Implementing Consensus based tasks with autonomous agents cooperating in dynamic missions using Subsumption Architecture

Transcription

1 Implementing Consensus based tasks with autonomous agents cooperating in dynamic missions using Subsumption Architecture Prasanna Kolar PhD Candidate, Autonomous Control Engineering Labs University of Texas at San Antonio 1

2 Outline Challenge Contributions Introduction Experimental Details Results and Discussions Conclusion Future Work Acknowledgements References 2

3 Challenge Develop a Robust Task Allocation & Task Execution unmanned system Architecture by integrating a decentralized task allocation system with a decentralized behavior task execution system We Integrated Consensus Based Bundled Algorithm(CBBA) with Subsumption Architecture(SA) Why? Modern day systems are network oriented Multi-application systems Need for proper coordination and collaboration to successfully execute missions Systems work in areas that are unknown or partially known environments 3

4 Contributions Development of an UAV System architecture by integrating CBBA with Subsumption Architecture Implemented the above contributions in simulation and on hardware Research and Development of Bound CBBA (BCBBA) which enables faster bidding up to 20% 4

5 Introduction.. Centralized systems Multiple child system transactions are handled by one leader system One main system handles most of the calculatio Others just follow its orders Advantages: powerful hardware for computation only in main system Sub systems can have inexpensive hardware Disadvantages: Single system decision making Single Point of Failure Heavy network traffic between main and sub systems Centralized System 5

6 Introduction.. De-Centralized systems All systems are decision makers using consensus Local information used to achieve global goals Advantages Faster and more up-to-date information Less network traffic No single Point of Failure Hence system continues even if some drones fail Disadvantages Complex logic for consensus strategies The drones may need more expensive hardware Well connected Decentralized system 6

7 Consensus Are we in agreement?!! Agent agreement Information sharing and auction Robust to network topologies Task Allocation Auction My bid is larger than yours! Calculate bids using predefined scoring logic Robust to inconsistencies in situation awareness Combine both? Task Selection use Auction Conflict Resolution use Consensus 7

8 CBBA Logic Task Selection Bundle the tasks Calculate Bid Choose Tasks that maximize the total score Conflict Resolution Each Agent communicates with its neighbor(s) Tasks are allocated to the highest bidder based on the agent communication Lose the bid? Release task and all subsequent tasks Re-execute if any task is not assigned an agent Our Environment worked with : 3 agents and 6 tasks 8

9 Traditional Intelligence Systems Vs Behavior Based Systems Traditional Intelligence Systems (Non-Behavior based architectures) Functional Decomposition Sequential processing of functionalities (could be slow) TASK EXECUTION! Traditional decomposition of a robot control system functional modules 9

10 Behavior Based System Subsumption Architecture Example Layered Architecture design, development starts at level 0 A parallel and distributed method for connecting sensors and actuators in robots Behavior based robot control system 10

11 Subsumption Steps for hardware implementation UAV is instructed to take survey & take pictures of a blue object and send it back Step 1: UAV takes off Step 2: Navigates towards target Step2: UAV senses an obstacle in: this case a Red object Starts the obstacle avoidance behavior and successfully avoids obstacle Step3 : UAV sensors do not detect an obstacle; UAV continues Step4: UAV sensors detect wifi is down (UAV starts wandering) CBBA is called and second UAV tracks the target Step5: UAV senses; WIFI is back up; and follows This continues until the target is reached Step6: Take pictures of target and send back to base 12

12 Problem Statement Autonomous Surveillance with BCBBA Ground Station Initiates Call to BCBBA Tasks and the associated agents list is sent back to Ground Station Subsumption Architecture module receives the list; starts the execution 13

13 Tasks executed based on the prioritization; Subsumption logic Autonomous Surveillance.. Contd. 14

14 Autonomous Surveillance.. Contd. If more tasks; execute from beginning; Process is terminated when all tasks are completed 15

15 Simulation Environment Operating system : Ubuntu Simulation software : ROS Hydro : Robot Operating System With autonomous packages for AR Drone For GUI Hector_quadrotor This System simulates the UAV s and their working environment. hector_quadrotor contains packages related to modeling, control and simulation of quadrotor systems Essential elements of ROS that we use: ROS Packages : container that houses all the elements below ROS Nodes : application codes that communicate and send information ROS Topics: Information channels that carry the data ROS msg: message containers/ variables that help info sharing between applications and also UAVs ROS Publishers, Subscribers : these are the consumers and broadcasters of information 16

