Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Modelsward 2018 Industrial Track - Alessandra Bagnato

233 views

Published on

Modelling a CPS Swarm System: A Simple Case Study
The CPSwarm workbench is a toolchain that facilitates the entire design process of swarms of CPS including
modelling, design, optimization, simulation and deployment. This paper highlights part of the work of the
CPSwarm workbench in the context of the CPSwarm H2020 project. In particular, the CPSwarm workbench
allows to create a generic swarm library that can be customized by developers to design new swarm environments,
new swarm members and new swarm goals. This paper shows an application of the initial CPSwarm
workbench by the example of a reference problem called EmergencyExit. In this example a swarm of robots
needs to find an exit in an unmapped environment and leave this room through the exit as soon as possible.
The example problem is further used to show the integration of Modelio, a UML/SysML modelling tool, and
FREVO, an optimization tool in the CPSwarm workbench

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Modelsward 2018 Industrial Track - Alessandra Bagnato

  1. 1. Intermediate Review Meeting (25/10/2017) Modelling a CPS Swarm System: A Simple Case Study in the CPSwarm H2020 Project Melanie Schranz1, Alessandra Bagnato2, Etienne Brosse2 and Wilfried Elmenreich3 1 Lakeside Labs, Klagenfurt, Austria 2 Softeam Research and Development Department, Paris, France 3 Alpen-Adria-Universität Klagenfurt, Austria Madeira, Portugal | 22nd Janvier 2018
  2. 2. Intermediate Review Meeting (25/10/2017) Outline • CPSwarm Project – Overview – Project Industrial Application Scenarios • Modelling a CPS Swarm System: A Simple Case Study – Initial CPS Modeling Library – Modelio.org: Development of the first version of CPSwarm Modeling Tool & CPSwarm Design Environment Language – Frevo: Optimization Tool • Future Work 2Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  3. 3. Intermediate Review Meeting (25/10/2017) CPSwarm Overview
  4. 4. Intermediate Review Meeting (25/10/2017) CPSwarm Vision Interactions amongst CPS might lead to new behaviors and emerging properties, often with unpredictable results. These interactions can become an advantage if explicitly managed since early design stages. © Josh Cassidy/KQED Science Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  5. 5. Intermediate Review Meeting (25/10/2017) High-Level Objective CPSwarm proposes a new science of system integration and tools to support engineering of CPS swarms. CPSwarm tools will ease development and integration of complex herds of heterogeneous CPS that collaborate based on local policies and that exhibit a collective behavior capable of solving complex, industrial-driven, real-world problems.
  6. 6. Intermediate Review Meeting (25/10/2017) CPSwarm at a Glance • CPSwarm is a 36-months Research and Innovation Action (RIA) funded under H2020 call ICT-01-2016 • Scope: science of system integration in the domain of swarms of CPS • 8 partners (3 Research Institutes, 1 University, 2 Large Enterprises, 3 SMEs) from 6 EU countries • Around 4.9 M€ total costs (578 PMs ≈ 16 FTE)
  7. 7. Intermediate Review Meeting (25/10/2017) ConsortiumThe Coordinator Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  8. 8. Intermediate Review Meeting (25/10/2017) CPSwarm Application Scenarios
  9. 9. Intermediate Review Meeting (25/10/2017) Application Scenarios Three reference Application Scenarios drive the collection of requirements for the development of the complete CPSwarm toolchain supporting the engineering and deployment of CPS swarms Swarm Drones Swarm Logistics AssistantAutomotive CPS © dailymail.co.uk © Drew Kelly for WIREDPhantom_Glacier: Image courtesy DJI Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  10. 10. Intermediate Review Meeting (25/10/2017) Automotive CPS: TTECH – Automotive Cyber Phisical Systems Use Case • Objectives: Enhancing and carrying on the developments for the (near) autonomous platform for autonomous operation. • Applications for collective driving with a focus on autonomous driving vehicles intended for freight transportation • independent vehicles could join or leave a swarm at any point during the journey • Laboratory level demonstrator (TRL 3 to TRL 4, demonstration in breadboard lab environment) • E.g., trucks, vans or cars and connecting them via kind of an electronic drawbar. © dailymail.co.uk © Daimler© clepa.eu Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  11. 11. Intermediate Review Meeting (25/10/2017) Swarm Logistics Assistant: Robotnik Focus on robots and rovers designed to assist humans in logistics domain • Scan the entire area of the warehouse and share the acquired information • Collect information about the maps of the entire area • Collect additional information implicitly e.g. room temperature, presence of humans, detection of in-path obstacles etc. • Join forces to move a heavy obstacle from one place to another © Drew Kelly for WIRED © OTTO © Fraunhofer IML Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  12. 