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. Davide Di Ruscio
Ivano Malavolta
Patrizio Pelliccione
A family of Domain-Speciļ¬c Languages
for specifying Civilian Missions
of Multi-Robot Systems
3. Civilian missions today
ā¢āÆ High costs
āāÆ team training and transportation
āāÆ operating costs
ā¢āÆ Safety
āāÆ signiļ¬cant risks (e.g., ļ¬re, earthquake, etc.)
ā¢āÆ Timing and endurance
āāÆ exhausting shifts
āāÆ activities stopped at night
4. Using robots for civilian missions [1]
Many civilian missions can be executed either by ļ¬ying, ground or water robots
5. Multi-robots missions
Civilian missions can be executed by multiple robots
Ć ļ lower mission completion time
Ć ļ āÆfault-tolerance w.r.t. mission goal fulļ¬llment
Ć ļ āÆenables the use of highly-specialized robots
All the robots perform their actions to fulļ¬l the common goal of
the mission
however...
common goal
6. Challenges
ā¢āÆ On-site operators must be expert of all the types of used robots
āāÆ in terms of dynamics, hardware capabilities, etc.
ā¢āÆ On-site operators have to simultaneously control a large number
of robots during the mission execution
ā¢āÆ Robots provide very low-level APIs and very basic primitives
āāÆ error-prone development
āāÆ task-speciļ¬c robots
āāÆ no reuse
These issues ask for
ā¢āÆ abstraction
ā¢āÆ automation
7. MDE for multi-robot missions
MDE allows all stakeholders to focus on models of the mission with
concepts that are:
ā¢āÆ closer to the application domain
ā¢āÆ independent from the speciļ¬c robot technologies
ā¢āÆ enabling automation Ć ļ autonomous robots
http://mdse-book.com
9. The family of languages
Mission
Context
Map
MML
BL
Behavior
BL models synthesis
Robots
conļ¬guration
Mission
Execution Engine
RL
10. Principles
Mask complexity
Ć ļ usable by non-technical experts
Ć ļ domain-speciļ¬c concepts
Independence w.r.t. the types of robots
Reuse of models
Robots must be autonomous
12. Monitoring mission language (MML)
Context layer: geographical areas that can inļ¬uence the execution
of the mission
The focus is on spatial context
15. Involved stakeholders
Operator
in-the-ļ¬eld stakeholder specifying the mission
Robot engineer
āāÆ models a speciļ¬c kind of robot
āāÆ develops the controller that instructs the robot on how to perform
BL basic operations
Platform extender
āāÆ extends the MML metamodel with new kinds of tasks
āāÆ develops a synthesizer for transforming each new task to its
corresponding BL operations
MML
RL + controller
MML + synthesizer
16. Extension for autonomous quadrotors
Special kind of helicopter with:
ā¢āÆ high stability
ā¢āÆ omni-directional
ā¢āÆ smaller ļ¬xed-pitch rotors
Ć ļ safer than classical helicopters
ā¢āÆ simple to design and construct
ā¢āÆ relatively inexpensive
image from http://goo.gl/FJFS5l
Issues
ā¢āÆ require a trained pilot to operate them
ā¢āÆ restricted to line-of-sight range
23. Future work
Extend the languages with timing constraints
Design a generic software architecture for
āāÆ mission editors, model transformations
āāÆ run-time engine for executing the mission
Safety and security as ļ¬rst-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. 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) 33ā47
[3] Di Ruscio, D., Malavolta, I., Muccini, H., Pelliccione, P., Pierantonio, A.: Developing Next
Generation ADLs Through MDE Techniques. In: Procs. ICSEā10, ACM (2010) 85ā94
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Ivano Malavolta |
Gran Sasso Science Institute
iivanoo
ivano.malavolta@gssi.infn.it
www.di.univaq.it/malavolta
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