Localization of Autonomous Mobile Robots in a Cellular … · 2012-05-25 · Localization of Autonomous Mobile Robots in a Cellular Transport System Christof Röhrig, Member, IAENG,

Localization of Autonomous Mobile Robotsin a Cellular Transport System

Christof Röhrig, Member, IAENG, Christopher Kirsch, Julian Lategahn, Marcel Müller and Lars Telle

Abstract—The paper presents global localization for a swarmof autonomous mobile robots which transport Euro-bins ina distribution center or warehouse. Localization is realizedby sensor fusion of range measurements obtained from anIEEE 802.15.4a network and laser range finders. The IEEE802.15.4a network is used for communication as well as forglobal localization. Laser range finders are used to detectlandmarks and to provide accurate positioning for dockingmaneuvers. Range measurements are fused in a Monte CarloParticle Filter. The paper presents the design of the networkincluding the communication protocol together with the designof the localization algorithm. In order to support a largenumber of robots, the whole working area is divided into cellswhich use different frequencies. The network protocol provideshandover between the cells and routing capabilities in real-time.Experimental results are given to prove the effectiveness of theproposed methods.

Index Terms—Localization, IEEE 802.15.4a CSS, Au-tonomous Transport Vehicle, Swarm Intelligence

I. Introduction

SHORT production cycles and just-in-time inventory man-agement require flexible material flow as well as usage

of small transportation units. These demands can be met byusing small autonomous mobile robots which act as a swarm.Several companies have introduced small mobile robotsfor logistic applications. Examples are “The Kiva MobileFulfillment System (MFS)” [1], the “Self-Driving Courier”from Adept Technology [2], “RoboCourierTM” from swisslogand “ADAMTM (Autonomous Delivery and Manipulation)”[3]. Inexpensive localization of mobile robots is an importantissue for many logistic applications and object of currentresearch activities. The Kiva MFS uses bar codes on thefloor that can be detected with a camera by the robots[4]. These bar codes specify the pathways and guaranteeaccurate localization. Drawbacks of this solution are therisk of polluting the bar codes and the need for predefinedpathways which restrict the movements of the robots. The“Self-Driving Courier”, RoboCourierTM and ADAMTM arebased on the same technology, which was developed byMobileRobots in Amherst USA. These mobile robots useopen path navigation with laser range finders to travel totheir destination. Laser range finders can be used to trackthe position of an autonomous vehicle within a knownenvironment using a predefined map, if the initial positionis given, but it is difficult to find the initial position ina complex or dynamically changing environment withoutapriori information.

Manuscript received May 7., 2012; revised May 11., 2012.The authors are with the University of Applied Sciences and Arts in

Dortmund, Intelligent Mobile Systems Lab, Emil-Figge-Str. 42, 44227 Dort-mund, Germany, Web: http://www.imsl.fh-dortmund.de/en/, Email:{christof.roehrig, christopher.kirsch, julian.lategahn,marcel.mueller}@fh-dortmund.de

Fig. 1. Swarm of mobile robots in a distribution center c© Fraunhofer IML

The paper proposes an open path navigation, which isbased on sensor fusion of range measurements using anIEEE 802.15.4a wireless network, measurements of laserrange finders and dead-reckoning (odometry). The IEEE802.15.4a wireless network is used for communication andglobal localization (without apriori information), the laserrange finders and odometry are used for position trackingand improved local accuracy.

Fig. 1 shows the target application of the proposedcommunication and localization system. In this distributioncenter, mobile robots transport bins with Euro footprint(600x400 mm) from a high bay racking to order pickingstations and back to the racking. Order pickers collect theorders from Euro-bins and pack them into custom bins.This so called Cellular Transport System is based on theMultishuttle Move (MSM) technology [5]. MSM is a fusionof a conventional rack shuttle and a mobile robot developedby Fraunhofer-Institute for Material Flow and Logistics (FhGIML). The vehicles are rail-guided while they are locatedin the racking system or the lift. The vehicles are ableto leave the rail-system and to operate as mobile robotswith open path navigation. This scalable and flexible vehicleswarm concept is a compact, adaptable solution for highstorage capacity and covers the entire performance spectrumof facility logistics with the maximum possible flexibility [5].Since the robots navigate autonomously and act as a swarm,real-time communication and global localization is needed.