12. Intermediate Review Meeting (25/10/2017) CPS Use Cases: the Robotnik context • Robotnik is a Service Robotics Manufacturer: mobile bases and manipulators: • Unmanned Ground Vehicle (UGV)’s - An unmanned ground vehicle (UGV) is a vehicle that operates while in contact with the ground and without an onboard human presence • Mobile manipulators • Omnidirectional mobile platforms • ROS based robots Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  13. 13. Intermediate Review Meeting (25/10/2017) • In CPSwarm we consider heterogeneous swarms of ground robots/rovers and UAVs to conduct certain missions in the surveillance of critical infrastructure like industrial or power plants, search and rescue (SAR) and fire detection. • The use case exploits different types of robots to approach an area and support rescuers/guardians by: mapping the actual state with a 3D map, finding people, tracking them and checking on their condition using computer vision, identifying safe passages for rescuers. • Smaller robots (rover/UAV) in the swarm are employed to map the environment and possibly move inside narrow passages whereas "bigger" robots could either guide rescuers to people. Heterogeneous swarms of ground rovers and UAVs Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  14. 14. Intermediate Review Meeting (25/10/2017) Heterogeneous swarms of ground rovers and UAVs SURVEILLANCE Intrusion detection (detection of unauthorized persons entering of the plant area). Follow and observe actions of unauthorized persons in the plant. 01 SEARCH & RESCUE Generating a situation overview of the disaster scene in case of an industrial plant accident including real-time images (vis, IR), toxic and explosive gas leakage detection. Finding of human casualties or persons trapped in the disaster area. 02 FIRE FIGHTERS Generating a situation overview of the disaster scene. Aerial or ground unmanned systems will ensure safer and more efficient intervention. 03 Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  15. 15. Intermediate Review Meeting (25/10/2017) Use case: operating SCENARIO D2 D3 D1 D4 R1 R2 R4 R3 D R Aerial drones Rovers A single mission planner to define the area of intervention setting the limits, the path and the observation points. Drones and rover are synchronized automatically, each device knows the position of others and communicates with the control center via 4G/LTE/WiFi Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  16. 16. Intermediate Review Meeting (25/10/2017) Use case: Fire Fighters • Every year forest fires burn, on average, about 500 000 hectares in Europe. • Each fire requires the intervention of firefighters, involving the deployment of aerial and ground assets which need to be coordinated in high risk operational areas. • Currently, the intervention is solely coordinated by the chain of command, which may expose to high danger the firemen safety. As the fire dynamics is depending on external factors (wind vegetation, artifact), the development of mathematical models is required. • The possibility to employ aerial or ground unmanned assets will ensure safer and more efficient intervention. Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  17. 17. Intermediate Review Meeting (25/10/2017) Initial CPS Modeling Library
  18. 18. Intermediate Review Meeting (25/10/2017) How to model a swarm Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  19. 19. Intermediate Review Meeting (25/10/2017) Initial Modeling Library for CPS Models • Overall Idea • Library with pre-defined models • Models: reused, changed, added • Separation into three initial groups (see Figure) • Swarm Member • Environment • Goal • Mandatory parts for each model • Unique name • Description • Parameters • Property: type [range] • Input: type [range] • Output: type [range] Swarm Member Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  20. 20. Intermediate Review Meeting (25/10/2017) Swarm Member (general def.) • Describes a single CPS • Sub-libraries: • local memory: local status, e.g. the current x/y position, available energy, etc. • behaviour: collecting data from sensor, performing calculations, sending data to actuators • physical aspects: sensors and actuators • security (optional) • human interaction (optional) Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  21. 21. Intermediate Review Meeting (25/10/2017) Environment (general def.) • Describes the environment of the CPS • Following models are mandatory, further ones can be added: • 2D/3D Map of the environment • occupancy grid map, i.e. free space and obstacles • expressed as a bitmap file • Size of the environment • width and height • expressed in number of grid cells • Resolution • expressed in number of grid cells per meter Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  22. 22. Intermediate Review Meeting (25/10/2017) Goal (general def.) • Description of the goal • … in terms of modelling the fitness by • Incorporating parameters from other models • Calculation specification right in the model • Multiple and multi-dimensional fitness values can be modelled Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  23. 23. Intermediate Review Meeting (25/10/2017) CPSWARM WORKBENCH
