implementing consensus based tasks with autonomous agents...
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The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 1
Implementing Consensus based tasks with autonomous agents cooperating in dynamic
missions using Subsumption Architecture
Prasanna Kolar PhD Candidate,
Autonomous Control Engineering LabsUniversity of Texas at San Antonio
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 2
Outline
Challenge
Contributions
Introduction
Experimental Details
Results and Discussions
Conclusion
Future Work
Acknowledgements
References
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 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 collaborationto successfully execute missions
• Systems work in areas that are unknown or partially known environments
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 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%
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 7
Consensus – Are we in agreement? !!
• Agent agreement
• Information sharing and auction
• Robust to network topologies
Auction – My bid is larger than yours!
• Calculate bids using predefined scoring logic• Robust to inconsistencies in situation awareness
Combine both?
Task Selection – use AuctionConflict Resolution – use Consensus
Task Allocation
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 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 tasksCopyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 9
Traditional Intelligence Systems Vs Behavior Based Systems
Traditional Intelligence Systems (Non-Behavior based architectures)
• Functional Decomposition
• Sequential processing of functionalities (could be slow)
Traditional decomposition of a robot control system – functional modules
TASK EXECUTION!
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 12
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 offStep 2: Navigates towards targetStep2: UAV senses an obstacle in: this case a Red object
Starts the obstacle avoidance behavior and successfully avoids obstacleStep3 : UAV sensors do not detect an obstacle; UAV continuesStep4: UAV sensors detect wifi is down (UAV starts wandering)
CBBA is called and second UAV tracks the targetStep5: 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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 13
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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 14
Autonomous Surveillance .. Contd.
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Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 15
Autonomous Surveillance .. Contd.
If more tasks; execute from beginning;Process is terminated when all tasks are completed
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 16
Simulation EnvironmentOperating system : Ubuntu 12.04Simulation software : ROS Hydro : Robot Operating SystemWith autonomous packages for AR Drone For GUI – Hector_quadrotorThis 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 informationCopyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 17
Hardware EnvironmentProcessor and Memory Specifications:
1GHz 32 bit ARM Cortex A8 processor with 800MHz video DSP
TMS320DMC64x
Linux 2.6.32
1GB 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
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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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 19
WiFi Down Simulation
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 20
WiFi Down Simulation
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 21
WiFi Down Simulation
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 22
WiFi Down Simulation
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 23
WiFi Down Simulation
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 24
WiFi Down Simulation
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Simulation Results – Battery Down
Integration system formats info (SA consumes it)
Task Allocation system sends a stream of delimited text
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ASP Task Execution – Take off
• All 3 Drones Take off• Drone 3 is Lead Drone since it won Task• Drone 1 and 2 follow
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Drones Navigate to Target
• Drone 3 avoids an obstacle
• Drone 1 and 2 navigate and follow to target
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Drone 3 – battery low
• Drone 3 avoids obstacle 2 ; ‘Battery Sensor’ gives low battery
• CBBA is run and info subscribed
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Rebidding Call.. Drone 1 selection
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Drone 1 – New Task winner
• Drone 1 avoids an obstacle
• Drone 2 follows
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Drone 1 – low battery
• Sensors again give low battery on Drone 1
• CBBA is run and info subscribed
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Drone 1 – low battery – Rebidding call
• Re-bidding on BCBBA allocates task to Drone 2
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 33
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
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Drone 2 new winner - picture taken
• Drone 2 takes picture of Target & sends to base
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All Drones - return back to base
• All drones return to base
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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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 37
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
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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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 39
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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 40
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
Copyright Prasanna Kolar, UTSA
The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 782499/2/2016 41
References
1. Brooks, “A Robust Layered Control System for a Mobile Robot”, Robotics and Automation, IEEE Journal of; Mar 1986, pp. 14 – 23, 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, 2008
3. A. Whitten, H.-L. Choi, L. Johnson and J. How, "Decentralized task allocation with coupled constraints in complex missions," in American Control Conference (ACC), 2011.
4. M. A. a. J. P. How, "Robust Decentralized Task Assignment for Cooperative UAVs," in AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, Colorado, 2006.
5. Wikipedia.org 6. ROS.org
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Questions ??
Please feel free to ask questions
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