The paper proposes the usage of an IEEE 802.15.4aWireless Sensor Network (WSN) for communication as wellas for global localization. A WSN consists of spatiallydistributed autonomous sensor nodes for data acquisition. Anew communication protocol for WSN is developed whichis based on IEEE 802.15.4a and provides global localization,communication and routing in real-time. Since the data sizein an IEEE 802.15.4 frame is limited to 127 Bytes, low over-head of the protocol is one key requirement. Instead of usingthe superframe structure of IEEE 802.15.4, a new superframe

Engineering Letters, 20:2, EL_20_2_05

(Advance online publication: 26 May 2012)

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structure is developed, because IEEE 802.15.4 supports onlysuperframes with 16 equally sized time slots. Furthermorethe paper describes location tracking of mobile robots usingan Extended Kalman Filter and global localization using aMonte Carlo Particle Filter. Experimental results are given toprove the effectiveness of the proposed methods. This paperextends the work presented in [6] to global localization usinga Monte Carlo Particle Filter.

II. RelatedWorkUp to now several kinds of localization techniques have

been developed for the use in wireless networks. A review ofexisting techniques is given in [7]. These techniques can beclassified by: Connectivity, Received Signal Strength (RSS),Angle of Arrival (AoA), Time of Arrival (ToA) and Round-trip Time of Flight (RToF).

Connectivity information is available in all kinds of wire-less networks. The accuracy of localization depends on therange of the used technology and the density of the beacons.In cellular networks Cell-ID is a simple localization methodbased on cell sector information. In a WSN with short radiorange, connectivity information can be used to estimate theposition of a sensor node without range measurement [8].

RSS information can be used in most wireless technolo-gies, since mobile devices are able to monitor the RSS aspart of their standard operation. The distance between senderand receiver can be obtained with the Log Distance Path LossModel described in [9]. Unfortunately, the propagation modelis sensitive to disturbances such as reflection, diffractionand multi-path effects. The signal propagation depends onbuilding dimensions, obstructions, partitioning materials andsurrounding moving objects. Own measurements show, thatthese disturbances make the use of a propagation modelfor accurate localization in an indoor environment almostimpossible [10]. A method to overcome this disadvantage isfingerprinting which is introduced in [11] and uses a radiomap. Fingerprinting is divided in two phases: In the initialcalibration phase, the radio map is built by moving aroundand storing RSS values at various predefined points of theenvironment. In the localization phase, the mobile devicemoves in the same environment and the position is estimatedby comparing the current RSS values with the radio map.Other approaches use a Bayesian algorithm [12] or Delaunaytriangulation with lines of constant signal strength [13]. Themain disadvantage of radio map based methods is the highmanual effort to build the map in the calibration phase. Theuse of Delaunay triangulation and interpolation allows a radiomap with a low density of calibration points and reduces thetime for manual generation of the map [10]. However, theaccuracy of RSS based methods is insufficient for the targetapplication.

AoA determines the position with the angle of arrival fromfixed anchor nodes using triangulation. In [14] a method isproposed, where a sensor node localizes itself by measuringthe angle to three or more beacon signals. Each signalconsists of a continuous narrow directional beam, that rotateswith a constant angular speed. Drawback of AoA basedmethods is the need for a special and expensive antennaconfiguration e.g. antenna arrays or rotating beam antennas.

ToA and RToF estimate the range to a sender by measuringthe signal propagation delay. The Cricket localization system

[15] developed at MIT utilizes a radio signal and an ultra-sound signal for position estimation based on trilateration.TDoA of these two signals are measured in order to estimatethe distance between two nodes. This technique can be usedto estimate the position of a node in a WSN [16] or totrack the position of a mobile robot [17]. Ultra-Wideband(UWB) offers a high potential for range measurement usingToA, because the large bandwidth (> 500 MHz) provides ahigh ranging accuracy [18]. In [19] UWB range measure-ments are proposed for tracking a vehicle in a warehouse.The new WSN standard IEEE 802.15.4a specifies two op-tional signaling formats based on UWB and Chirp SpreadSpectrum (CSS) with a precision ranging capability [20],[21]. Nanotron Technologies distributes the nanoLOC TRXTransceiver with ranging capabilities using CSS as signalingformat.