  24. 24. Intermediate Review Meeting (25/10/2017) CPSwarm Workbench Case Study settings • CPSwarm Workbench: • Modeling Tool - to model swarms of CPSs. • An optimization tool for further optimization of the models. • An optimization tool API – to passes configuration files from the modelling tool Modelio to the Optimization tool In the context of this paper we used Modelio as Modeling tool and Frevo as Optimization tool. 24Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  25. 25. Intermediate Review Meeting (25/10/2017) Modelio for Software and System Engineering 25 • Available under: Modelio.org Modeliosoft.com • Modelio UML editor with more than 20 years’ history • SysML • MARTE • Code generation • Documentation • Teamwork • World Wide Modelling, • Distribute and share models on the web Diagram Explorer tabs Diagram Palette Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  26. 26. Intermediate Review Meeting (25/10/2017) CPSwarm Modeling Environnent 26 • Modelio Modeling Environment Ergonomics and specific palette for each diagrams: • Swarm Member Architecture Modelling Elements • Swarm Architecture Modelling Elements Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  27. 27. Intermediate Review Meeting (25/10/2017) • These compositions are made by using Object Management Group Systems Modeling Language (SysML) and more precisely a Block Definition Diagram (BDD) for the Swarm Architecture and an Internal Block Definition Diagram (IBD) for the Swarm Member Architecture. 27 Modeling Environment Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  28. 28. Intermediate Review Meeting (25/10/2017) Modeling Environment • Modeling Environment CPS swarm template generation: A CPS swarm model contains several elements sorted into three domains, three packages - respectively named “Swarm”, “Environment”, and “Goal” - are created when using CPS swarm template generation. • Modeling Environnent CPS swarm predefined diagrams: 5 CPSwarm predefined diagrams and associated modeling palette with required SysML and UML elements have been defined and implemented. 28Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  29. 29. Intermediate Review Meeting (25/10/2017) • Optimization Project Generation: Once the CPS swarm is modelled, the next steps is to optimize it by using the Algorithm Optimization Environment. • The Modelling Tool can pass informations to the Optimization Tool through the Optimization API. This is can be done by exporting using the Optimization Project generation. • The Export can be done by right clicking on any CPS Problem, then selecting CPSwarm > Optimization Project generation entry to achieve integration • 29 Optimization Project Generation integration Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  30. 30. Intermediate Review Meeting (25/10/2017) Optimization tool API • The optimization tool API provides all modeling details to the algorithm optimization environment. They are received by the optimization tool and further interpreted. • The models that are required for simulation are defined through the modeling process and forwarded to the optimization simulator through the simulator API. Two files are provided: • A problem description file as a Java class that implements the problem to be optimized. • A parameters file complements the problem description as it specifies all model parameters as an XML file, parameters include configuration, properties, and requirements. 30Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  31. 31. Intermediate Review Meeting (25/10/2017) Optimization tool FREVO • FREVO (Framework for Evolutionary Design) • needs a simulation of the problem • Interface for sensor/actuator connections to the agents • Feedback from a simulation run -> fitness value 31 • Open source: http://frevo.sourceforge.net Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  32. 32. Intermediate Review Meeting (25/10/2017) Emergency Exit case study
