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Remote monitoring of indoor environment using mobile robot based wireless sensor network
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Remote monitoring of indoor environment using mobile robot based wireless sensor network



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    Remote monitoring of indoor environment using mobile robot based wireless sensor network Remote monitoring of indoor environment using mobile robot based wireless sensor network Document Transcript

    • Abstract—Remote environmental monitoring is one of theimportant applications of wireless sensor networking technologywhere spatially distributed sensor nodes are used to monitorenvironmental parameters and collaboratively transmit theirdata through the network. This paper discusses the design of amulti robotic platform within a hybrid wireless sensor network tomonitor the environmental changes. The system includes a set ofstatic wireless sensor nodes, a set of mobile robots and a centralcontroller. Mobile robots help to reduce traffic congestions andfacilitate close monitoring capabilities. For the effectivenavigation of the robot, the system uses a known map of theenvironment, sensor readings and the guidance from the wirelesssensor network. The combination of more than one technique fornavigation significantly reduces the errors in path planning of themobile robots. The sensing nodes in the network will forward theaccumulated data to the central controller for analysis. Thecentral controller will classify the data based on differentthreshold levels and records the data. If the system detects anevent which exceeds current threshold level, then it will providean early warning to the authorized users of the system.Index Terms—Wireless sensor network, robot navigation,environment monitoringI. INTRODUCTIONIRELESS sensor networks are one of the most effectivetechniques for monitoring and controlling of physicalenvironments from remote locations with betteraccuracy[1] .A set of small devices called sensor nodes willacquire data and transmit through the network for decisionmaking. These nodes can be stationary nodes or mobile nodes.Mobile nodes help to reduce traffic congestions in the networkand to provide close monitoring capabilities. This paperpresents the design of a hybrid wireless sensor network withboth static and mobile nodes to monitor environmentalparameters. A set of self navigating robots are the mobilenodes in this wireless sensor network. These robots will beable to move around the indoor area having unknownobstacles. They will explore the given area, sense usefulinformation and send to the remote users through the network.Obstacle free path planning is one of the major difficultiesin the navigation of mobile node. Path planning [2,3] refers tocomputation of a collision free path between initial positionand target position. The indoor area under considerationcontains a number of obstacles which may be static, dynamicor unstructured. The mobile robot has to move from onelocation to another with in this environment by avoiding theseobstacles. For the autonomous surveillance of the givenenvironment effective path planning algorithms are required.In this paper, we present the design of a wireless sensornetwork and a path planning algorithm for mobile robots. Thepath planning algorithm uses partial map of the environment,readings from embedded sensors in the mobile robot andsignal strength of static wireless sensor nodes for navigation.Combination of more than one orthogonal technique for pathplanning helps to reduce possible errors in positionestimation. The mobile robots will be able to communicatewith each other and with the wireless sensor network.Whenever a set of mobile robots are operating in the sameenvironment their motions have to be coordinated in order toavoid deadlocks or collisions. The central controller node ofthe wireless sensor network is responsible for the coordinationamong the robots. Authorized users of the network can issuecommands to robots through the network and this providesfacilities to monitor specific areas of interest by issuing specialcommands to the mobile robots. The communication betweenrobots avoids deadlocks in the system.The paper is organized as follows: Section II describes abrief review of the related works. Section III presents thesystem model. The algorithms used in the system aredescribed in section IV. Section V deals with theimplementation of the system followed by the conclusion.II. RELATED WORKMany algorithms exist in literature to solve robot pathplanning problem based on different approaches like fuzzylogic [3], neural networks [4] etc. Byoung-Tak Zhang et.al [5]presents a new method for evolutionary path planningalgorithm. It can be used on-line for real-time applications.Given a source-destination pair, the path planner searches themap for a best matching route. If an optimal path is not found,the planner uses another evolutionary algorithm to generateon-line a path for the source destination pair. The overallsystem is an incremental learning planner that graduallyexpands ats own knowledge suitable for path planning in real-Remote Monitoring of Indoor EnvironmentUsing Mobile Robot Based Wireless SensorNetworkJoshua D Freeman, Simi SAmrita Center for Wireless Networks and Applications,AMRITA Vishwa Vidyapeetham (Amrita University)Kollam, Kerala, Indiajoshdfreeman@am.amrita.edu, simi.surendran@gmail.comW978-1-4244-9718-8/11/$26.00 ©2011 IEEEThe 6th International Conference onComputer Science & Education (ICCSE 2011)August 3-5, 2011. SuperStar Virgo, Singapore1080ThC 10.8
