PEA Action / 2014

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Eugenia Chambers
6 years ago
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Cooperation of multiple heterogeneous vehicles PEA Action - 2007 / 2014 The goals of PEA Action Investigate the available means and design future technologies

agency DGA MI (Maîtrise de l'information) Follow-up: DGA TT (Techniques Terrestres) et DGA TN (Techniques Navales) Localization of: friends, foes, targets,

1 Cooperation of multiple heterogeneous vehicles PEA Action / 2014 The goals of PEA Action Investigate the available means and design future technologies in order to upgrade the performance of the localization function within a network of heterogeneous autonomous vehicles Funding: French Defence procurement agency DGA MI (Maîtrise de l'information) Follow-up: DGA TT (Techniques Terrestres) et DGA TN (Techniques Navales) Localization of: friends, foes, targets, landmarks, obstacles GPS-independent localization means, as much as possible Scientific experiments involving multiple uninhabited vehicles 2 An overview of PEA Action 1

2 Project team ONERA - The French Aerospace Lab Contact: Magali Barbier (Magali.Barbier@onera.fr) DCSD and DTIM departments: DCSD: Systems control and Flight Dynamics department Research Units CD and CDIN CD: Decision and Control CDIN: Control and Integration DTIM: Modelling and Information processing department Research Unit EVS: Estimation and Vision LAAS/CNRS Laboratory for Analysis and Architecture of Systems / French National Centre for Scientific Research Contact: Simon Lacroix (Simon.Lacroix@laas.fr) Robotics and Interactions RIS, Robotics, Action and Perception RAP groups 3 An overview of PEA Action Project schedule Consolidation (C.) and Experiment (E.) phases C. I à IV E. II et IV C. V et VI E. I et III E. V et VI Scenarios I, III, V and VI: air-ground Scenarios II and IV: air-sea 4 An overview of PEA Action 2

3 Six scenarios 4 air-ground scenarios, from 2 to 12 vehicles (AAV and AGV) Infrastructure surveillance mission: localization and tracking of non-cooperative targets Growing complexity: areas, targets, disruptive events AAV Autonomous Aerial Vehicle AGV Autonomous Ground Vehicle 5 An overview of PEA Action Six scenarios 2 air-sea scenarios, with 2 and 3 vehicles (AAV, AUV, ASV) Securing mission Fight against water pollution mission AAV Autonomous Aerial Vehicle AUV Autonomous Underwater Vehicle ASV Autonomous Surface Vehicle 6 An overview of PEA Action 3

Decision making: planning, supervision Project management and quality State of the art Publications and workshop organization Scenario

4 Vehicles for scientific demonstrations VTOL Yamaha RMax ONERA-ReSSAC AAV Dala, Mana Laas/CNRS On-going selection of other vehicles An overview of PEA Action 7 PEA Action subjects Scientific subjects 8 Multivehicle architecture Cross-disciplinary subjects Data fusion Decision making: planning, supervision Project management and quality State of the art Publications and workshop organization Scenario consolidation Simulation Specification and vehicle selection Experiments Web Site: An overview of PEA Action 4

5 Approach / Scientific subjects From Onera and Laas State of the art And from literature state of the art Focus on AxV cooperative teams Autonomy is shared with human operator Simulations, experiments and demonstrations Incremental approach: scenarios with growing complexity 9 An overview of PEA Action Scientific work Data fusion Data processing for the localization function SLAM (Simultaneous Localization and Mapping) of vehicles, targets and landmarks Layered environment modeling Tracking of targets Planning Plan = sequence of actions or policy Coordindation for cooperation: rendez-vous, communication Offline preparation and online repair or replanning Supervision Online control of the execution of planned actions Reactions trigger depending on current and predicted situations Execution of elementary actions: move, perception, communication, replanning 10 An overview of PEA Action 5

6 Scientific work Decisional architecture Data Data fusion State of vehicles, environment, targets Situation tracking High level knowledge of system state Mission Supervision Planning Moves, Perceptions, Communications control data 11 An overview of PEA Action Evaluation Simulations Blender-based simulation architecture Vehicle in the loop (hybrid) Experiments Detection Classification Identification and Localization DCI : emulation or simulation 12 An overview of PEA Action 6

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