无人机自主飞行系统实验室

Aeronautics

&

AstronautAutonomous

Flight

SystemsLaboratoryAll

slides

and

material

copyright

ofUniversity

of

Washington

AutonomousFlight

Systems

LaboratoryAeronautics

&

AstronautAutonomous

Flight

SystemsLaboratoryResearch

and

Development

at

theAutonomous

Flight

Systems

LaboratoryUniversity

of

WashingtonSeattle,

WAGuggenheim

109,

AERB

214(206)

543-7748/research/afslReal

Time

Strategic

Mission

PlanningBaseObstacle/ThreatAvoidanceSearching/Target

IDCoordination

w/

surface

vehiclesAutonomous

Flight

Systems

Laboratory

Aeronautics

&

AstronautiPattern

hold/Team

assembly

TransitionUniversity

of

Washington3Aeronautics

&

AstronautiAutonomous

Flight

Systems

LaboratorySystem

OverviewPreviously

funded

by

DARPA

&

AFOSRUniversity

of

Washington4Aeronautics

&

AstronautiAutonomous

Flight

Systems

LaboratorySystem

Block

DiagramSolving

optimal

control

problems

in

real-timeUniversity

of

Washington5Stochastic

Problem

FormulationAutonomous

Flight

Systems

Laboratory

Aeronautics

&

AstronautiPredicted

probability

of

survival

of

each

vehicle

at

time

tq+1Predicted

probability

that

a

task

is

not

completedat

time

tq+1Team

utility

functionUniversity

of

Washington6Add

stuff

about

BAT

SIMDistributed

Architecture

forCoordination

of

Autonomous

VehiclesAutonomous

Flight

Systems

Laboratory

Aeronautics

&

Astronauti

Each

vehicle

plans

its

ownpath

and

makes

task

tradingdecisions

to

maximize

theteam

utility

function

There

is

one

activecoordinator

agent

at

a

timeefficiencyfailure

detection

local/global

informationexchanges

Computational

requirementfor

running

coordinator

agentis

small

compared

to

planning

Coordinator

role

can

betransferred

to

another

vehiclevia

a

voting

procedureUniversity

of

Washington7Evolution-based

Cooperative

PlanningSystem

(ECoPS)Autonomous

Flight

Systems

Laboratory

Aeronautics

&

Astronauti

Uses

Evolutionary

Computation-based

techniques

in

theoptimization

of

trading

decisionmakingandpath

planning

Task

planner

uses

price

andshared

information

in

addition

topredicted

states

of

the

world

formaking

trading

decisions

Task

planner

interacts

with

pathplanner

and

state

predictor

tosimultaneously

search

feasiblenear-optimal

task

and

path

plans.

We

call

this

system

the“Evolution-Based

CollaborativePlanning

System”

–

ECoPS,combining

market

basedtechniques

with

evolutionarycomputation(EC).University

of

Washington8Aeronautics

&

AstronautiAutonomous

Flight

Systems

LaboratoryEvolutionary

Computation

(EC)

Motivated

by

evolutionprocess

found

in

nature

Population-basedstochastic

optimizationtechniqueMetaphorMappingUniversity

of

Washington9Aeronautics

&

AstronautiUniversity

of

Washington10Features

of

Evolution-BasedComputationAutonomous

Flight

Systems

LaboratoryProvides

a

feasible

solution

at

any

timeOptimality

is

a

bonusDynamic

replanningNon-linear

performance

functionCollision

avoidanceConstraints

on

vehicle

capabilitiesHandling

loss

of

vehiclesOperating

in

uncertain

dynamic

environmentsTiming

constraintsAeronautics

&

AstronautiMarket-based

Planning

forCoordinating

Team

TasksAutonomous

Flight

Systems

LaboratoryTask

allocation

problem:At

trading

round

nThe

goal

of

task

trading:Each

vehicle

proposeswhich

are

approved

by

the

auctioneerbased

on

bid

price.At

the

end

of

the

trading

round:University

of

Washington11Distributed

Task

Planning

AlgorithmAeronautics

&

AstronautiAutonomous

Flight

Systems

LaboratoryDynamic

Path

Planning

Generate

feasible

paths

andplanned

actions

within

aspecified

time

limit

(ΔTs

)while

the

vehicles

are

inmotion.

Highly

dynamic

environmentrequires

a

high

bandwidthplanning

system

(i.e.small

ΔTs).

Formulate

the

problem

as

aModel-basedPredictiveControl

(MPC)

problemUniversity

of

Washington12Aeronautics

&

AstronautiAutonomous

Flight

Systems

LaboratoryEC-Based

Path

PlanningMutationDynamic

PlanningPath

EncodingUniversity

of

Washington13Aeronautics

&

AstronautiAutonomous

Flight

Systems

LaboratoryCollision

Avoidance

Model

each

site

in

the

environment

as

auncertainty

circular

area

with

radiusProbability

of

intersection:use

numerical

approximationcomputationally

easier

than

true

solution:

possible

intersection

region:

probability

density

field

function:

position

on

the

path

Ci

:

expected

site

locationv

:

velocity

of

the

vehicleUniversity

of

Washington14Aeronautics

&

AstronautiAutonomous

Flight

Systems

LaboratoryCollision

Avoidance

ExampleUniversity

of

Washington15Aeronautics

&

AstronautiAutonomous

Flight

Systems

LaboratorySimulation

ResultsSimulation

on

the

Boeing

Open

Experimental

PlatformUniversity

of

Washington16Some

Aspects

of

ECoPSUniversity

of

Washington17Autonomous

Flight

Systems

Laboratory

Aeronautics

&

Astronauti

Eachvehicle

computes

its

own

trajectory

and

makes

decisionto

trade

its

tasks

with

other

vehicles.Vehicles

may

sacrifice

themselves

if

that

benefits

the

team.

Each

vehicle

needs

to

have

periodically

updated

locations

ofnearby

vehicles

only

for

collision

avoidance.

Each

vehicle

needs

to

know

the

information

about

theenvironment.

The

accuracy

of

the

information

affects

thequality

of

its

decision

making.

The

rate

of

environment

information

updates

shou