Http://robotics.usc.edu/~gaurav1/72 Networked Robotics: From Distributed Robotics to Sensor Networks...

http://robotics.usc.edu/~gaurav 1/72

Networked Robotics:From Distributed Robotics to Sensor Networks

Gaurav S. Sukhatme

Center for Robotics and Embedded SystemsCenter for Embedded Networked Sensing

Computer Science DepartmentUniversity of Southern California

[email protected]://robotics.usc.edu/resl

http://robotics.usc.edu/resl


Introduction

• Synoptic sensing: sense everywhere in parallel

• Enablers: small computers, sensors, radios

• Role of robotics: Deploy sensors, Localize sensors, Replenish and repair network

• Potential Applications:– Ecosystem bio-complexity

monitoring– Marine microorganism

monitoring– Structural health

monitoring– …


Network Deployment


Deployment Constraints and Tradeoffs

• Connectivity– Final/Intermediate– K-connectedness, K-degree (density)

• Visibility– Communication visibility, sensing

visibility• Efficiency

– How many nodes ? How quickly ?


Network Repair


Repair Constraints

• Minimal Intervention– Smallest number of nodes are

subjected to small displacements– Small number of new nodes deployed

• Speed– Faster than (re)deployment

• Preserve connectivity/visibility


Multi-Robot Task Allocation (MRTA)

B B


MRTA Constraints

• Speed of event detection– ‘Robot only’ solution could be slow– Network effectively extends sensing

range• Speed of response• Simple vs. complex robots

– Computation on the robots vs. in-network


Today

• Network deployment of robotic nodes– Sensor coverage and line-of-sight connectivity– Sensor coverage and k-neighbor connectivity

• Network deployment of static nodes using mobile robots– Sensor coverage and fast exploration, no

environment map• Network repair using mobile robots

– Speed, no map of the environment• Multi-robot task allocation using a network

– Navigation: No central planning, no constraints on adding robots dynamically

– Adaptive sampling


Deployment

• Assumptions– Robotic nodes initially confined to a small

area

– Each node has a finite sensing radius Rs and communication radius Rr

• Goal– Maximize total sensor coverage such that

each node has k neighbors within communication range

• Approach– Repulsive potential field with damping and

switching


The Deployment Algorithm

nonoi FFUUUF )(

i i

nono rkU

1||

i i

nonor

kF 2||

1

mxFx iii /)(

Each robot is controlled according to virtual forces

Andrew Howard, Maja J. Mataric and Gaurav S. Sukhatme, "Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem," in Proceedings of the International Symposium on Distributed Autonomous Robotic Systems, 2002.


Static Deployment using a Potential Field

• Tuning the damping term– Use value that gives critical damping for a two node

system– Rs and Rr not used explicitly

• No control over k


Modified Potential Field Approach

If degree > kRepel neighbors (increase coverage)

elseAttract neighbors (maintain degree)

Sameera Poduri and Gaurav S. Sukhatme, "Constrained Coverage for Mobile Sensor Networks," IEEE International Conference on Robotics and Automation, 2004


• d<=2Rs

Coverage depends on d

• d>2Rs

Coverage independent of d

• 2 regimesRr > 2Rs

Rr <= 2Rs

Role of Rr/Rs

d

d

d

d


Evaluation

• Performance metric

– Coverage: Area covered/ NπRs2

• Compare with

– Regular tilings

– Random Networks (uniformly distributed)

– Centralized iterative approach


Regular k-Tilings

• Rr <= 2Rs and large N

• Exact formulas for coverage for k = 0,1,2,3,4,6


Random Networks

• Uniformly distributed random network – Poisson distribution

• Probability of exactly m nodes in an area S :

1( , ) exp

!

m

P m S SS m

• Degree depends on ρ

• For , find ρ such that

2rS R ( , ) 0.95P m K S

1 exp( )Coverage S


A Centrally Computed Baseline

• Generate a configuration prior to deployment

(assumes an environment map)

• Loop:

