Robot Swarm Communication Networks:Architectures, Protocols, and Applications

Ming LiDepartment of Computer Science

California State University, FresnoFresno, CA 93740, USAmingli@csufresno.edu

Min ChenDepartment of ECE

University ofBritish ColumbiaV6T 1Z4, Canada

minchen@ece.ubc.ca

Kejie LuDepartment of ECE

University of Puerto Rico at Mayagiiez,Mayagiiez, Puerto Rico 00681

lukejie@uprm.edu

ShiwenMaoDepartment of ECEAuburn University

Auburn, AL 36849, USAsmao@ieee.org

HuaZhuSan Diego Research CenterSan Diego, CA 92121 USA

hua.zhu@argonst.com

B. PrabhakaranDepartment of Computer ScienceThe University of Texas at Dallas

Richardson, TX 7~080, USApraba@utdallas.edu

Abstract- There have been increasing interests in deploying ateam of robots, or robot swarms, to fulfill certain complicatedtasks such as surveillance. Since robot swarms may move to areasof far distance, it is important to have a pervasive networkingenvironment for communications among robots, administrators,and mobile users. For this purpose, we propose to build a wirelessmesh network as the wireless backbone within the areas ofspecial interest. One or more robots can get connected with anearby mesh router and access the remote server. Within eachswarm, a self-organizing mobile ad hoc network is formed. Withthis type of robot swarm communication network, there aremany important open issues to be addressed.

Keywords-robot swarnl, wireless mesh networks

I. INTRODUCTION

There has been much concern regarding effective andefficient surveillance recently. Although lots of human effortsare made to ensure the safety at the borders such as dispatchingmore security guard forces and building walls along theborders, these enforcements demonstrate several criticaldisadvantages. Especially, the extremely high cost and lack ofsufficient flexibility may eventually fail such efforts. Incontrast, emerging networking technologies such as wirelessmesh networks (WMNs) can help create virtual walls wherehuman and wireless devices share important information tomake appropriate actions, thereby greatly reduce the cost andimprove the operational efficiency.

On the other hand, robot swarms have gained lots ofattention in the past a few years. Despite of many complicatedartificial intelligent algorithms proposed to improve thecorrectness and efficiency of decision making of a single robot,using a team of robots has many advantages. First, single robotmay still not be intelligent enough to fulfill some tasks,especially in complicated geographical environments wheremultiple obstacles exist on the path of the robots. Second, faulttolerance is critical in some applications such as surveillanceand military battlefield where a single robot may be damaged

or die due to battery failure. Third, a swarm of robots is desiredfor accomplishing surveillance tasks difficult for human beingsdue to its low cost and flexibility. Finally, the idea of robotswarm is very natural since most complicated tasks such asmoving around a big obstacle and cargo transportation requirecollaboration among different robots. Furthermore, with thecost of robots being significantly reduced to several hundreddollars, dispatching multiple robots is becoming a feasiblescenario for a broader set of potential users. Therefore, webelieve that integrating robot swarms with wireless meshnetworks provides a new solution of effective and interactiveborder surveillance.

In this paper, we propose a new architecture called robotswarm communication networks. In this architecture, robots areclustered to one or multiple teams or swarms and each swarmcan be monitored and controlled by some central serversthrough a wireless mesh backbone as well as Internet. Withineach swarm, a self-organizing mobile ad hoc network is formedsuch that all robots are connected to all other robots despite ofthe movements. Meanwhile, mobile users can also monitorswarms through mobile devices such as laptops and PDAs andcan take action immediately based on certain informationcollected. The monitoring module may include location,topology, and related information. Pictures and videos are alsoproposed to be streamed from robots to servers or mobile usersfor appropriate decisions.

The proposed architecture poses new challenges from bothprotocol design and software system development aspects. Onthe one hand, how to coordinate robots that move randomlyand continuously is a critical issue. Related issues includecollaborative object recognition, deployment, etc. On the otherhand, how to develop a software system to efficiently monitor,coordinate, and control robot swarms is important for thesystem to be put in practice. In addition, streaming video fromrobots requires quality of service (QoS) support from thewireless mesh network backbone.

This paper is organized as follows. Section II discussesrelated works in robot swann algorithms, softwares, and QoSprotocols. Section III proposes the robot swarm communicationnetwork architecture. Section IV discusses the challenges fromnetwork protocol aspect. Section V discusses the challengesfrom software development aspect and Section VI concludesthis paper.

