Algorithms for Robot-based Network Deployment, Repair, and Coverage Gaurav S. Sukhatme Center for Robotics and Embedded Systems Center for Embedded Networked Sensing Computer Science Department University of Southern California

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

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, 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.

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 (accepted, to appear) M. Batalin, G. S. Sukhatme, Efficient Exploration Without Localization Proceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA'03), Taipei, Taiwan, May , Robot Loop If no beacon within radio range deploy beacon Else move in direction suggested by nearest beacon Beacon Loop Emit least recently visited direction

Robot deploys network Network Deployment

Environment change Network extension Adapting to Environment Change

Graph Cover Times Cover time is a measure of exploration speed Random walk is O(n 2 ) –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 goal computed using dynamic programming Robot uses network 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 Sensor node start goal start goal start goal

Robot Navigation Using a Sensor Network Video

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, (to appear) Video

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 w i 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 w i and a hop count h i and computes the following utility(i) = w i /h i 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

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.