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Algorithms for Robot-based Network Deployment, Repair, and Coverage

Gaurav S. Sukhatme

Center for Robotics and Embedded SystemsCenter for Embedded Networked Sensing

Computer Science DepartmentUniversity of Southern California

gaurav@usc.eduhttp://robotics.usc.edu/resl

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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– …

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Network Deployment

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Deployment Constraints and Tradeoffs

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

• Visibility– Communication visibility, sensing visibility

• Efficiency– How many nodes ? How quickly ?

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Network Repair

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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

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Robot-based Network Deployment

• Case 1: All the network nodes are mobile robots

• Case 2: Single ‘capable’ robot drops off nodes at their places– Network nodes are stationary– Repair: Robot ‘plugs holes’ in the resulting

network using the same algorithm

Sameera Poduri and Gaurav S. Sukhatme, "Constrained Coverage for Mobile Sensor Networks," IEEE International Conference on Robotics and Automation, 2004Maxim Batalin, Gaurav S. Sukhatme, and Myron Hattig, "Mobile Robot Navigation using a Sensor Network," IEEE International Conference on Robotics and Automation, 2004Maxim Batalin and Gaurav S. Sukhatme, "Using a Sensor Network for Distributed Multi-Robot Task Allocation," IEEE International Conference on Robotics and Automation, 2004.

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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

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Approach

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

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

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Robot deploys network

Network Deployment

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Environment change

Network extension

Adapting to EnvironmentChange

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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

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Path to goalcomputed usingdynamic programming

Robot usesnetwork to navigate

Robot Navigation using the Network

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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

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Robot Navigation Using a Sensor Network

Video

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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

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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. (to appear)

Video

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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.

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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

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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)

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Results

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

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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

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

Maxim Batalin, Gaurav S. Sukhatme, and Myron Hattig, "Mobile Robot Navigation using a Sensor Network," IEEE International Conference on Robotics and Automation, 2004

Maxim Batalin and Gaurav S. Sukhatme, "Using a Sensor Network for Distributed Multi-Robot Task Allocation," IEEE International Conference on Robotics and Automation, 2004.