II. RELATED WORK

A. Robot swarm coordination:

Mondada and Pettinaro [2] proposed the concept ofSwarm-Bot, a group of autonomous mobile robots called S­BOTs. S-BOTs have a particular assembling capability thatenables them to connect physically to each other. With around10 to 30 S-BOTs being interconnected, SWARM-BOTbecomes much more robust than single bots and can handledifficult tasks even in hard environment conditions. Poduriand Sukhatme [4] investigated the self-deployment of a mobilesensor network and proposed to maximize the area coverageof the network with a K neighbor constraint. The core idea ofthe scheme is to control the location of mobile sensors throughtwo forces: Fcover that causes nodes to repel each other toincrease their coverage and Fdegree that causes nodes to attracteach other to have more number of neighbors. A nodestabilizes its position when the two forces are equivalent.Sheng and Yang [3] proposed a multi-robot exploration modeland studied the coordination among the robots in a group. Adistance based bidding algorithm was proposed to enforce thecoordination. Also, a map synchronization algorithm wasproposed to minimize the amount of information exchangewhen two sub-networks merge.

B. Robot monitoring/control softwares:

Correll and Sempo [1] developed SwisTrack, a newsoftware for tracking multi-unit robots and biologicalbehaviors. SwisTrack is a framework that enables users toremotely monitoring the movement of robots/insects. First, thevideo images of the swarm is captured with a camera and thensegmented to identify the targets in the background. Then,these data are transmitted over TCP/IP to the user side forpost-processing. SwisTrack significantly ease the task of smallrobot movements. However, it is only used for monitoringpurpose and the use of camera limits its application to smallareas. It is more desirable to have a framework that can beused in general scenarios such as outdoor surveillance.McLurkin and Smith [6] proposed a human-robot interface forcontrol iRobot Swarm that includes a team of 112 individualrobots. But the emphasis of the research is to interact with agroup of robots through commands or input from gamecontrollers and does not take care of the independent behaviorof robots. MobileEyes [5] is a commercial GUI software fordriving or controlling individual robot by issuing commandsand displaying the video captured by the moving robot.However, MobileEyes does not handle the coordination andcontrol of a robot swarm.

C. QoS support in wireless ad hoc networks:

Xue and Ganz [15] proposed Ad hoc QoS on-demandrouting (AQOR), a QoS routing protocol with admissioncontrol enforced to support quality of service in multi-hop ad

hoc networks. Yang and Kravets [14] proposed to enforceadmission control by considering the contention of all nodeswithin the carrier sensing area. However, expensive powerconsumption is required to obtain neighboring information.Also, they did not consider the link rate and traffic forbandwidth estimation. Chen and Heinzelman [8] proposed tocalculate available bandwidth by local estimation and messageexchange among neighboring nodes. Then, they adopted theresult proposed by Li and Blake [10] to account for intra-flowinterference. However, this result is not accurate enough,especially when multi-rate is enabled. With multi-rate MAC,the distance of a link may be much smaller due to transmissionwith high data rate, resulting in more intra-flow interference.To consider both the mobility, multi-rate feature, and the multi­link interference, Li and Prabhakaran [11] proposed routeavailable bandwidth (RAB) and route reliability (RR) metricswhen admission control decision is made.

Awerbuch and Holmer [7] proposed medium time metric(MTM) for better route selection in ad hoc networks. Based onthe assumption of complete interference, MTM chooses theroutes with minimum accumulate medium time consumption.To achieve this, each link is assigned a link weight, which isdetermined by the link rate and data packet. However, theassumption of complete interference makes it only appropriatefor small networks. Also, link loss ratio is not considered.Draves and Padhye [9] further considered multi-rate capabilityand combine link capacity and ETX for better performance inmulti-radio, multi-hop wireless mesh networks. They proposeWeighted Cumulative Expected Transmission Time (WCETT)to account for the interference among links that use the samechannel. Also, WCETT calculation can be tuned for variousdifferent QoS objectives.