Compared to the large number of published researchfocused on localization, there is less research on protocolscombining localization and communication. In [22] a MACprotocol with positioning support is described. This work ismainly focused on energy efficient medium-access. A MACprotocol combining localization and communication based onIEEE 802.15.4a is described in [23] and [24]. The protocol iscontention-based and did not support real-time localization.WirelessHART is based on IEEE 802.15.4 and offers real-time communication using TDMA, but it did not supportranging [25], [26].

In order to increase the accuracy of wireless localizationtechniques, sensor fusion with complementary sensors canbe used. In [27] a sensor fusion of RSSI obtained from aWSN with computer vision is proposed. Sensor fusion ofRSSI obtained from a Wireless LAN and laser range findersis presented by [28]. In that paper the authors propose ahierarchical method which uses the Ekahau location enginefor room level localization in the first step and a laser rangefinder for local localization in the second step.

III. The nanoLOC Localization SystemNanotron Technologies has developed a wireless tech-

nology which can work as a Real-Time Location System(RTLS). The distance between two wireless nodes is de-termined by Symmetrical Double-Sided Two Way Ranging(SDS-TWR). SDS-TWR allows a distance measurement bymeans of the signal propagation delay as described in [29].It estimates the distance between two nodes by measuringthe RToF symmetrically from both sides.

The wireless communication as well as the rangingmethodology SDS-TWR are integrated in a single chip, thenanoLOC TRX Transceiver [30]. The transceiver operatesin the ISM band of 2.4 GHz and supports location-awareapplications including Location Based Services (LBS) andasset tracking applications. The wireless communication isbased on Nanotron’s patented modulation technique ChirpSpread Spectrum (CSS) according to the wireless standardIEEE 802.15.4a. Data rates are selectable from 2 Mbit/s to125 kbit/s.

SDS-TWR is a technique that uses two delays which occurin signal transmission to determine the range between twonodes. This technique measures the round trip time andavoids the need to synchronize the clocks. Time measurementstarts in Node A by sending a package. Node B starts its



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Fig. 2. Symmetrical Double-Sided Two Way Ranging [30]

measurement when it receives this packet from Node A andstops, when it sends it back to the former transmitter. WhenNode A receives the acknowledgment from Node B, theaccumulated time values in the received packet are used tocalculate the distance between the two stations (Fig. 2). Thedifference between the time measured by Node A minusthe time measured by Node B is twice the time of thesignal propagation. To avoid the drawback of clock drift therange measurement is preformed twice and symmetrically.The signal propagation time td can be calculated as

td =(T1 − T2) + (T3 − T4)

4, (1)

where T1 and T4 are the delay times measured in node Ain the first and second round trip respectively and T2 andT3 are the delay times measured in node B in the first andsecond round trip respectively (see Fig. 2). This double-sidedmeasurement zeros out the errors of the first order due toclock drift [29].

IV. Network Architecture and Protocol Design

The protocol supports communication and localization inreal-time. Owing to this requirement, the medium-access isdivided into different time slots (TDMA). In order to providereal-time communication for a large number of mobile robotsthe whole working area is divided into three cells which usedifferent frequencies (FDMA).

A. Network Architecture

Fig. 3 shows the architecture of the whole system. Mobilerobots transport Euro-bins containing one sort of goods froma high bay racking to order picking stations and back to theracking. Order pickers collect the orders from Euro-bins andpack them into custom bins. In order to navigate from highbay racking to order picking stations the robots localize itselfusing IEEE 802.15.4a ranging to at least three anchor nodes.IEEE 802.15.4a range measurements are not precise enoughto allow docking maneuvers at order picking stations. Fordocking maneuvers the range measurements can be fusedwith measurements obtained from a safety laser range finder.

AG

V AG

V

AG

V

AG

V

AG

V

AG

V

high bay racking

= anchor node

= control unit

= master node +anchor node

Switch

ord

er p

icking

station

AGV

AGV

AGV

AGV

AGV

AGV

AGV

AGV

ord

er p

icking

station

ord

er p

icking

station

ord

er p

icking

station

Fig. 3. Wireless network with three cells and router

Every cell consists of a master node and three anchornodes. The master node controls the medium-access in its celland acts also as anchor node. Master nodes are connected toa distributed system (Ethernet) for routing purposes. Routingis executed by a central control unit which is connected tothe warehouse management system. The control unit storesa routing table with all robots connected to a cell. Thewarehouse management system sends transport orders to therobots and monitors their state.