1. Start with a network N of min. degree k

2. Randomly displace a node to get Nnew

3. If Nnew also has min. degree k, N=Nnew

4. If Average_degree < k+2, stop, else goto 2


Results Rr < 2Rs


Results Rr = 2Rs

Rr=8, Rs=4, n=49


Results Rr > 2Rs


Potential Field-based Deployment

• Distributed algorithm• No map of the environment• Need range and bearing to neighbors –

impractical on small sensor-constrained nodes

• Experimental results:– Reasonably good coverage– Connectivity > 97%– #nodes with degree at least k > 95%– Converges quickly


Today








Robot-based Deployment

• Its hard to make small mobile network nodes

• Not clear if its energetically feasible• Alternate regime:

– Deployment: Single ‘capable’ robot drops off nodes at their places

– Repair: Robot ‘plugs holes’ in the resulting network using the same algorithm


What’s in it for the Robot(s) ?

• An efficient deployment strategy (linear in the network size), is also an efficient exploration strategy for the robot

• Once the network is emplaced– any robot can use it to navigate (path

planning is done ‘in-network’)– in-network (de-centralized) task

allocation can coordinate the actions of multiple robots


Approach

M. Batalin, G. S. Sukhatme, Coverage, Exploration and Deployment by a Mobile Robot and Communication Network, Telecommunications Systems, April 2004

M. Batalin, G. S. Sukhatme, Efficient Exploration Without LocalizationProceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA'03), Taipei, Taiwan, May 12 - 17, 2003.

Robot LoopIf no beacon within radio range

deploy beaconElse

move in direction suggested by nearest beacon

Beacon LoopEmit least recently visited direction


Robot deploys network

Network Deployment


Environment change

Network extension

Adapting to EnvironmentChange


Graph Cover Times

• Cover time is a measure of exploration speed

• Random walk is O(n2)– on a regular graph of n nodes

• DFS is O(n) and requires– passive markers– a topological map– markers of 3 colors

• Our algorithm is O(n ln n) and requires– infinite active markers, no map


Path to goalcomputed usingdynamic programming

Robot usesnetwork to navigate

Robot Navigation using the Network


Robot Navigation using a Sensor Network

• Mica2 mote-based sensor network

• Mobile robot navigates based solely on network directives

• Results include over 1 km robot traverses in experiments

robot

Sensornode

startgoal

start

goal

start

goal


Robot Navigation Using a Sensor Network


Robot Navigation to Contours

• Use sensor network to navigate robot towards a contour of interest

• Variant on the previous approach

Karthik Dantu and Gaurav S. Sukhatme, "Detecting Level Sets of Scalar Fields Using Actuated Sensor Networks," Submitted to IROS 2004


From the Air

Peter I. Corke, Stefan E. Hrabar, Ron Peterson, Daniela Rus, Srikanth Saripalli, and Gaurav S. Sukhatme, "Autonomous Deployment and Repair of a Sensor Network using an Unmanned Aerial Vehicle," IEEE International Conference on Robotics and Automation, 2004.


Today








Multi-Robot Task Allocation

• Problem: Events in the environment, robot needed in vicinity of each event to observe it

• Given a pre-deployed sensor network, no environment map, no assumptions about a static environment

• Solution: Augment the deployment/exploration algorithm based on event occurrence

M. Batalin, G. S. Sukhatme, Sensor Network-based Multi-robot Task Allocation, Proceedings of the 2003 IEEE International Conference on Intelligent Robots and Systems (IROS '03), Las Vegas, Oct 27-31, 2003.


Outline

• Pre-computation: In the exploration phase compute P(s’|s,a) transition probability from node s to s’ for action a

• Every event i in the environment is assumed to have a weight wi

• Every node computes a suggested direction of travel for a robot in its vicinity


In-network Computation

• Events are flooded through the network

• Each node receives an event weight wi

and a hop count hi and computes the following

utility(i) = wi /hi

current alarm = argmax utility(i)V(s’) = C(s,a) + max Σ P(s’|s,a) V(s)Π(s) = argmax Σ P(s’|s,a) V(s)


Results

• Compare aggregate event on-time for ‘exploration/deployment-only’ mode vs. ‘task-allocation’ mode


Task Allocation Application I

• Marine microorganism monitoring• Adaptive sampling of the underwater

environment based on physical and chemical measurements

Bin Zhang, Gaurav S. Sukhatme, and Aristides A. Requicha, "Adaptive Sampling for Marine Microorganism Monitoring," IROS 2004.