16 Hardware Environment Processor and Memory Specifications: 1GHz 32 bit ARM Cortex A8 processor with 800MHz video DSP TMS320DMC64x Linux GB DDR2 RAM at 200MHz USB 2.0 high speed for extensions Wi-Fi b g n 3 axis gyroscope 2000 /second precision 3 axis accelerometer +-50mg precision 3 axis magnetometer 6 precision Pressure sensor +/- 10 Pa precision Ultrasound sensors for ground altitude measurement 60 FPS vertical QVGA camera for ground speed measurement Video Specifications: HD Camera. 720p 30FPS Wide angle lens : 92 diagonal H264 encoding base profile Low latency streaming Video storage on the fly with the remote device JPEG photo Video storage on the fly with Wi-Fi directly on your remote device or on a USB key Frame Specifications: Carbon fiber tubes : Total weight 380g with outdoor hull, 420g with indoor hull High grade 30% fiber charged nylon plastic parts Foam to isolate the inertial center from the engines vibration EPP hull injected by a sintered metal mold Liquid Repellent Nano-Coating on ultrasound sensors 17

17 Communication channels in ROS via topics CBBA node and subsumption architecture node are key here Nodes communicate with each other using ROS Topics CBBA node is started by the Ground Station; SA node subscribes to CBBA node through the integration system In the dynamic situation call, SA node places a call through integrated system to CBBA node during low battery, for a re-bid Similarly each drone can communicate via the non-node using topics 18

18 WiFi Down Simulation 19

19 WiFi Down Simulation 20

20 WiFi Down Simulation 21

21 WiFi Down Simulation 22

22 WiFi Down Simulation 23

23 WiFi Down Simulation 24

24 Simulation Results Battery Down Task Allocation system sends a stream of delimited text Integration system formats info (SA consumes it) 25

25 ASP Task Execution Take off All 3 Drones Take off Drone 3 is Lead Drone since it won Task Drone 1 and 2 follow 26

26 Drones Navigate to Target Drone 3 avoids an obstacle Drone 1 and 2 navigate and follow to target 27

27 Drone 3 battery low Drone 3 avoids obstacle 2 ; Battery Sensor gives low battery CBBA is run and info subscribed 28

28 Rebidding Call.. Drone 1 selection 29

29 Drone 1 New Task winner Drone 1 avoids an obstacle Drone 2 follows 30

30 Drone 1 low battery Sensors again give low battery on Drone 1 CBBA is run and info subscribed 31

31 Drone 1 low battery Rebidding call Re-bidding on BCBBA allocates task to Drone 2 32

32 Drone 2 new winner navigates to target Drone 2 successfully navigates to Target Drone 2 takes picture of Target & sends to base All Drones get back to base 33

33 Drone 2 new winner - picture taken Drone 2 takes picture of Target & sends to base 34

34 All Drones - return back to base All drones return to base 35

35 Simulation Analysis Inter Module communication CBBA Bid call is processed without delay Automatic CBBA call for Rebid based on sensor readings Subsumption Architecture executes task as soon as the task is allocated Intra Module (Drones) communication Each drone is able to communicate with each other 36

36 System Analysis Pros : The new system can exploit the distributed/decentralized nature of these 2 systems; CBBA guarantees convergence Parallel processing; quick reaction in Subsumption Cons : No state retention in the traditional Subsumption Architecture Coding for memory in the programs will help Program designer should have a good system knowledge to program for the unforeseen anomalies 37

37 BCBBA vs. CBBA BCBBA Pros: Computational efficiency by about 20% Flexible : Leader drone can split tasks during execution BCBBA Cons: Task split may require computation time 38

38 Conclusion Objectives : Develop an Unmanned Aerial System integrating a decentralized task allocation system using a Consensus based system& task execution system using Subsumption Architecture Simulate and Implement de-centralized strategy for Surveillance with Dynamically changing Situations Both these objectives have been accomplished in this thesis 39

39 Future work Current system did not have an offboard computer (like odroid), we used external computers for this processing. Use of systems like odroid xu4, raspberry pi These can be integrated onto a more powerful drone. Eg: 3dr x8, iris, solo with odroid systems, raspberry systems. We have successfully integrated a 3dr pixhawk system with an odroid XU4 system, to enable complete autonomy. We had previously used default ROS messages Using broadcasting systems like LabStreamingLayer(LSL), we can communicate various different message types. We plan on building decentralized systems in the future using these technologies 40

40 References 1. Brooks, A Robust Layered Control System for a Mobile Robot, Robotics and Automation, IEEE Journal of; Mar 1986, pp , vol. 2, issue 1 2. H.-L. C. a. J. P. H. Luc Brunet, "Consensus-Based Auction Approaches for Decentralized Task Assignment," in AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, Hawaii, A. Whitten, H.-L. Choi, L. Johnson and J. How, "Decentralized task allocation with coupled constraints in complex missions," in American Control Conference (ACC), M. A. a. J. P. How, "Robust Decentralized Task Assignment for Cooperative UAVs," in AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, Colorado, Wikipedia.org 6. ROS.org 41

41 Questions?? Please feel free to ask questions 42

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