Engineering the software of robotic systems - 2 - Drones [ICSE 2017 - technical briefing]

Federico Ciccozzi1, Davide Di Ruscio2, Ivano Malavolta3,
Patrizio Pelliccione4, Jana Tumova5
1Malardalen University (Sweden)
2University of L’Aquila (Italy)
3Vrije Universiteit Amsterdam (The Netherlands)
4Chalmers University of Technology | GU (Sweden)
5KTH Royal Institute of Technology (Sweden)
Engineering the software
of robotic systems - FLYAQ
UNIVERSITY OF L’AQUILA

Federico Ciccozzi, Davide Di Ruscio, Ivano Malavolta, Patrizio Pelliccione, Jana Tumova
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
What is a drone?

Application scenario
Mission
to monitor the CO2 level in a geographical area by monitoring
it along a grid composed of cells of size 12x12 meters
Contextual entities
obstacles
emergency areas
no fly zones
home
NO FLY ZONE

Mission
modeling
Mission
Context
Map
MML
BL
Behavior specification
Detailed flight plan synthesis
Robot type
models
Mission
Execution
Engine
this layer is extensible
RL

Mask complexity
• usable by non-technical experts
• domain-specific concepts
• separation of concerns
Independence w.r.t. the types of robots
• behaviour generation capability
Reuse
• context models
• robot models
Robots must be autonomous
Principles Mission
Context
Map
MML
BL
Behaviorspecification
Robot type
models
Mission
Execution
EngineRL

Modeling languages Mission
Context
Map
MML
BL
Behaviorspecification
Robot type
models
Mission
Execution
EngineRL

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 + software controller
MML + synthesizer
Involved stakeholders

Mission layer
sequence of tasks executed by a team of robots
Extension point
Monitoring mission language (MML)

Photogrid task
• identifies a virtual grid within an area
• each cell of the grid has a size of n meters
• each drone flies over each cell of the grid at an altitude of z
meters and takes a picture of the ground
Example of extension

Example (1) MML model (in the tool)
NF1
NF2
home
RT
PGT

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)& 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

Web UI
Mission Design
and
Behaviour
Synthesis
request MML &
drones configurations
1
2 MML concepts & drones
3 mission design
4 submit MML models
6 BL model and analysis results
5
behaviour analysis
and synthesis
7 start mission
Mission Execution
Engine
Mission modeling workflow

Mission execution
Mission Execution
Engine
6 send status*
(continuous flow)
start mission
setup drone
controllers
* steps executed in parallel
Monitoring
Engine
tasks execution
commands*
feedback*
feedback*
25
1
3
4 C1
D1

s1
0 TakeOff(p1.z) s1
Mission entering
s2s'''2
Goto(c1)
s''2
Goto(c2)
s'2
Goto(p1)Goto(home1)Land
s1
f land1
Mission leaving
s'1 Goto(c2) s''1 Goto(p1) v1
v1
1
Goto(q1) / DoPhoto(…)
v2
1v16
1
…
NoOp
Mission tasks execution
Goto(q16) / DoPhoto(…)
NoOp
r1
NoOp
Goto(c1)
v17
1
Flight plan synthesis
MML
Each MML task is constrained
by:
• geometry
• behavioural strategy
• actions

q4= p2
q1= p1
q3
q14
q5
q6
q15
q16
q2
q13
q12
q11
q7
q8
q9
q10
p3
p4
c1
c2
p1
Synthesis method on the example

