Cooperative air and ground surveillance
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Cooperative Air and Ground Surveillance. Wenzhe Li. Outline . Introduction Experimental Testbed Framework Air-Ground coordination Experiment Results Conclusion. Introduction. The use of robots in surveillance and exploration is gaining prominence. Surveillance Target detection

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Outline
Outline

  • Introduction

  • Experimental Testbed

  • Framework

  • Air-Ground coordination

  • Experiment Results

  • Conclusion


Introduction
Introduction

The use of robots in surveillance and exploration is gaining prominence.

  • Surveillance

  • Target detection

  • Tracking

  • Search and rescue operations


Uav and ugv
UAV and UGV

  • UAV(Unmanned aerial vehicle)

    Advantage: Move rapidly, Cover large area

    Disadvantage: Low accuracy for localization

  • UGV(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 paper
Major topics covered in this paper

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 UGVs

  • Framework

  • Algorithms to search and localization


Contribution of paper
Contribution of paper

  • Framework is scalable to multiple vehicles.

  • Decentralized Algorithms for control of each vehicle

  • Easy Implemented, independent of number of vehicles, offer guarantee for search and localization


Before moving to next section
Before moving to next 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?

  • ……….


Outline1
Outline

  • Introduction

  • Experimental Testbed

  • Framework

  • Air-Ground coordination

  • Experiment Results

  • Conclusion


Uav airframe and payload
UAV 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 15

frames/s from Point Grey Research

◆ custom-designed camera-IMU Pod includes the

IMU and the camera mounted on the same plate.

The plate is soft mounted on four points inside the

pod. Furthermore, the pan motion of the pod can be

controlled through an external-user PWM port on

the avionics.


Ground station
Ground Station

  • Each UAV continuously communicate with Ground Station Communication : 1hz, up to 6mi

  • Performs GPS corrections and Flight Update

  • Concurrently monitor up to ten UAVs

  • Direct communication between UAVs via Ground Station and 802.11b

  • Ground station has an operator interface program



Outline2
Outline

  • Introduction

  • Experimental Testbed

  • Framework

  • Air-Ground coordination

  • Experiment Results

  • Conclusion


Framework
Framework

  • Information-driven framework

  • ASN(Active sensor network) architecture

  • Key idea: sensing action -> reduction in

    uncertainty

  • Utility on robot and sensor state and actions

  • Target Detection

  • Target Localization


Target detection

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

Target Detection

  • Certainty 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:

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 localization1
Target Localization

  • Vector Yf : Coordinates of all the features detected by the target detection algorithm

  • Yf,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 control
Uncertainty Reducing Control

  • Entropy-based measure

  • Mutual information measures

  • Control objective is to reduce estimate uncertainty

  • Uncertainty 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 network
Scalable Proactive Sensing Network

  • Can be deployed for searching for targets and for localization

  • Search and localization algorithms are driven by

    Information-based utility measures

  • Independent of the source of the information

  • Nodes automatically reconfigure themselves in this task

  • Scales to indefinitely large sensor platform teams


Outline3
Outline

  • Introduction

  • Experimental Testbed

  • Framework

  • Air-Ground coordination

  • Experiment Results

  • Conclusion


Air ground coordination
Air-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).


Fe a ture observation uncertainty
Feature Observation Uncertainty


Optimal reactive controller for localization
Optimal Reactive Controller for Localization

  • Controller 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 Nffeatures



Outline4
Outline

  • Introduction

  • Experimental Testbed

  • Framework

  • Air-Ground coordination

  • Experiment Results

  • Conclusion


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


Outline5
Outline of flight time)

  • Introduction

  • Experimental Testbed

  • Framework

  • Air-Ground coordination

  • Experiment Results

  • Conclusion


Conclusion
Conclusion of flight time)

Unique 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.


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