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:
Screen clipping taken: 2010/3/29, 11:11
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.