1 Multi-agent Collaborative Flight Experiment Karl Hedrick UC Berkeley.
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Transcript of 1 Multi-agent Collaborative Flight Experiment Karl Hedrick UC Berkeley.
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Multi-agent Collaborative Flight
Experiment
Karl HedrickUC Berkeley
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CIRPAS, Camp Roberts, CA
Operated by the Naval Post Graduate School
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Collaborative UAV Flight Test
GOALS of the August, 2006 Experiment Thrust 1. Distributed collaboration with limited communications a.) multi-vehicle, multi-step task allocation b.) limited ground-air and air-air com c.) user task cancellation/reallocation d.) agent created tasks e.) task prioritization f.) no fly zone filter and re-planner g.) Simple human/UAV team interface
Thrust 2. Vision-based river following.
a.) Ability to identify and search for the desired structure (river).
b.) Ability to accurately track the river once identified.
c.) Ability to accurately map the boundaries of the river.
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Collaboration Research Goals In General
Study distributed mechanisms for the collaboration of Unmanned Aerial Vehicles (UAV)
Generalize a large number of missions under one framework• Surveillance/Mapping • Border Patrol• Search & Rescue• Convoy Protection, etc.
Emphasis on Robustness rather than Optimization:• Addition and Deletion of Tasks• Addition and Removal/Failure of Agents• Limited and/or Failed communication
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C3UV Collaboration Software
GOALS•Transmit desired mission from user to agents•Provide user with fused information from agents•Decompose and assign tasks among agents in response to dynamic mission definition •Accomplish tasks in an efficient and robust manner
Agent in range of user
Agent out of range
User
New tasksCancel tasks
Command station
Mission state est. Mission state estimate
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Communication Infrastructure
User
New tasksCancel tasks
Command station
Piccolo Groundstation
Piccolo Autopilot
PC104
Piccolo Autopilot
PC104
900 MHzradio
2.4 GHzethernet
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The Mission
User defines mission in terms of tasks
Philosophy“The user specifies what he or she would like accomplished.
The system decides how to do so efficiently.”
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Mission Commander Interface
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Graphical User Interface
C:\Documents and Settings\Sonia\Desktop\CommanderSDK.exe
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Monitor Area Task
Task details
Goal Detect any velocity-bounded intruder that cannot leave a defined boxTrajectory UAV path depends on maximum speed of intruder; path
becomes a “lawnmower” trajectory if maximum speed is zero
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Guaranteed Search Task
Goal Detect any intruder traveling from a known start point with a bounded velocity
Simulation Result 500 of 500 intruders detected in simulation
Task details
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Avoiding a no fly zone: active filter
Task Controller
No FlyZone Filter
Autopilot
Desired autopilotcommand
Safe autopilotcommand
No fly zone
Visit task
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BLCC- Berkeley “Language” for Collaborative Control
Define the mission and communicate it to team members
Define the “state” of each agent
Define the mission “state”
Allow for faults
Allow for conflict resolution
Define the information to be communicated between agents.
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Agents (UAVs)
Transition Logic: Governs transitions of tasks and subtasks
Communication: Deconflicts plans and synchronizes information between agents vs.
Planner(ex. path-planner): calculates cost, generates plan and chooses “todo”
Low-level Controller (ex. waypoint tracker)
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Task-Point List
Every process and each agent communicates primarily through the task-point list
A task-point list exists for each task and is manipulated by each process to generate a desired mode/task/mission
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Real-Time Task Allocation Algorithms Multiple agents/Multiple Tasks
Emphasis on real-time computation and robustness to communication limitations and agent failure.
Recent progress on optimal and sub-optimal algorithms
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Task Allocation
Given n UAVs and m tasks, how do we assign tasks to UAVs?• Assume that each task is simply a point to be visited, with some time spent at
that point. • Neglect UAV turn rate constraints – assume constant velocity• For each UAV, let a tour be an ordered set of targets that it will visit• Let the cost of tour be the total time required to complete. For a constant
velocity UAV with no turn rate constraint, this time corresponds to distance.
Often this is posed as an instance of the multiple traveling salesman problem
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Multiple Traveling Salesman
The Multiple Traveling Salesman Problems focuses on minimizing total cost. For n UAVs, with the cost of a tour for UAV j = Tj
Our problem differs: we should focus on minimizing the max cost of any tour• Given that we’re working with constant velocity UAVs, the cost in fuel
of having a UAV circle is the same as having it do some work. • For our problem, this corresponds to a minimum clock time problem.
This problem is often referred to as the min-max Vehicle Routing Problem.
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Min-Max Vehicle Routing
The min-max Vehicle Routing Problem is NP hard. There is no polynomial time solution.
We’d like to develop algorithms find near-optimal solutions to the min-max problem.
In practice, we’d like to build algorithms that are robust to communication losses; with perfect communication, we’d like these algorithms to achieve near optimal performance.