Federico Ciccozzi, Davide Di Ruscio, Ivano Malavolta, and Patrizio Pelliccione. Adopting mde for specifying and executing
civilian missions of mobile multi-robot systems. IEEE Access, PP(99):1–1, 2016.
Darko Bozhinoski, Davide Di Ruscio, Ivano Malavolta, Patrizio Pelliccione, Massimo Tivoli (2015). FLYAQ: Enabling Non-Expert
Users to Specify and Generate Missions of Autonomous Multicopters. In 30th IEEE/ACM International Conference on
Automated Software Engineering (ASE 2015).
Darko Bozhinoski, Ivano Malavolta, Antonio Bucchiarone, Annapaola Marconi (2015). Sustainable Safety in Mobile Multi-Robot
Systems via Collective Adaptation. In Ninth IEEE International Conference on Self-Adaptive and Self-Organizing Systems,
SASO 2015, Cambridge, Massachusetts, USA, September 21-25, 2015,
Darko Bozhinoski (2015). Managing Safety and Adaptability in Mobile Multi-Robot Systems. In Proceedings of the 11th
International ACM SIGSOFT Conference on Quality of Software Architectures, pp. 135–140.
Davide Di Ruscio, Ivano Malavolta, Patrizio Pelliccione (2014). A family of Domain-Specific Languages for specifying Civilian
Missions of Multi-Robot Systems. In Proceedings of the 1st International Workshop on Model-Driven Robot Software
Engineering (MORSE), pp. 13–26.
Davide Di Ruscio and Ivano Malavolta and Patrizio Pelliccione (2014). The Role of Parts in the System Behaviour. In Software
Engineering for Resilient Systems - 6th International Workshop, SERENE 2014, Budapest, Hungary, October 15-16, 2014.
Proceedings, pp. 24–39.
Davide Di Ruscio, Ivano Malavolta, Patrizio Pelliccione (2013). Engineering a Platform for Mission Planning of Autonomous and
Resilient Quadrotors. In Software Engineering for Resilient Systems - Fifth International Workshop, SERENE 2013, pp. 33–47.
References

Backup slides

Tool support
…
ROS
HTTPS and web
sockets
Web interface
Editor for MML
models
M2M transformation
+ models validation
+ mission workflow
manager
Layer of controllers that interpret BL
models at run-time
HTML5, CSS3,
JavaScript Java + OCL + Rosbridge
Any technology supported by ROS
Drone driver
any
C1
C2
C3
…
Any
protocol

point
line polygon
volume
Geometries supported by MML

sweep(d) – full coverage of an area discretized by a grid
whose cells dimension is d
search(target, d) – visit performed on a d-sized grid
towards the discovery of target (e.g., represented by a PNG
image)
track(m, m’, d) – it is like sweep(d) where m and m’are
the visit restart and stop messages, respectively
Actions:
• taking a picture
• making a video
• detecting the presence of CO2
• etc.
Strategies supported by MML

Let’s go back to the example…
NF1
NF2
home
RT
PGT

Context = ( (NF1,NF2), (OB) )
PGT = ( (p1,p2,p3,p4,p1), sweep(10), (doPhoto(2480 3508), i) )
Swarm ={ (d1, {PGT}), (d2, {PGT}), (d3, {RT}) }
Task dependency graph
( (t0, PGT, RT, t3), ∅ ∪ { (t0, {PGT, RT}) } ∪ { ({PGT, RT}, t3) } )
t0
PGT RT
t3
Synthesis method on the example

• Safety
• avoidance of collisions (P1), and of traversing no-fly zones
(P2)
• no concurrency issues (P3)
• P1&P2
• correct and complete specification
• thanks to specific auxiliary functions
• volume partitioning algorithms
• visibility graph
• coverage path planning
• P3
• partitioning in independent areas
• no message lost
• sound mechanisms for sequencing, join, and fork
• a drone cannot be involved in concurrent tasks
Properties of the synthesized BL model

EXPERIMENTATION
models@runtime to control the
mission execution and manage
adaptation
Support other types of robots
Mission Execution
Engine
navigation
commands
(MAVLink)
telemetry
(MAVLink)
C1
MAV
Proxy
Drone
Kit
Arducopter
physics
simulator
physics data
Google Earth
drones positions at run-time
Future
work

BL model automatic synthesis
BL
Mission entering
Mission leaving
Mission tasks
execution
…
…
dn
…
d2
…
d1
State transition
State
Synchronization and communication message
Flight plan synthesis
MML

Engineering the software of robotic systems - 2 - Drones [ICSE 2017 - technical briefing]

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