III. NETWORK ARCHITECTURE

The architecture of the proposed robot swarmcommunication network is illustrated in Figure 1. Wirelessmesh routers are deployed on the top of buildings, walls, ortowers. Each mesh router consists of multiple antennas and canoperate over multiple channels to improve the network capacityand coverage. Mesh routers form a wireless backbone, which isfurther connected to wired Internet through gateways and IProuters. One or more team of robots are equipped with one ormore wireless adaptors which can communicate with meshrouters or other robots, digital video camera, and GPS. Eachrobot swarm maintains continuous connectivity within itselfand periodically updates the collected information such as itslocation, picture, and video to some administrators that resideeither locally or remotely. Meanwhile, users such as securityguards drive around the area frequently, access wirelessdevices such as PDA or Palm PC, and monitor one or morerobot swarms. If certain emergency is identified, a user canimmediately take action. Due to the limited computationalpower of these devices on image/video analysis, a user mayalso get certain instructions from the administrators forappropriate actions. Altogether, the proposed architecturepresents an interactive coordination framework for frequentmonitoring, efficient collaboration, and fast reaction.Furthermore, since of mesh routers, servers, robots, and PDAsare all inexpensive and require minimum human labor, sucharchitecture is feasible and very cost-effective.

...... IP Routers

"•I

II

UserI

•

I

•-------­

------.

Administrator

User

Figure 1. Architecture of robot swarm communication network

With the proposed architecture, the robot swarms helpfulfill the following critical tasks difficult for humans:

• Continuous surveillance: a swarm of robots can movearound various areas in a non-stopped pattern, whichsignificantly improves the information accuracy andthe timeliness of actions taken by security guards.Some robots can be made in very small size and cannotbe easily detected to enhance the effectiveness ofsurveillance.

• Information collection: through effective coordination,a team of robots equipped with GPS, video camera,and sensors can capture image/video periodically,recognize sensitive objects such as enemy andchemicallbiological stuff, and report to administratorsor security guards instantly.

• Coverage inspection: a robot can report to theadministrator immediately if it cannot receive wirelesssignal from the mesh routers, which helps identifycertain areas subject to security problems.

Wireless mesh networks (WMNs) and mobile ad hocnetworks (MANETs) have been investigated extensively. ForWMNs, many schemes have been proposed to address issuessuch as wireless channel assignments, network capacity, androuting. For MANETs, many schemes have been proposed onmobility and quality of service (QoS) aware routing, energyefficient Medium Access (MAC) protocols, and topologycontrol. Therefore, the emphasis of this research is not onWMNs and MANETs, but on the communications amongadministrators, users, and robot swarms. However, the meshbackbone provides an infrastructure to facilitate suchcommunications with broadband capability.

With one or more teams of robots being dispatched, thereare many interesting issues to be resolved. First, one importantissue is how to monitor these swarms in a real-time manner. Ifrobots can be effectively monitored, critical information suchas the location of each robot, the connectivity, and otherinformation can be collected at a central server. Based on theinformation from the swarm, the server may perform certaincontrol and/or coordination to assist the robots if necessary.Second, how to design algorithms to allow intelligent robots tocontrol and coordinate among themselves autonomously is abig challenge. The research on these topics, along with otherrelated issues, is still in its infancy.

IV. PROTOCOL DESIGN CHALLENGES

From the protocol design aspect, a set of protocols shouldbe developed in following topics:

• Distributed coordination of robot swarms: where theproblem of maintaining continuous connectivity ishandled by incorporating network information such astopology, energy level, and link condition with robotstatus such as moving, working, or idle.

• QoS support in wireless mesh networks: whereefficient protocols are designed to support voice/videostreaming in multi-radio multi-channel multi-hopwireless mesh networks.

A. Distributed coordination ofrobot swarms

Usually robots need to exchange certain information suchas location and status in order to make global decision. In thiscase, maintaining continuous connectivity of a swarm iscritical for the successful collaboration among robots within aswarm. The key challenge of coordination is how to ensureswarm connectivity without the prior knowledge about robotmobility pattern, i.e., robots can move freely based on its own

intelligence and tasks. Compared to centralized approacheswhere a central control robot coordinate the movement ofother robots, distributed approaches exhibits severaladvantages such as better scalability, efficiency, and faulttolerance. Based on collected information from neighborsthrough periodical message exchange, a robot can determinehow to guide its own movement to achieve certain goals whilemaintaining effective connectivity. In general, successfulcoordination protocols include, but not limited to the following:

• Movement based swarm coordination: It is desirable tohave a distributed algorithm such that each nodedetermines its moving speed and direction to avoid linkbreaking.

• Swarm partitioning recovery: Although coordinationmaximizes the connectivity, it is still possible that aswarm may get partitioned due to complicatedgeographical environments or one or more robots areattacked and damaged. A swarm partitioning recoveryalgorithm should be devised to first quickly detect theoccurrence of the swarm partition. Then, based on therecorded historical connectivity knowledge, each robotdecides its movement to recover the connectivity withprevious neighbors.

