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Sensing Uncertainty and the Role of Constrained Actuation Aman Kansal.

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Presentation on theme: "Sensing Uncertainty and the Role of Constrained Actuation Aman Kansal."— Presentation transcript:

1 Sensing Uncertainty and the Role of Constrained Actuation Aman Kansal

2 Overview Problem Sensing medium is anisotropic Phenomenon moves, environment changes Existing Approach Provide expected coverage for assumed deployment Add still more sensors NIMS ApproachStrategically position for actual deployment environment Reconfigure as per run time dynamics Sensor network performance: quality of information returned by it

3 Contributions 1.Develop models for realistic sensing –Going beyond the circular disc model 2.Develop platforms for evaluating sensing coverage with real sensors and real world sensing media 3.Use actuation based system reconfiguration for estimating/improving sensor network coverage and uncertainty –plan additional resources –provide decision confidence

4 Modeling the Sensing Process Phenomenon of Interest Anisotropic Environmental Attenuation Noise in Transducer Electronics Compression Loss Fidelity of data depends on multiple factors

5 Measuring Sensing Uncertainty Model reality closely –Existing work assumes artificial sensing models Circular range model Consider resolution of coverage Assess coverage due to multiple sensors –Existing work considers degree of coverage: may not model application requirement Sensing radius Coverage degree 1 Coverage degree 2

6 Sensing Uncertainty: distortion in reconstructed phenomenon –Raw sensor reading not of interest With multiple sensors: distortion in joint reconstruction X 1 i L … Sensors Fusion Center Phenomenon Joint Reconstruction Chen et al, IEEE JSAC’04 Measuring Sensing Uncertainty

7 Model the sensor field as a stochastic process with autocorrelation function R(x 1,y 1,x 2,y 2 ) = R x Model sensing noise as another stochastic process with autocorrelation function R N. –Sensing medium anisotropies and attenuation affect sensing SNR

8 Propagation Matrix Denote H to be the propagation matrix Phenomenon + N1N1 Y1Y1 h 1j XLXL + N1N1 Y1Y1 h Lj X1X1 Estimation [X 1,…,X L ] Sensor Readings + N2N2 Y2Y2 h 2j … X2X2

9 Information Theoretic Bound Expression derived for actual H and R N Optimal Rate-Distortion relationship Distortion Rate Low Noise High Noise

10 Reduce Sensing Uncertainty Higher Costs: cannot deploy densely Cannot handle occlusions Precision requires higher energy, bandwidth May need very high density to guarantee coverage in arbitrary environment Finite communication bandwidth shared by more sensors: per sensor share falls Intrusive: interferes with phenomenon Higher precision transducers Higher density deployment CONFLICTING Transducer noise,  N Medium Anisotropies, H

11 Use Actuation Need better quality information instead of more bits Actuation can achieve: –Higher fidelity without high density Move towards phenomenon to enhance sensing SNR Adapt to specific deployment scenario –Adaptation to run time dynamics Growth of foliage, movement of phenomenon, presence of mobile occlusions (animals)

12 Use Actuation Challenges: –Accurate localization and navigation is resource and power intensive –Uncertainty due to changing sensor position –Energy overhead Solution: Low Complexity Actuation –Small motion on assisted tracks –Pan, tilt, zoom capabilities –Virtual Mobility: changing active and inactive nodes Sensor Node Traction Platform Track

13 Intuition: Small Actuation Helps Coverage area increases Multiple perspectives feasible Adapt to medium and phenomenon changes Uncovered Area Covered Area Uncovered area Sensor

14 Intuition: Small Actuation Helps 20 25 30 35 Distance to obstacle, x 3000 2500 2000 1500 1000 500 Reduction in occluded Area, % l 2l l x

15 Simulating Multiple Obstacles Assume multiple small aspect ratio obstacles Single camera moves a small multiple of mean obstacle diameter Obstacles distributed uniformly randomly Obstacles Sensor

16 Simulation Results Results averaged over 20 random topologies Changing Obstacle Size Changing Obstacle Density Percentage Gain due to mobility 100 300 500 l average D move /l average 1 2 5 10 15 20 Coverage Fraction Obstacle Density 1 static Mobile (l) Mobile (2l)

17 Laboratory Experiments with Image Sensors Constructed a system of four cameras and a square field with obstacles Image processing used on noisy camera output to detect target –Constant lighting conditions Measured detection probability by moving a target around the field

18 Laboratory Set-up Obstacle placement models tree locations in an example forest (WindRiver Canopy Research Facility) Camera Movement

19 Experiment Results 1 2 34 Number of Cameras 10 -1 Probability of Mis-detection 10 0 Static Mobile Target

20 Real World Experiments In woods near UCLA (near Sunset Rec.) Arbitrary obstacle shape and size Lighting conditions no longer constant Sensor noise increased –Sensor not designed for outdoor usage and imaging in sunlit conditions Detection measured in 12’x12’ region, Motion range = 2’ from mean position

21 Real World Experiments Constrained motion (small multiple of mean obstacle size) helps reduce sensing uncertainty Camera ModePoMGain(%) STATIC0.3885- Move (1 direction) 0.2374163.65 Move (2 directions) 0.1942200.05

22 Pan-Tilt-Zoom Volume Coverage Evaluating coverage gain in volume for a commercial sensor (Sony SNCrz30N) ActuationGain (covered volume with actuation / static coverage) Pan7.74 Zoom73 Pan and Tilt 27.7 Pan and Zoom 6363 Pan, Tilt, Zoom 226940

