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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


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21st July 2014. My presentation at MORSE 2014 ( about a family of Domain-Specific Languages for specifying Civilian Missions of Multi-Robot Systems.

21st July 2014. My presentation at MORSE 2014 ( about a family of Domain-Specific Languages for specifying Civilian Missions of Multi-Robot Systems.

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  • 1. Davide Di Ruscio Ivano Malavolta Patrizio Pelliccione A family of Domain-Specific Languages for specifying Civilian Missions of Multi-Robot Systems
  • 2. Roadmap Background Challenges The family of languages Application to autonomous quadrotors Conclusions and future work
  • 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. Using robots for civilian missions [1] Many civilian missions can be executed either by flying, ground or water robots
  • 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. 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. 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
  • 8. Application scenario[2]
  • 9. The family of languages Mission Context Map MML BL Behavior BL models synthesis Robots configuration Mission Execution Engine RL
  • 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. Monitoring mission language (MML) Mission layer: sequence of tasks executed by a swarm of robots extensible
  • 12. Monitoring mission language (MML) Context layer: geographical areas that can influence the execution of the mission The focus is on spatial context
  • 13. Robot language (RL) Hardware and low-level configuration of each type of robot
  • 14. Behaviour language (BL) Atomic movements and actions performed by each robot of the swarm
  • 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. 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 Issues •  require a trained pilot to operate them •  restricted to line-of-sight range
  • 17. Languages extensions unchanged MML BL RL
  • 18. Example (1) MML model (in the tool) PG1 NF1 NF2 R1 home
  • 19. Example (2) Robot model (Parrot)
  • 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. 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. Conclusions
  • 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. 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. + 39 380 70 21 600 Ivano Malavolta | Gran Sasso Science Institute iivanoo Contact