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The Greedy Algorithm
In constructing a tour, let the UAV with the lowest cost function for its partial tour choose the next task.
This algorithm leads to balanced tours among UAVs: all UAVs perform tours of roughly equal cost. • For the min-max VRP, optimal solutions will contain tours balanced to within
the maximum distance between any two tasks.
This is a fast algorithm that creates balanced tours
Sub-optimal
2 questions:• How well does this work?• How do we implement this in a distributed system with limited
communication?
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Approximate Multi-step Distributed Min-Max Vehicle Routing Algorithm
0 Communication and 1 Computation
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Example continued…
1 Communication and 1 Computation
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Example continued…
1 Communication and 2 Computation
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Example continued…
2 Communication and 2 Computation
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Collaboration Architecture
Mission State
Estimate MSEA
Internal State
ISA
Information Base
Communication
Computation
BroadcastReceive
Decision
Functions
Choose
RedoFault
Complete
MSEAMSEB
Integrates MSEA
and MSEB
Retains most up-to-date information
Agent A
User Plan
Task Execution
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How we implement in software
PiccoloInterface Datahub
PayloadInterface
Controllers
A2A Wireless
Collaboration Sensors
Telemetry
Aircraft Commands
High Level Telemetry
User Commands
SensorData
Collaboration info
AC Commands
MissionPlan
Mission Plan
Task Execution
Safe FlightController
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Sig Rascal 110 airframe
Balsa frame remote control aircraft kit with 110” wingspanModifications:•32 cc gasoline engine with vibration isolation mounts•Dual fuel tanks for 60 min flight time•Carbon fiber reinforcement to support payload•26 lb takeoff weight•Piccolo avionics system
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Berkeley Air Force
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PC104 stack and payload tray
•PC104 with 700 MHz Pentium III processor•2 GB flash memory (16 GB on vision plane)•Bidirectional 1 Watt amplifier for 802.11b communication•Vibration isolating suspension•Wireless analog video transmitter
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UC Berkeley UAV Platform: Autopilot, HIL Sim
Low Level Guidance and Control Provided by Cloud Cap Technology’s Piccolo Avionics Module and Corresponding Ground Station System
Capable of operating multiple vehicles. C3UV has successfully flown four vehicles simultaneously.
Wireless LinkActuators and
Sensors Signals
Piccolo Avionics
Ground Station
Ground Station Computer
Simulated States
Hardware In Loop (HIL) Simulation
Computer
Manual Control Console
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UC Berkeley UAV Platform: Communications
Three Communications Channels:
Autopilot Avionics and Ground station: 900 MHz
Plane to Plane communication: 2.4 GHz
Video downlink: 1.2 GHz
Ground Station
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Piccolo avionics system
PiccoloGround station
900 MHz UHF radio“Piccolo channel”
Autopilot commands and telemetry
Piccolo Operator Interface•Send waypoint commands•Monitor aircraft physical state
Piccolo Piccolo
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User interfaces and Piccolo payload channel
PiccoloGround station
900 MHz UHF radio“Piccolo channel”
Autopilot commands and telemetry“Payload channel”
Communication between PC104s and GUIs
Piccolo Operator Interface•Send waypoint commands•Monitor aircraft physical state
Piccolo Piccolo
PC104 PC104
Process Monitor Interface•Activate or idle processes•Monitor process states
Mission Commander Interface•Create tasks•Monitor status of tasks and UAVs
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Aircraft to aircraft communication
Piccolo Piccolo
PC104 PC104
2.4 GHz 802.11b ad-hoc wireless ethernetMission state estimates
Task allocation data
Piccolo
PC104
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Preview of collaboration experiment - Logistics
Mission Commander InterfaceVideo downlink from aircraft
Piccolo Operator InterfaceRead-only display of Mission Commander Interface
Process Monitor Interface
Mission Commander Tent Status Monitor Tent
Aircraft launch process1. Aircraft takes off under manual control2. Pilot gives control to Piccolo autopilot- aircraft flies on preloaded waypoint loop3. Onboard software is activated using Process Monitor Interface
** At this point the aircraft is controlled only via the Mission Commander Interface.All other personnel serve only as monitors.**
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UC Berkeley UAV Platform: All Together
Wing-Mounted Camera
Onboard computer and wireless communication radio
GPS Ant. Analog Video TX’s
Air-Ground UHF Ant.
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August ONR Demonstration
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LESSONS LEARNED Initially Air-Air com was so bad that mission performance was unacceptable.
Improved amplifier/antenna combined with robust architecture allowed us to accomplish complex missions successfully.
Ground-Air link needs to have a higher bandwidth for human/UAV interaction.
Required computation including task allocation and vision processing can be done on a Pentium III.
3 UAV’s with 4-5 tasks is already too complex for humans without autonomy software.
Complex interaction between task allocation and priority system needs further analysis.
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The End