpret BL
models at...
Conclusions
Future work
Extend the languages with timing constraints

Design a generic software architecture for 
–  mission editors, ...
References
[1] Skrzypietz, T.: Unmanned Aircraft Systems for Civilian Missions. BIGS policy paper.
Brandenburgisches Insti...
+ 39 380 70 21 600
Ivano Malavolta | 
Gran Sasso Science Institute
iivanoo
ivano.malavolta@gssi.infn.it
www.di.univaq.it/m...
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A family of Domain-Specific Languages for specifying Civilian Missions of Multi-Robot Systems

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21st July 2014. My presentation at MORSE 2014 (http://st.inf.tu-dresden.de/MORSE14) about a family of Domain-Specific Languages for specifying Civilian Missions of Multi-Robot Systems.

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A family of Domain-Specific Languages for specifying Civilian Missions of Multi-Robot Systems

  1. 1. Davide Di Ruscio Ivano Malavolta Patrizio Pelliccione A family of Domain-Specific Languages for specifying Civilian Missions of Multi-Robot Systems
  2. 2. Roadmap Background Challenges The family of languages Application to autonomous quadrotors Conclusions and future work
  3. 3. Civilian missions today •  High costs –  team training and transportation –  operating costs •  Safety –  significant risks (e.g., fire, earthquake, etc.) •  Timing and endurance –  exhausting shifts –  activities stopped at night
  4. 4. Using robots for civilian missions [1] Many civilian missions can be executed either by flying, ground or water robots
  5. 5. Multi-robots missions Civilian missions can be executed by multiple robots à lower mission completion time à fault-tolerance w.r.t. mission goal fulfillment à enables the use of highly-specialized robots All the robots perform their actions to fulfil the common goal of the mission however... common goal
  6. 6. Challenges •  On-site operators must be expert of all the types of used robots –  in terms of dynamics, hardware capabilities, etc. •  On-site operators have to simultaneously control a large number of robots during the mission execution •  Robots provide very low-level APIs and very basic primitives –  error-prone development –  task-specific robots –  no reuse These issues ask for •  abstraction •  automation
  7. 7. MDE for multi-robot missions MDE allows all stakeholders to focus on models of the mission with concepts that are: •  closer to the application domain •  independent from the specific robot technologies •  enabling automation à autonomous robots http://mdse-book.com
  8. 8. Application scenario[2]
  9. 9. The family of languages Mission Context Map MML BL Behavior BL models synthesis Robots configuration Mission Execution Engine RL
  10. 10. Principles Mask complexity à usable by non-technical experts à domain-specific concepts Independence w.r.t. the types of robots Reuse of models Robots must be autonomous
  11. 11. Monitoring mission language (MML) Mission layer: sequence of tasks executed by a swarm of robots extensible
  12. 12. Monitoring mission language (MML) Context layer: geographical areas that can influence the execution of the mission The focus is on spatial context
  13. 13. Robot language (RL) Hardware and low-level configuration of each type of robot
  14. 14. Behaviour language (BL) Atomic movements and actions performed by each robot of the swarm
  15. 15. Involved stakeholders Operator in-the-field stakeholder specifying the mission Robot engineer –  models a specific kind of robot –  develops the controller that instructs the robot on how to perform BL basic operations Platform extender –  extends the MML metamodel with new kinds of tasks –  develops a synthesizer for transforming each new task to its corresponding BL operations MML RL + controller MML + synthesizer
  16. 16. Extension for autonomous quadrotors Special kind of helicopter with: •  high stability •  omni-directional •  smaller fixed-pitch rotors à safer than classical helicopters •  simple to design and construct •  relatively inexpensive image from http://goo.gl/FJFS5l Issues •  require a trained pilot to operate them •  restricted to line-of-sight range
  17. 17. Languages extensions unchanged MML BL RL
  18. 18. Example (1) MML model (in the tool) PG1 NF1 NF2 R1 home
  19. 19. Example (2) Robot model (Parrot)
  20. 20. Example (3) Behavioural model Drone& D1& Drone& D2& Drone& D3& Start&(ε,&ε)& Start&(ε,&ε)& Start&(ε,&ε)& TakeOff&(ε,&ε)& TakeOff&(ε,&ε)& TakeOff&(ε,&ε)& GoTo&(ε,&ε)&GoTo&(ε,&ε)& GoTo&(ε,&ε)& GoTo&(ε,&{Photo})&GoTo&(ε,&{Photo})& GoTo&(ε,&{Photo})& GoTo&(ε,{Photo,BroadCast(D3.R1.Done)})& GoTo&(ε,&ε)& Land&(ε,&ε)& Stop&(ε,&ε)& GoTo&(ε,&ε)& Land&(ε,&ε)& Stop&(ε,&ε)& 0GoTo&(ε,&{Photo,&& BroadCast&(D2.PG1.Done)})& 0 GoTo&(ε,&ε)& Land&(ε,&ε)& Stop&(ε,&ε)& GoTo(ε,&{Photo,&& BroadCast&(D1.PG1.Done)})& PG1 PG1 R1
  21. 21. Tool support Editor for MML models M2M transformation + models validation Layer of controllers that interpret BL models at run-time HTML5, CSS3, JavaScript Java + OCL Java + ROS + Rosbridge Drone driver any
  22. 22. Conclusions
  23. 23. Future work Extend the languages with timing constraints Design a generic software architecture for –  mission editors, model transformations –  run-time engine for executing the mission Safety and security as first-class elements both at mission design-time and run-time A more systematic language extension mechanism (like in [3]) Exercise the family of languages with other kinds of robot (e.g., underwater missions)
  24. 24. References [1] Skrzypietz, T.: Unmanned Aircraft Systems for Civilian Missions. BIGS policy paper. Brandenburgisches Institut fur Gesellschaft und Sicherheit. BIGS (2012) [2] Di Ruscio, D., Malavolta, I., Pelliccione, P.: Engineering a platform for mission planning of autonomous and resilient quadrotors. In: Fifth International Workshop, on Software Engineering for Resilient Systems , Springer Berlin Heidelberg (2013) 33–47 [3] Di Ruscio, D., Malavolta, I., Muccini, H., Pelliccione, P., Pierantonio, A.: Developing Next Generation ADLs Through MDE Techniques. In: Procs. ICSE’10, ACM (2010) 85–94
  25. 25. + 39 380 70 21 600 Ivano Malavolta | Gran Sasso Science Institute iivanoo ivano.malavolta@gssi.infn.it www.di.univaq.it/malavolta Contact
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