  33. 33. Intermediate Review Meeting (25/10/2017) Initial CPS Modelling – on the example of the EmergencyExit Description EmergencyExit example: In the EmergencyExit example, multiple agents move in a 2D, discrete environment and try to find one of two emergency exits. In each discrete time step, an agent senses the neighbouring cells and moves to a free cell. When an agent reaches an emergency exit, it is removed from the environment. The goal is that all agents exit the environment. Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  34. 34. Intermediate Review Meeting (25/10/2017) • In this example a swarm of robots needs to find an exit in a unmapped environment and leave this room through the exit as soon as possible. • A possible scenario matching this problem could be fire or collapsing buildings. • In the case study, the problem was first modeled using Modelio and then exported to FREVO for optimization. • FREVO was then used to generate a possible solution by applying an evolutionary search algorithm in order to optimize the weights for an ANN. • In this work solution had been evaluated using simulation. 34Madeira, MODELSWARD IndTrack 2018, 22nd January 2018 Initial CPS Modelling – on the example of the EmergencyExit The Design Environment Architecture
  35. 35. Intermediate Review Meeting (25/10/2017) Swarm Member – in the EmergencyExit Madeira, MODELSWARD IndTrack 2018, 22nd January 2018 Models of the library: • “localState” • Description:” The local state describes the current x/y position of the swarm member” • Property name: “localState.x”, type: int • Property name: “localState.y”, type: int • “ps: POI Sensor” • Description:” The POI sensor returns a vector, describing the path to the next point of interest (POI)” • Output: Path, type: int[2] • “rs: Range Sensor” • Description:” The range sensor returns the map around the swarm member.” • Output: NearField, type: int [8] • “loc: Locomotion” • Description:” The locomotion actuator is a motor that moves the swarm member to a given x/y coordinate.”” • Input: Position, type: int [2] • “exb: EmergencyExit behavior” • Description:” In the EmergencyExit example, multiple swarm members move in a 2D, discrete environment and try to find one of two emergency exits. In each discrete time step, a swarm member senses the neighbouring cells and moves to a free cell. When a swarm member reaches an emergency exit, it is removed from the environment. The goal is that all swarm member exits the environment.” • Input1: Path, type: int [2] Input2: NearField, type: int [8] Output: Position, type: float [2]
  36. 36. Intermediate Review Meeting (25/10/2017) Environment – in the EmergencyExit Madeira, MODELSWARD IndTrack 2018, 22nd January 2018 Models of the library: • “map: String” • Description:” The map defines a file path to the bitmap file of the environment.” • Property: path, type: string “C:tempCPSwarmenviron mentEmergencyExit.png” • “res: Resolution2D” • Description:” The resolution defines the resolution of the map (number of grid cells per meter).” • Property: x, type: int • Property: y, type: int • “size: Size2D” • Description: «The size defines the size of the map (total number of grid cells in height and width).” • Property: x, type: int • Property: y, type: int • “poi: Position2D” • Description: «The POI defines two points of interest (POI) in the map with their x/y coordinates.” • Property: poi.x., type: int • Property: poi.y., type: int
  37. 37. Intermediate Review Meeting (25/10/2017) Goal – in the EmergencyExit Madeira, MODELSWARD IndTrack 2018, 22nd January 2018 The fitness function represents the goal of the specified CPS swarm. By maximizing this fitness function, the Algorithm Optimization Environment can provide the best CPS swarm configuration
  38. 38. Intermediate Review Meeting (25/10/2017) • Simulation Settings of FREVO • Simulation of the EmergencyExit example with Stage/ROS 38Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  39. 39. Intermediate Review Meeting (25/10/2017) Next Steps Swarm Intelligence Models and Algorithms
  40. 40. Intermediate Review Meeting (25/10/2017) Swarm Intelligence Models Process of adopting models found in nature: o ants, bees, fire flies, fish, etc. • Characteristics • Emergent behavior arises from simple interactions among individuals in a swarm • Individuals act according to simple and local behavior • Organized behavior emerges automatically • There is no central control ! NO common modeling approach ! Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  41. 41. Intermediate Review Meeting (25/10/2017) Ant Routing - Inspiration • Foraging behavior of ants • Single ants are foolish – whole system exhibits “intelligent“ behavior Ant hill, nest Food ? next ant Obstacle Madeira, MODELSWARD IndTrack 2018, 22nd January 2018
  42. 42. Intermediate Review Meeting (25/10/2017) THANKS! ANY QUESTIONs?
  43. 43. Intermediate Review Meeting (25/10/2017) CONTACTS Alessandra Bagnato Softeam R&D Alessandra.bagnato@softeam.fr www.softeam.fr @alebagnato Coordinator Partners This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731946. http://www.cpswarm.eu

×