• Energy efficient swarm coordination: Since each robotonly has limited battery power, it is important tominimize energy consumption. While coordinationalgorithms try to minimize necessary movements,power adjustment technique can be used to minimizethe required energy to maintain effective connectivity.

• Swarm deployment: robots may move from onelocation to another location to perform certain tasks.How to maximize an area with a given number ofrobots is an interesting issue. A successful deploymentalgorithm has to be able to adapt to variousenvironmental constraints such as obstacles and walls.

• Data fusion and collaborative object recognition:Another important issue in robot swarm is how tomake robots collaborate with each other to recognizecertain targets. Due to the limited knowledge at eachrobot, it is critical for robots to share the informationwith each other and reach the same conclusion. Then,data are analyzed to decide their relative importancefor sharing. Finally, a minimum data set selector isdesigned to choose to disseminate to neighboringrobots.

B. QoS support in robot swarm networks

In wireless mesh networks (WMNs), multi-channel ormulti-radio features are enabled at mesh routers. Similar toIEEE 802.11 WLAN, WMNs suffer from significant channelinterference and thus not able to fully achieve its high capacityand support sufficient QoS for multimedia applications. Thefollowing topics should be investigated:

• Network reconfiguration: How to design an automaticnetwork configuration protocol such that the IP addressof a robot swarm is dynamically changed according tothe associated mesh router.

• QoS support: How to design a wired/wirelessintegration scheme such as [12] to support end-to-endQoS support for users accessing Internet within a meshnetwork.

• Load balancing: The QoS experienced by wireless endusers is constrained by the number of gateway routersand available channels. Therefore, it is desirable tobalance the load of network traffics going throughgateway routers. Furthermore, as users move, it mightbe more appropriate to route traffics to a closergateway node or a gateway node with higher routeavailable bandwidth.

v. SOFTWARE SYSTEM DEVELOPMENT CHALLENGE

Although several softwares [1][5][6] have been developed,they are either designed to monitor a single robot, or does notperform control and coordination, or are non-real time and usedjust for the purpose of simulation. To meet the software needsresulting from the increasing deployment of network enabledrobot swarms, we have designed and developed ROBOTRAK[13], a secure software for monitoring, control, andcoordination of intelligent robotic swarms. Using TCPconnections through the wireless medium, the ROBOTRAKserver can exchange information with the robotic swarmreliably and continuously. For monitoring purpose, all the botscollect and report wireless signal strength, interference,neighboring bots list, and location information. For controlpurpose, a new robot can be dispatched to join a swarm or anexisting robot in a swarm can be guided to move to a specificdestination. For coordination purpose, the server maintains theconnectivity of the swarm under various situations.Furthermore, in order to maintain network privacy and avoidoutsider intrusion, multi-security levels and a dynamicpassword technique were implemented.

The major advantages of ROBOTRAK are:

• Flexibility with comprehensive features on robotmonitoring, control, and coordination.

• User friendliness with nice graphical user interface.

• Multi-level security at both application layer andtransport layer.

• Robustness with the implementation of reliable socketcommunication.

ROBOTRAK can run on any server with Internetconnection and requires the robots in a target swarm to beequipped with wireless adaptors (such as WiFi) and globalpositioning system (GPS). All the messages can betransmitted/received through typical wired/wireless multi-hopnetwork connections. The software was implemented withMicrosoft Visual Basic.Net and can be easily modified toprovide web-based versions. All the designed features andfunctionalities have been implemented with user friendlygraphical user interfaces (GUI) and extensively tested invarious scenarios such as multiple robots and multipleinstances of running ROBOTRAK software.

Right now, ROBOTRAK has provided basicfunctionalities for monitoring, coordination, and control ofrobot swarms. Many new features should be added to make itmore practical. We intend to work on the following:

• Video streaming: We will implement the datacommunication and video rendering modules to enablevideo streaming from any robot to the server. Toimprove the quality, peer to peer techniques will beused.

• Robot movement guidance: Based on the location ofthe robots, the server can quickly look up thegeographical infonnation from the database andprovide certain guidance on moving around thespecific areas.

• Integration with coordination algorithms: We willimplement various coordination algorithms in T1 tomaintain effective connectivity within a swann to builda heterogeneous system to take advantage of bothcentral control from the server and the distributedcoordination within the swann.

