Intelligent and Autonomous UAVs using Multi-Agent Systems

Transcription

1 Seminários de Sistemas Inteligentes, Interacção e Multimédia Intelligent and Autonomous UAVs using Multi-Agent Systems Prof. Rosaldo Rossetti Lúcio Sanchez Passos Zafeiris Kokkinogenis Gustavo Oliveira Rúben Veloso

2 Outline Part I Introduction Part II Related Works Part III Overall Architecture Part IV The Proposed Approach Part V Research Schedule Part VI Possible Projects

3 Part I Introduction

4 Unmanned Vehicles for Civil Applications Research Scope UUVs UGVs UAVs Symbiotic Simulation Design & Analysis Decision-making USVs

5 Research Problem Proliferation of UAV s in civil applications to substitute human in hazardous situations The efforts of the research community unfold in different directions. Among them: Autonomy in taking actions Higher level of reasoning Coordination of multiple units toward to a common goal Embedded agent architectures Improved control schemes Localization and mapping under constrained of computational resources This work focuses on the first 4

6 Motivation and Research Aim Our motivations Multi-Agent system are used in the robotic field Usually UAV for outdoor applications doesn t manifest high degree of decision-making capabilities Existing UAV application (indoor) use expensive infrastructures Lack of a framework that supports the development of agents for aerial robots Main goal of the proposed research is to specify, devise and implement a quadcopter framework featuring the necessary mechanisms to develop agents in a higher level that can be tested in real and virtual scenarios.

7 Part II Similar Projects

8 Civil Applications Figure 1: ptti.ipn.pt/news_items/7 Figure 2: Figure 3: penngazetteblog.com/2011/01

9 Quadcopters Occasional Projects Name Entity Advantages Disadvantages OS4 EPFL Many controller options; Highly detailed dynamics model; No autonomous flight; Expensive; N/A DTIC Low cost; Testbed for research; No OS on-board; No autonomous flight; N/A MIT Virtual simulation; Stabilization recovery; No autonomous flight; Does not support 6 degrees of freedom; Microraptor Oakland University Camera; Some degree of autonomy; Uses too many sensors; Autonomy dependent on GPS signal; RAVE ESTIA LIPSI Tracking algorithm; N/A Standford University Ability to perform aggressive maneuvers; Gas powered; No real autonomy; No autonomous flight;

10 Quadcopters Recurring Projects FMA at Zurich Institute of Technology Google IO 2012, a quadcopter trio throwing and catching a ball during the talk. GRASP Lab at UPenn YouTube clip showing a quadcopter doing aggressive and acrobatic manoeuvres.

11 Part III Overall Architecture

12 Overall Architecture

13 Part IV Physical System

14 Physical Architecture

15 Conceptual Architecture

16 Conceptual Architecture

17 Why the OpenPilot CC3D?

18 OpenPilot Features Our motivations Agents are being used in the robotic field Lack of a framework that supports the development of agents for aerial robots Main goal of the proposed research is to specify, devise and implement a quadcopter framework featuring the necessary mechanisms to develop agents in a higher level that can be tested in real and virtual scenarios.

19 Robotic Operating System (ROS) ROS is a flexible framework for writing robot software Communications Infrastructure publish/subscribe anonymous message passing recording and playback of messages request/response remote procedure calls distributed parameter system Robot-Specific Features Robot Description Language Diagnostics Pose Estimation, Localization, Mapping, Navigation Tools Command Line / GUI rviz rqt

20 Part V Simulator System

21 Simulator Architecture

22 Conceptual Architecture Simulador Quadcopter Virtual Comunicação UAVtalk Ligação Simbiótica Ambiente

23 Quadcopters Occasional Projects Simulators of UAVs Mixed Reality Simulators Flight Simulation USARSim JOCosim Co-simulators SPEEDES Webots OFFIS SimLink Virtual Reality Toolbox Microsoft Robotics Studio simulator TTMC Piccolo Gazebo IntelWheels (LIACC/FEUP) Microsoft Flight Simulator X-Plane FlightGear

24 Architecture To specify, design and test e simulation platform to support the design and development of autonomous quadcopters Lack of platforms for modeling and simulation of realistic 3d environments for multiple teams of quadcopters Lack of platform that integrate a real system with a simulated one in a trasparent way The associated cost for the implementation of real quadcopters is too high. A validate model of a quadcopter can help dvelopers implement their quad-based application in a safe environment. Through the simulation platform we can control more than one quadcopters without human intervantion Implementation of cordination/cooperation strategies for quadcopter teams in civil application scenarios can be performed and tested without a great amount of founds.

25 Architecture This project considers the design of applications in the civil domain It specifies a quadcopter s meta-architecture It specifies the physics of the environment It implements a simulation platform It specifies a communication protocol It specifies and implements the mechanisms for symbiotic simulation

26 Prototype

27 Jason/AgentSpeak platform AgentSpeak is an agent-oriented logic-based language used to program multi-agent systems and complex agent behaviour. Jason is an open-source java-based interpreter for an extended version of the AgentSpeak Jason/agentspeak offers the possibility to program complex behaviour with high-level abstractions

28 J3M features MonkeyEngine is a java-based 3D game engine Free, open-source software Minimal adaptations for cross-compatibility Built around a shader-based architecture jmonkeyengine SDK Complete and modular Bullet, a Java port of the Bullet Physics library Supports multiple Formats

29 Part VI Possible Projects

30 Real Component Apply operational research and machine learning approach to tune the PID control in real time; Integrate IR and sonar sensor to the actual control; Implement a communication module with mobile devices for monitoring and control; Implement a communication module between UAVs Implement SLAM approach Implement embedded agent to control the UAV

31 Virtual Component Development of coordination/cooperation mechanism for solving complex tasks Implement a proper ontology and a communication protocol. Implement a more detailed model of the Quadcopter Implement other autonomous vehicles in the simulator

32 Thank You! 32