B. Protocol Design

The network protocol supports different services:• Ranging: Every mobile node in a cell (mobile robot)

uses this service to obtain range measurements to anyother node in the cell. Usually a mobile node executesranging to the master node and three anchor nodesduring its time slot. To optimize localization accuracymobile nodes can execute ranging to robots at fixedpositions e.g. docking stations.

• Data Transmission: Nodes are addressed with 16 Bitaddresses (8 Bit type, 8 Bit ID), where mobile nodesown the same type. Every node can send messages toother nodes during its time slot. Messages to nodes in adifferent cell are routed through the master node of thesource cell via the control unit and the master node ofthe destination cell to the target node.

• Time Slot Request, Release: Before executing otherservices, a mobile node has to assign to a cell andrequest a time slot. After service, a mobile node releasesits time slot.

• Handover: During their way from the racking to thepicking stations, robots can travel through different cells.The mobile nodes execute a handover to change a cell,after their position has moved to another cell. Handoveris triggered through the position of a mobile node andrequested by the mobile node.

The master node controls the medium-access in its cell andsend a time slot table in regular intervals as a broadcast. Thetime slot table contains a time slot for any connected nodetogether with free time slots for concurrent medium-access(CSMA/CA). Fig. 4 shows the format of the superframe aswell as a time slot table. Every node that is in range of a



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LEN 0x01

1 1 1

PT_TDMA_TIME_SLOT_TABLE

1

0x01 0xFF

1 1

0xFFmastertimeslot

3 0 1 … 0

paket header timeslots: masterslot + n agv slots

1 1 1111

mastertimeslot

3 0 1 … 0 = free communication

timeslots: masterslot + n agv slots

Fig. 4. Time slot table and superframe design

cell receives the time slot table and synchronized its real-time clock. The time slot table includes occupied slots andslots for free communication (CSMA/CA). The first time slotin a superframe is always occupied by the master node. Thetime slots are marked with the address of the nodes (8 BitID), free slots are marked with 0. Since all nodes receive thetime slot table, they know every node connected to the celland can transmit data during their time slot directly.

Master Slave1 Slave2 Slave3

Timeslot table

Subscribe timeslot

Timeslot table

Masterslot

Slot1

Slot2

Slot3

Free Slot

Masterslot

Slot1

Slot2

Slot3

Free Slot

Waiting Masterslot Slaveslot Blocked Free Communication

Fig. 5. Allocation of time slot

When a mobile node needs to connect to a cell, it waitsfor the master slot table and sends a request in the first freeslot. Media access in free slots are controlled by CSMA/CA.The last slot at the end of the superframe is never allocatedby the master node and therefore always free. Fig. 5 shows asequence chart with a time slot allocation. At the beginningof the first superframe, the master node broadcasts a timeslot table, in which mobile node Slave1 occupies the firsttime slot and Slave2 occupies the second time slot. Slave3sends a time slot request in the first free slot (Slot3). At thebeginning of the next superframe, the master node broadcastsa new time slot table, in which Slave3 occupies Slot3.

When a mobile robots travels from one cell to anothercell, it must change the frequency and request a time slotin the new cell. The protocol supports this procedure with ahandover service. The handover service is requested by themobile node and triggered by its position. Fig. 6 shows asequence chart of the handover procedure. The robot requestsa handover from Cell1 to Cell2. It sends a handover requestto the master node of Cell1. The master node of Cell1 sends ahandover time slot request via distributed system and controlunit (router) to the master node of Cell2. The master node of

Cell2 confirms the handover time slot request with a messageto master node of Cell1 which confirms it to the robot. Themobile node on the robot changes its frequency and waits forthe start of the superframe in Cell2 and the time slot table. Inits time slot it sends a handover done message to the masternode of Cell2, which sends a handover delete message to themaster node of Cell1. The master node of Cell1 releases thetime slot of the robots. Master node of Cell2 send a messageto the control unit (router), to update the routing table. Afterthis last update the handover procedure is completed.

Since the assignment to a cell depends on the positionof the mobile node, a mobile node has to localize itself,before requesting a time slot in a cell. During initializationof a mobile node its position is unknown. Fig. 7 shows asequence chart of the initialization procedure of a mobilenode and the assignment to the correct cell. In the first stepa mobile node changes its frequency to the Cell1. It waitsfor a free time slot and executes ranging to the master nodeof Cell1. If the obtained range to this master node is smallerthan the width of Cell1 it determines Cell1 as the correct cell.If not, the mobile node changes its frequency to the Cell2and executes ranging to the master node of Cell2. After thatstep, the mobile node can localize itself with bilateration andconsequently assign to the correct cell.