VI. CONCLUSTION

Robot swann has emerged in recently years as a solutionfor surveillance in complicated geographical environmentsthanks to the significant cost reduction of individual robotsand accessories such as wireless adaptors, GPS, and videocameras. With a team of robots being dispatched, severalchallenges arise. From the communications aspect, continuousnetwork connectivity has to be maintained despite of randommovement patterns and various status and roles of each robot.From the algorithms aspects, how to enable effective andefficient collaboration among robots for appropriate decisionmaking is critical to the success of task fulfillment. On theother hand, it is imperative to design and develop a securereal-time software system such that administrators and mobileusers can monitor, coordinate, and control robot swannsremotely.

ACKNOWLEDGEMENT

This work is supported by the Provost Activity Awards atCalifornia State University, Fresno. Shiwen Mao's researchhas been supported in part by the National Science Foundationunder Grant ECCS-0802113, and through the WirelessInternet Center for Advanced Technology (WICAT) atAuburn University.

REFERENCES

[1] N. Correll, G. Sempo, Y. Lopez de Meneses, J. Halloy, J.-L.Deneubourg, and A. Martinoli, "SwisTrack: A Tracking Tool for Multi­Unit Robotic and Biological Systems", in IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS), pages 2185­2191,2006.

[2] Mondada, F., Pettinaro, G. C., Guignard, A., Kwee, I., Floreano, D.,Deneubourg, 1.-L., Nolfi, S. and Gambardella, L.M., Dorigo, M. (2004)SWARM-BOT: a New Distributed Robotic Concept. AutonomousRobots, special Issue on Swarm Robotics, Volume 17, Issue 2-3,September - November 2004, Pages 193 - 221.

[3] W. Sheng, Q. Yang, 1. Tan and N. Xi, Distributed Multi-robotCoordination in Area Exploration, Robotics and Autonomous Systems(Journal). Page: 945-955, Issue 54. 2006.

[4] Poduri, S. and Sukhatme, G. S. (2004). Constrained Coverage forMobile Sensor Networks. IEEE International Conference on Roboticsand Automation, New Orleans, LA, USA.

[5] MobileEyes: http://www.mobilerobots.com/MobileEyes.html

[6] James McLurkin, Jennifer Smith, James Frankel, David Sotkowitz,David Blau, Brian Schmidt. "Speaking Swarmish: Human-RobotInterface Design for Large Swarms of Autonomous Mobile Robots",AAAI Spring Symposium, March 28, 2006.

[7] B. Awerbuch, D. Holmer, and H. Rubens, "High Throughput RouteSelection in Multi-Rate Ad Hoc Wireless Networks", in Proceedings ofWONS'04, Madonna di Campiglio, Italy, January 2004.

[8] L. Chen, W. Heinzelman, "QoS-aware routing based on bandwidthestimation for mobile ad hoc networks", IEEE Journal on SelectedAreas o/Communication, Vol. 23, No.3, March 2005.

[9] R. Draves, 1. Padhye, and B. Zill, "Routing in multi-radio, multi-hopwireless mesh networks". In Proceedings of ACM MobiCom 2004,Philadelphia, PA.

[10] J. Li, C. Blake, D.SJ. De Couto, H.1. Lee and R. Morris, "Capacity of adhoc wireless networks," ACM MobiCom'OI, pp. 61-69,2001.

[11] Ming Li, B. Prabhakaran, "On Supporting Reliable QoS in Multi-hopMulti-rate Mobile Ad Hoc Networks", Best Student Paper Award, inProceedings of the First IEEE International Workshop on NextGeneration Wireless Networks (WoNGeN'05), Goa, India, Dec. 18-21,2005.

[12] Ming Li, Hua Zhu, Imrich Chlamtac, B. Prabhakaran, "End-to-end QoSFramework for Heterogeneous Wired-cum-Wireless Networks",ACM/Springer/URSI Wireless Networks (WINE]), pages 439 - 450, Vol.12, Issue 4, July 2006.

[13] Ming Li, Anthony Alvarez, Francesco De Pellegrini, Imrich Chlamtac,B. Prabhakaran, "Design and Development of A Secure Real-timeMonitoring, Control, and Coordination Systems for Intelligent RobotSwarms", poster paper, ROBOCOMM 2007, October 2007.

[14] Y. Yang and R. Kravets, "Contention-Aware Admission Control for AdHoc Networks", UIUCDCS-R-2003-2337, May 2004.

[15] Q. Xue, A. Ganz, "Ad hoc QoS on-demand routing (AQOR) in mobilead hoc networks," Journal 0/Parallel Distributed Computing, Vol. 63,pp. 154-165,2003.