C:\Documents and Settings\Gaurav S. Sukhatme\My Documents\Videos and Pix\robotics_sensor_network_320by240_sped_up_5000times.mov


TA Application II: Networked Infomechanical Systems

• Networked mobile nodes– Sensing – Sampling– Energy logistics– Communication

• Infrastructure– Enables deterministic and

precise motion– Enables proper elevation– Enables mass transport at

low energy• System Ecology

– Fixed nodes– Mobile nodes– Infrastructure


NIMS Node


NIMS Node in Action


Results

0.0 1.0 2.0 3.0 4.0 5.0

0.0

1.5

3.0

4.5

2

12

22

32

42

52

62

72

PAR Value

Horizontal Displacement (m)

Vertical Displacement

(m)

0.0 1.0 2

.0 3.0 4.0 5.0

0.0

1.5

3.0

4.5

271217222732

3742

47

52

RH (%)

Horizontal Displacement (m)

Vertical Displacement

(m)

PAR Humidity


Extensions

• Dealing with thrashing• Explicit task assignments vs. implicit • Further experiments underwater and in the forest

canopy


Today








Conclusion

• Symbiotic relationship between mobile robots and sensor networks– Actuation enables us to focus sensing

where it is needed when it is needed– Networks extend the effective sensing

range of robots and offload some processing


C:\Documents and Settings\Gaurav S. Sukhatme\My Documents\Videos and Pix\ap_hill_07_640x480_3.mov


Localization

• Stationary sensor network with some ‘GPS-enabled’ nodes

• Three algorithms (all based on rigid graphs):– Range only (high accuracy requires high

density)– Range and bearing (high accuracy requires

very few neighbors but bearing is difficult to measure on small nodes)

– Range and sectoring (high accuracy with moderate number of neighbors)

K.Chintalapudi, A. Dhariwal, R. Govindan and G. Sukhatme, Adhoc Localization using Ranging and Sectoring, Proceeeding of Infocomm, March 2004


Cooperative Localization

• System contains beacons and beacon detectors

• Assumptions:• beacons are unique,• beacon detectors

determine correct identity.

• Determine the relative pose of each pair of beacons/detectors


Team Localization using MLE

• Construct a set of estimates H = {h} where:h is the pose of robot r at time t.

• Construct a set of observations O = {o} where o is either:the measured pose of robot rb relative to robot

ra at time t, or the measured change in pose of robot r

between times ta and tb.

Find the set of estimates H that maximizes:


Approach

Equivalently, find the set H that minimizes:


Gradient-based Estimation

• Each estimate• Each observation • Measurement

uncertainty assumed normal

• Relative Absolute

),,ˆ( trqh

),,,,,( bbaa trtro

)ˆ,ˆ(ˆ ba qq

)ˆ()ˆ(2

1)|( THoU


Gradient Descent

U(o|H)μh

μU(O|H)h Oo ˆ

ˆ

•Compute set of poses q that minimizes U(O|H)•Gradient-based algorithm


SLAM: Simultaneous Localization and Mapping


Sensor Network Calibration

C:\Documents and Settings\Gaurav S. Sukhatme\My Documents\Gaurav's VAIO 1999-2002\TALKS\SPIFFY_VIDEOS\ANDREW\IROS2001A_CALIB.AVI


Team Localization

• Runs on any platform as long as it can compute its motion via inertial sensing

• Unique beacons: robots, people, fixed locations etc.

• Global communications• No model of the environment• Indifferent to changes in the environment• Robust to sensor noise • Centralized

Andrew Howard, Maja J. Mataric´, and Gaurav S. Sukhatme, "Relaxation on a mesh: a formalism for generalized localization," In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1055 - 1060, Wailea, Hawaii, Oct 2001.

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