V. Location Tracking Using the Extended Kalman Filter

The Kalman Filter is an efficient recursive filter, whichestimates the state of a dynamic system out of a series ofincomplete and noisy measurements by minimizing the meanof the squared error. It is also shown to be an effective toolin applications for sensor fusion and localization [31].

The basic filter is well-established, if the state transitionand the observation models are linear distributions. In thecase, if the process to be estimated and/or the measurementrelationship to the process is specified by a non-linearstochastic difference equation, the Extended Kalman Filter(EKF) can be applied. This filtering is based on linearizing anon-linear system model around the previous estimate usingpartial derivatives of the process and measurement function.

The Extended Kalman Filter is suitable to track the x- andy-position of a mobile system using measured distances toartificial landmarks (anchors). To estimate the initial positionof a mobile system, at least three distances are necessary.Using trilateration the anchor distances ri are calculated asfollow:

ri =

√(px − ax,i)2 + (py − ay,i)2

, (2)

where (ax,i, ay,i) are the x- and y-positions of anchor i and(px, py) represents the x- and y-position of the mobile systemto be located.

To gain the unknown initial position, equations (2) aresolved for px and py, and are transformed in matrices:

H ·(px

py

)= z with H =

2 · ax,1 − 2 · ax,2 2 · ay,1 − 2 · ay,2

......

2 · ax,1 − 2 · ax,n 2 · ay,1 − 2 · ay,n

,

and z =

r2

2 − r12 + ax,1

2 − ax,22 + ay,1

2 − ay,22

...rn

2 − r12 + ax,1

2 − ax,n2 + ay,1

2 − ay,n2

,

(3)



______________________________________________________________________________________

PT_HANDOVER_REQUEST

AGV Master Cell 1 Control Unit Master Cell 2

change cellto cell 2

PT_HANDOVER_TIMESLOT_REQUEST

PT_HANDOVER_TIMESLOT_ANSWER

PT_HANDOVER_COMMAND

PT_HANDOVER_DONE

PT_HANDOVER_DELETE

PT_STATUS_HANDOVER_UPDATE

Fig. 6. Sequence chart of handover procedure

AGV Master Node 1

TIME_SLOT_TABLE

free communicationRanging

change Frequencesave distance 1

Master Node 2

IF (distance < MCdistance)Register Slot

free communication

ELSE

TIME_SLOT_TABLE

Ranging

save distance 2

bilateration(distance 1,distance 2)

determinedMaster

TIME_SLOT_TABLE

Register Slot

free communication

change frequenceto determinedmaster frequence

free communication

Fig. 7. Initialization and cell assignment

where n is the overall number of anchor nodes. Eqn. 3 canbe solved using the method of least squares:(

px

py

)= (HTH)−1HT · z (4)

For location tracking using EKF, Eqn. (3) needs only to besolved for the initial position estimate x0. The EKF addressesthe general problem of estimating the interior process stateof a time-discrete controlled process, that is governed bynon-linear difference equations:

xk+1 = f (xk,uk,wk),yk+1 = h(xk+1, vk+1). (5)

The state vector contains the position of the mobile roborxk = (px, py)T. The optional input control vector uk =

(vx, vy)T contains the desired velocity of the robot. Thesevalues are set to zero, if the input is unknown. The ob-servation vector yk represents the observations at the givensystem and defines the entry parameters of the filter, in thiscase the results of the range measurements. The processfunction f relates the state at the previous time step k tothe state at the next step k + 1. The measurement function hacts as a connector between xk and yk. The notation xk andyk denotes the approximated a priori state and observation,

xk typifies the a posteriori estimate of the previous step.Referring to the state estimation, the process is characterizedwith the stochastic random variables wk and vk representingthe process and measurement noise. They are assumed tobe independent, white and normal probably distributed withgiven covariance matrices Qk and Rk. To estimate a processwith non-linear relationships the equations in (5) must belinearized as follow:

xk+1 ≈ xk+1 + Fk+1 · (xk − xk) + Wk+1 · wk

yk+1 ≈ yk+1 + Ck+1 · (xk+1 − xk+1) + Vk+1 · vk+1,(6)

where Fk+1,Wk+1,Ck+1 and Vk+1 are Jacobian matrices withthe partial derivatives:

