cooperative air and ground surveillance

Cooperative Air and Cooperative Air and Ground Surveillance Ground Surveillance Wenzhe Li

OutlineOutlineIntroductionExperimental TestbedFramework Air-Ground coordinationExperiment ResultsConclusion

IntroductionIntroduction The use of robots in surveillance

and exploration is gaining prominence.

SurveillanceTarget detectionTrackingSearch and rescue operations

UAV and UGVUAV and UGVUAV(Unmanned aerial vehicle) Advantage: Move rapidly, Cover large area Disadvantage: Low accuracy for localizationUGV(Unmanned ground vehicle) Advantage: High accuracy for localization Disadvantage: Not move rapidly, can not

see through obstacles.Main idea arise from answering question : How to make it both Move rapidly and

Accurately locate target ?

Major topics covered in this Major topics covered in this paperpaper In this paper, authors present the approach

to cooperative search, identification, and localization of targets using a heterogeneous team of fixed-wing UAV and UGVs.

Three major topics.Synergy of UAVs and UGVsFrameworkAlgorithms to search and

localization

Contribution of paperContribution of paperFramework is scalable to multiple

vehicles.Decentralized Algorithms for

control of each vehicleEasy Implemented, independent

of number of vehicles, offer guarantee for search and localization

Before moving to next Before moving to next section…section…How to integrate UAVs and UGVs ?What UAVs and UGVs be

responsible for? (to exhibit complementary capability)

Why such framework is scalable to large system?

What techniques to use to solve problem?

……….

OutlineOutlineIntroductionExperimental TestbedFramework Air-Ground coordinationExperiment ResultsConclusion

UAV Airframe and PayloadUAV Airframe and Payload

◆ onboard embedded PC◆ IMU 3DM-G from MicroStrain◆ external global positioning system (GPS): Superstar GPS receiver from CMC electronics, 10 Hz data◆ camera DragonFly IEEE-1394 1024 × 768 at 15frames/s from Point Grey Research◆ custom-designed camera-IMU Pod includes theIMU and the camera mounted on the same plate.The plate is soft mounted on four points inside thepod. Furthermore, the pan motion of the pod can becontrolled through an external-user PWM port onthe avionics.

Ground StationGround StationEach UAV continuously communicate

with Ground Station Communication : 1hz, up to 6mi

Performs GPS corrections and Flight Update

Concurrently monitor up to ten UAVsDirect communication between UAVs

via Ground Station and 802.11bGround station has an operator

interface program

The UGV PlatformThe UGV Platform

OutlineOutlineIntroductionExperimental TestbedFramework Air-Ground coordinationExperiment ResultsConclusion

FrameworkFrameworkInformation-driven frameworkASN(Active sensor network)

architecture Key idea: sensing action -> reduction

in uncertaintyUtility on robot and sensor state and

actionsTarget DetectionTarget Localization

Target DetectionTarget DetectionCertainty Grid : our

representation certainty grid is a discretestate binary random field in which

each element encodes the probability of the corresponding grid cell being in a particular state

1. Yd,i(k|k) = logP(x) = logP(s(Ci) = target). where subscript d denotes detection, stores the accumulated

target detection certainty for cell i at time k 2. id,s(k) = logP(z(k)|x) Information associated with the likelihood of sensor measurements

z

3. Updated by the log-likelihood form of Bayes rule:

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Identify cells that have an acceptably high probability of containing features or targets of interest.

Target Localization Target Localization

Target Localization : Second part of task

Problem posed as a linearized Gaussian estimation problem

Kalman filter is used

Target Localization Target Localization Vector Yf : Coordinates of all the features

detected by the target detection algorithmYf,i : denoting the (x, y) coordinates of the

feature in a g lobal coordinate system Information filter maintains Yf,i(k | k) and matrix Yf,i(k | k)Estimation mean and covariance by

Fusion of Ns sensor measurements

Uncertainty Reducing Uncertainty Reducing ControlControlEntropy-based measure Mutual information measuresControl objective is to reduce estimate

uncertaintyUncertainty directly depends on the system

state and action Vehicle chooses an action that results in a

maximum increase in utility or the best reduction in the

uncertainty

Scalable Proactive Sensing Scalable Proactive Sensing NetworkNetworkCan be deployed for searching for targets

and for localizationSearch and localization algorithms are driven

by Information-based utility measures Independent of the source of the informationNodes automatically reconfigure themselves

in this taskScales to indefinitely large sensor platform

teams

OutlineOutlineIntroductionExperimental TestbedFramework Air-Ground coordinationExperiment ResultsConclusion

Air-Ground CoordinationAir-Ground Coordination

The search and localization task consists of two components:

1. First, detection of an unknown number of ground features in a specified search area ˆyd (k|k).

2. The refinement of the location estimates for each detected feature Yf,i(k|k).

FeFeaature Observation ture Observation UncertaintyUncertainty

Optimal Reactive Controller for Optimal Reactive Controller for LocalizationLocalizationController is a gradient control law, which

automatically generates sensing trajectories that actively reduce the uncertainty in feature estimates by solving:

where U is the set of available actions, and If,i(ui(k)) is the mutual information gain for the feature location estimates given action ui(k).

For Gaussian error modeling of Nf features

Optimal Reactive Controller for Optimal Reactive Controller for LocalizationLocalization

OutlineOutlineIntroductionExperimental TestbedFramework Air-Ground coordinationExperiment ResultsConclusion

Aerial images of the test site captured during a typical UAV flyover at 65 m altitude. Three orange ground features highlighted by white boxes are visible during the pass.

1. When only use UAVs : In excess of 50 passes (about 80 min of flight time)

2. When only use UGVs : In excess of half an hour for the ground vehicle

3. When they are collaborative: completes this task in under 10 min

OutlineOutlineIntroductionExperimental TestbedFramework Air-Ground coordinationExperiment ResultsConclusion

ConclusionConclusionUnique Features: 1. Methodology is transparent to the

pecificity and the identity of the cooperating

vehicles. 2. Computations for estimation and control are decentralized 3. Methodology presented here is

scalable to large numbers of vehicles.