    • time. Multi-robot path-planning problem can be solved usingcentralized or distributed approach. In the centralizedapproach [6], the criterion functions and the constraints forpath planning of all the robots are considered together.Distributed approaches on the other hand reduce thecomplexity by an autonomous planning of the path for eachrobot. In [7] authors discuss a new algorithm for multirobotcooperative path planning. The algorithm uses fullydistributed path planning without any central instance. Pathplanning is done on local sections of the time-spaceconfiguration space utilizing the communications betweenrobots.Maxim A and et.al [8] describe an algorithm for robotnavigation using a sensor network. This method does not useany map. They are using sensor network nodes to find path ofthe robot. The navigation decision is based on the nearest nodeand computed using small low-power radios. The authors of[9] in this, the authors present an algorithm for path planningto a target for mobile robot in unknown environment. The pathfinding strategy is designed in a grid map form of an unknownenvironment with static unknown obstacles. It will plan anoptimal or feasible path for itself avoiding obstructions in itsway and minimizing a cost such as time, energy, and distance.III. SYSTEM DESIGNThe proposed system is the design of a wireless sensornetwork with both static and mobile nodes to monitorenvironmental parameters. A set of self navigating robots actsas mobile nodes in the system. Each robot is embedded withobstacle finding sensors and environmental parametermonitoring sensors. There is a centralized server to coordinatethe activities of robots. Total area considered for surveillanceis divided into a number of regions. Central controller willassign operating region of each robot and hence balance thesensing load and provide surveillance of the whole area.Fig. 1. System DesignPololu 1060 is the platform selected for mobile robotoperation and Micaz motes from Crossbow is selected as staticnodes. Sensing nodes in the system will collect environmentalparameters and send to the wireless sensor network. Sink nodeof the wireless sensor network will compare the data with thecurrent threshold levels. If the incoming data exceed thethreshold, it will give indications to the concerned authoritythrough the network. For the effective computation of roboticpath, the system uses known map of the environment, obstacleidentifying sensor readings and the signal strength of wirelesssensor nodes.The robot is using distance sensor data to find obstaclesahead of it. There may be errors in the data collected by therobot. So we are using a probabilistic approach to reducenumber of possible errors. Suppose the robot sensor collect ameasurement x. From the reading x we have to calculate theprobability that it is an obstacle. We can assume P(obstacle) =P ( Ī obstacle) = 0.5. Also we know that P( x | obstacle ) fromthe range of readings from the sensor. In the applicationscenario, we know the partial map of the environment andrepresented it in the form of a grid. To update the map and toselect the next move of the robot we can apply Baye’sprobability. Using Baye’s rule we can writeܲ( ‫݈݁ܿܽݐݏܾ݋‬ | ‫)ݔ‬ =ܲ( ‫ݔ‬ |‫݈݁ܿܽݐݏܾ݋‬ ) ∗ ܲ(‫)݈݁ܿܽݐݏܾ݋‬ܲ(‫)ݔ‬(1)We are also measuring the gyrometer values to know therotation of wheels and the translation. Let the robot movesfrom (x1, y1, θ1) to (x2, y2, θ2) the sensors will collect changein rotation angle of two wheels and the change in translation( δ r θ w1, δr θ w2, δtrans).These values are calculated asδ‫ݏ݊ܽݎݐ‬ = ඥ(‫1ݔ‬ − ‫)2ݔ‬2 + (‫1ݕ‬ − ‫)2ݕ‬2 (4)δ‫ݎ‬ ߠ ‫1ݓ‬ = ܽ‫2݊ܽݐ‬ ቀ൫( ‫2ݕ‬ − ‫,1ݕ‬ ‫2ݔ‬ − ‫)1ݔ‬൯ − ߠ1ቁ (5)δ‫ݎ‬ ߠ ‫2ݓ‬ = ߠ1 − ߠ2− δ‫ݎ‬ ߠ ‫1ݓ‬ (6)IV. ALGORITHM DESIGNIn the proposed system, we are using two major algorithms,one for the navigation of mobile node within the area andanother for wireless sensor network setup and communication.A. Mobile Node Navigation AlgorithmFor the effective operation of the system, the robots needobstacle free path from source to destination. The wholeindoor area under consideration is sampled into a set of cells.Each cell is marked with the presence or absence of knownobstacles. Also the area is equipped with wireless sensornodes and all robots are moving within this wireless sensornetwork. Some of the sensor nodes in the system act aslocation finders, whose main function is to broadcast theirlocation information. The area covered by the signal strengthof one location finder is identified as a region. Locationfinders are deployed in such a way that they will cover thewhole area with minimal overlapping.The total area is divided into R different regions with thehelp of location finder nodes. Let the number of robots be N.The robotic state is represented by the set state = {idle, pathfinding, sensing, advanced-sensing}. Initially states of allrobots are set to idle. Each robot in the system is assigned arandom target and changes its state to path finding. Pathfinding function generates a tree with possible moves (left,right, up, down) from current location to the target location. Aranking function is used to estimate the cost of each path.Highest rank will be assigned to cells which are closer to thetarget location. Using a back propagation method, lowestranks are assigned to cells far away from the target or moreobstructions near that cell depending on the map and sensor1081ThC 10.8