23 Virtual Mobility Node ID insignificant: deactivating one node and activating another is same as relocation of a sensor Higher node deployment density required to enable migration to sufficient locations for coverage Motion delay can be made very small Multiple simultaneous nodes can be activated for special events

24 Managing Actuation GOALS: –Generate optimal actuation commands and sensor placements to minimize sensing uncertainty –Coordinate the actuation of multiple sensors simultaneously measuring distributed phenomenon to maximize global coverage metrics Joint optimizations with –Energy usage –Navigation constraints –Communication requirements –Resource scheduling in space and time

25 Two Phase Solution 1.Learn H in deployment scenario –Actuation can be used to acquire the propagation matrix coefficients at high resolution 2.Use actuation to optimally place and move sensors –Achieve favorable H, R N –System evolves with phenomenon and environment dynamics

26 Phase 1: Self-Aware Actuation Learn and improve system coverage and uncertainty –Map environmental obstructions –Estimate sensor noise SENSOR

27 Self-awareness Sensors Acoustic range sensor to acquire propagation matrix Alternatives: –Stereo-vision: needs two cameras and heavier processing –Laser Ranging: more accurate but Very expensive hardware Higher energy requirements Large size (more processing electronics) –IR Ranging: useful for shorter range Beam Pattern of SensComp Acoustic Transducer

28 Feature Extraction Algorithms Pan the range sensor to measure distances Build environment model –Estimate positions of environmental features –Move to take further measurements and refine map Algorithms: –VFH: Vector Field Histogram –SLAM: Simultaneous Localization and Mapping

29 Discritized Medium Model 3D space divided into voxels Learn whether a voxel is occupied or not A slice of the 3D space White: voxels revealed to be empty by range sensor ray tracing Green: Unexplored/occupied

30 Phase 2: Coordinated Actuation Multiple sensors tracking multiple phenomenon Questions –How should coverage be maximized –How should the sensor move to improve information after it detects a phenomenon –How should other sensors locate themselves to gather additional non-redundant information –How should multiple sensors be shared among multiple phenomenon

31 Analyzing the Information Gain Information gained from a new observation be z Bayesian approach to update belief about measured phenomenon, x: Methods to execute this for multi-variate probabilities and multiple simultaneous observations exist: Bayesian networks –Exploit problem structure and variable dependencies to simplify computation

32 Move to Maximize Information Gain Expected information gain can be measured as mutual information: Utility of new observation can thus be measured as: - E{log[P(x|z)]} For multiple sensors: -E{log[P(x|z 1,z 2 …,z L )]}

33 Motion Control With above metrics, motion trajectories known for sensor teams in –Occlusion free scenario –Gaussian phenomenon –Unconstrained motion Need methods to measure information gain in the presence of sensing occlusions using the acquired propagation matrix Need optimal actuation along constrained paths Grochoslky, 2002 x y phenomenon Sensor trajectory

34 Learning Based Approach Central Dispatcher determines which sensors move –Based on estimated quality of each sensor’s data Sensors locally determine pose –Obtain central estimate of target trajectory –Orient/Move towards estimated target location –Action reinforced based on achieved target visibility [Ref: U.W Ontario]

35 Distributed Actuation: Example Simple pan actuation to optimize instantaneous coverage Random Orientations Improved Orientations

36 Distributed Actuation Strategies Define: Neighbors = {any node within 2*R s } Wish to coordinate pan orientations to maximize network coverage Algorithm 1: Obstacle information not available, location available Each sensor transmits {identity,location} to neighbors Each sensor sorts received identities in ascending order and waits for message from those with smaller identity than itself –Identity order ensures no two sensors choose orientations simultaneously and hence cover overlapping regions When all messages received (or this sensor has lowest identity within its neighborhood) –Choose a pan orientation which has minimum overlap with sensors whose pan orientation received –Transmit chosen pan orientation to neighbors

37 Distributed Actuation Strategies Algorithm 2: Environment obstacle sensing capability available (location not used) Each sensor chooses pan orientation to maximize its line of sight coverage Overlap with neighbors may causes sub-optimal behaviour

38 Distributed Actuation Strategies Algorithm 3: Utilize environment knowledge and sensor coordination Follow algorithm 1 except that when choosing orientation: Choose a pan angle where the covered area is maximum after accounting for neighbor overlap and environmental occlusion Geometric calculations based on obstacle locations and neighbor orientations allow the above decision Expected to perform better as using more information than previous two algorithms

39 Comparison of Actuation Strategies Node Density Coverage Fraction

40 More on Sensing Uncertainty and Actuation Outlier Verification: Suppose sensor reading differs significantly from neighboring sensors –Is it due to unexpected phenomenon or sensor error? –Mobile node can be moved to location of exception to compare values for critical decisions

41 More on Sensing Uncertainty and Actuation In-situ Calibration: Need calibration after deployment –Re-calibrate as part of complete device –Re-calibrate to overcome drift Hard to provide known stimulus in-situ –Known calibrated mobile sensor can be used as ground truth to calibrate

42 More on Sensing Uncertainty and Actuation Security Issues: Mobile Sensor Can Carry Trust –Malicious behavior: Sensor not faulty but node is compromised and reports malicious data –Mobile sensor can be used for security patrols to periodically weed out such nodes

43 Conclusions Actuation can reduce sensing uncertainty where high density or higher precisions sensors alone fail Actuation can be used in a self-aware setting to reconfigure and adapt the system to run time dynamics Coordinated actuation can help achieve best sensing performance by efficiently utilizing system resources

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