    • readings. If there exists a path to the target from the currentlocation, path finding function will return a tree showingpossible paths with its rank.After finding a path to the target, the state of the robot isupdated to sensing. During this phase, it will search the treeand select a local maximum as its next move. At the same timethe sensors in the robot, sense the environmental parametersand communicate to the wireless sensor network. Robot willcontinue this operation until it reaches the target or receiveany command from wireless sensor network or from otherrobots. If any new obstacle comes in front of the robot in thecomputed path, it will backtrack to the previous node of thetree and select an alternative lowest path. If no such pathexists, it can re compute the path.B. RSSI Based technique for region localizationThis is an alternate mechanism to localize the robots withinthe specified region. The guiding sensors in the wirelesssensor network will periodically broadcast their locationinformation and identity. The robots will be able to receivemessages broadcasted from guide sensors and compute thesignal strength. There is a database of the identity of guidesensors and their static location in each robot. Usingtriangulation method, the robot will be able to localize itsregion with respect to the location of static guide sensors.C. Cluster setup in wireless sensor networkFor effective communication of robot and the wirelesssensor network, the nodes in the wireless sensor network formdifferent groups. According to the signal strength of theneighboring nodes, they form clusters. Following figure 2shows the network topology.Fig. 2. Network topologyThere is a master node which will acts as the central controllerof all these nodes. The cluster setup algorithm will elect one ofthe available special nodes as cluster head. In this phase of thealgorithm, the nodes in the network will exchange a set ofmessages. These messages are invitation message, replymessage, agree message, negotiation message andacknowledgement. Invitation message is a broadcast messagesent by the nodes to invite other nodes in its communicationrange to create clusters. This message contains the ID of thecluster head. The nodes who receives invitation message willsend a reply message. This is an indication that the node isreachable from the node and it is ready to join the cluster. Themessage includes the node ID, number of invitations, and theIDs of inviting nodes and corresponding signal strengths.Negotiation message is transferred between the cluster headsto compromise the number of cluster members and link nodesin each cluster. This maintains a minimum and maximum limitin the number of nodes in the cluster. Agree message is sendby the cluster heads to confirm the membership in the clusterby specifying the ID of the cluster head. After receiving1082ThC 10.8
    • confirmation message, the cluster members will send anacknowledgement to the cluster head.In the cluster generation process, nodes will broadcast aninvitation message to all the neighboring nodes. The nodeswhich are in the communication range of the node will receivethis message. A node may receive invitation from more thanone cluster heads. The nodes receiving invitation will send areply message to all the inviting nodes. If the signal strengthof any of the invitation message is less then it will ignore theinvitation otherwise send a response. The node who receivesmaximum number of reply messages will be elected as clusterhead. In each cluster select atleast one node as intermediatenode which is having range to neighboring cluster head also.These intermediate nodes act as a bridge between thecommunications of two cluster heads. Also the cluster headswill communicate with each other to make an agreementbetween numbers of cluster members and connection nodeswith in a cluster. The cluster heads will send a agree messageto all nodes in its cluster table. By receiving this agreemessage, the member nodes will store the ID of cluster headand send an acknowledgement to the cluster head. If thecluster head is not receiving the acknowledgement after thetimeout period, it will retransmit the reply message.All cluster heads in the network knows the number of hopsto the sink node from that node. These cluster heads have toforward the aggregated data to the sink node. For fast andeffective forwarding, the number of hops travelled by thepacket should be less. We used Dijkstra’s shortest pathalgorithm to find the shortest path from each cluster head tosink node. It uses number of hops to the sink as metric of thealgorithm. The shortest path information is added to therouting table of each cluster head and intermediate node. Aftersetting up of clusters, the actual communication between thenodes will take place. The messages can be synchronizationmessage, data message, beacon message. Based on the type ofmessage received the receiving nodes process the data, updateitself and forward it if required.V. IMPLEMENTATIONThe robotic platform selected for this project is Pololu 1060with ATmega microcontroller. For implementation, we haveselected AVR studio 4 and programmed using Embedded C.The simulation of the robot navigation algorithm shows that itwill work for different number of dense obstacles. We havecreated a test bed to test navigation algorithm in hardwareplatform Pololu 1060. The mobile robot was able move insidethe test area by avoiding obstacles. Robots sensedenvironmental data and sent to the central controller of thenetwork for analysis. Figure 3 shows simulation of roboticpath planning algorithm.To implement wireless sensor network, we used MicaZmotes and ZigBee technology. Each cluster member in thesystem can sense the data and communicate to higher levelnodes. Also they can receive synchronization messages andother control messages from higher level nodes in thehierarchy. The transmission and reception of the messages arethrough MicaZ mote embedded with ZigBee compatible RFtransceiver . TinyOS is the operating system used for thedevelopment. The components and interfaces of TinyOS areused to communicate messages in the network. In the test bedwe have implemented a wireless sensor network. Clusteringalgorithm was tested to find the participation of all nodes insensing and forwarding operations. In central controller, datais analyzed and plotted in real-time. Figure 4 shows the realtime graph of the sensed data. Multiple sensor types areembedded in the sensor network to monitor differentenvironmental parameters. Authorized users of the system canview the real-time graph of any sensor type deployed in thenetwork through internet.Fig.3. Robot path planningFig.4. Plotting real time dataVI. CONCLUSIONAutonomous robot navigation is the most difficult task inrobotics. In the proposed system, the robots are operating in anindoor area equipped with a wireless sensor network. Theembedded sensors in the robot allow the close monitoring ofenvironmental parameters and provide minimal noise, accuratedata to the wireless sensor network. This can be applicable forsensing data where the range of available sensors are less andthe effect of noise in the data is very sensitive. The wirelesssensor network also assists the robots for its effective pathplanning with the help of guide sensors. More than one type of1083ThC 10.8
    • method used for navigation allows the system to reduce errorsin path planning. With the help of the robotic platform, thewireless sensor network senses useful information and providereal time streaming of the environment.ACKNOWLEDGEMENTAuthors wish to express their deepest gratitude to Mr.K.A.Unnikrishna Menon and Dr. Maneesha V Ramesh,Amrita Center for Wireless Networks and Applications,Amrita University for their inspiration and contributions to theoverall success of the project.REFERENCES[1] Akyildiz I, Su W, Sankarasubramaniam Y, Cayirci E, A survey onsensor networks, IEEE Communications Magazine, 43(5), 102–114,2002[2] J. M. Ahuactzin and A. Portilla. A basic algorithm and data structuresfor sensor-based path planning in unknown environments. In IEEE/RSJInternational Conference on Intelligent Robots and Sys-tems, volume 2,pages 903–908, Takamatsu, Japan, Nov. 2000.[3] Liu, K. and Lewis, F.L., (1994) ”Fuzzy logic based navigationcontroller for an autonomous mobile robot”, Systems, Man, andCybernetics, 1994. Humans, Information and Technology, 1994 IEEEInternational Conference on Volume: 2, 1994, Page(s): 1782 -1789 vol.2[4] Lee, S. and Kardaras, G., (1997) ”Collision-free path planning withneural networks”, Robotics and Automation, 1997. Proceedings, 1997IEEE International Conference on Volume: 4, 1997, Page(s): 3565 -3570vol.4[5] Byoung-Tak Zhang and Sung-Hoon Kim,(1997) ”An evolutionarymethod for active learning of mobile robot path planning”,Computational Intelligence in Robotics and Automation, 1997.CIRA’97, Proceedings, 1997 IEEE International Symposium on, 1997,Page(s): 312 -317[6] P. Svestka and M. Overmars, “Coordinated path planning for multiplerobots,” Robotics and Autonomous Systems, vol. 23, no. 4, pp. 125-152, 1998.[7] R. Regele, P. Levi, “Cooperative Multi-Robot Path Planning byHeuristic Priority Adjustment,” in Proceedings of the IEEE/RSJInternational Conf. on Intelligent Robots and Systems, 2006.[8] Maxim A. Batalin, Gaurav S. Sukhatme and Myron Hattig, “MobileRobot Navigation using a Sensor Network,” in IEEE Int. Conf. onRobotics and Automation, 2003.[9] O. Hachour, “path planning of Autonomous Mobile Robot”,International Journal of Systems Applications, Engineering&Development, Issue4, vol.2, 2008, pp178-190.1084ThC 10.8