Fk+1 =∂ f∂x (xk,uk, 0) Wk+1 =

∂ f∂w (xk,uk, 0)

Ck+1 = ∂h∂x (xk+1, 0) Vk+1 = ∂h

∂v (xk+1, 0).(7)

Because in the analyzed system the predictor equation con-tains a linear relationship, the process function f can beexpressed as a linear equation:

xk+1 = Fxk + Buk + wk, (8)

where the transition matrix F and B are defined as:

F =

(1 00 1

), B =

(T 00 T

),

(9)



______________________________________________________________________________________

where T is the constant sampling time.The observation vector yk contains the current measured

distances:yk =

(r1 · · · rn

)T. (10)

The initial state estimate x0 is calculated based on (3). Forthe subsequent estimation of the position x = (px, py) thefunctional values of the non-linear measurement functionh must be approached to the real position. The functionh comprises the trilateration equations (2) and calculatesthe approximated measurement yk+1 to correct the presentestimation xk+1. The equation yk+1 = h(xk+1, vk+1) is givenas:

r1...

rn

=

√

( px − ax,1)2 + ( py − ay,1)2

...√( px − ax,n)2 + ( py − ay,n)2

+ vk+1 . (11)

The related Jacobian matrix Ck+1 = ∂h∂x (xk, 0) describes the

partial derivatives of h with respect to x:

Ck+1 =

∂r1∂px

∂r1∂ py

......

∂rn∂px

∂rn∂ py

with

∂ri∂ px

=px−ax,i√

(px−ax,i)2+( py−ay,i)2

∂ri∂ py

=py−ay,i√

(px−ax,i)2+( py−ay,i)2.

(12)

Given that h contains non-linear difference equations theparameters ri as well as the Jacobian matrix Ck+1 must becalculated newly for each estimation.

VI. Global Localization Using theMonte CarloParticle Filter

The KF and EKF rely on the assumption, that motion andsensor errors are Gaussian and that the estimated positioncan be modeled by using a Gaussian distribution. Becauseof this fact, KF and EKF can not handle position ambiguities.Another method which is based on the Bayesian filter is aParticle Filter (PF). A PF can handle position ambiguitiesand does not rely on the assumption that motion and sensorerrors are Gaussian. Also PF can cope with multimodaldistributions. In a PF, a set S of N samples is distributedin the environment or at known places. A sample s isdefined by cartesian coordinates and an orientation. A widelyused PF for mobile robot localization is the Monte CarloParticle Filter (MCPF), which is described in [32]. Theestimated pose of a mobile robot and its uncertainty aboutthe correctness is represented by the samples. MCPF consistsof two phases: The prediction phase and the update phase.Inside the prediction phase the motion information ut areapplied on each sample si

t−1 (1 ≤ i ≤ N). The predictionphase is also called motion model. The result of the motionmodel is a new set of samples S t which represents thepositions, where the mobile robot could be after executing the movement ut.

Inside the update phase, the set of distance measurementsDt is used to assign each sample with an importance factor w.The importance factor complies the probability p(Dt | si

t,m),i.e. the probability of the distance measurements Dt at apoint in the environment defined by sample si

t and by usingthe information from the map m. In m the positions ofanchors and landmarks are stored. The result of the updatephase – also called measurement update – is the set ofsamples S t of the prediction phase with the corresponding

set of N importance weights wt. Both sets together representthe current position likelihood of the mobile robot. Afterthe update phase, the resampling step follows. Inside theresampling step, samples with a low importance weight areremoved and samples with a high importance factor areduplicated. The result of the resampling is the set S t of Nsamples which represents the current position of the mobilerobot. In the next time step, the set S t is used as S t−1. Thereare two possibilities to extract the pose of the mobile robotout of the sample set S t: The first method is to use theweighted mean of all samples and the second method is touse the sample with the highest importance factor. MCPFsflow chart is drafted in Fig. 8. The MCPF has the advantages

Global LocalizationdnanoLOC,t

Prediction phase ut

Update phase dlaser,tdnanoLOC,t

Resampling

start pose

S t

S t,w[N]t

S t = S t−1

Fig. 8. MCPF flow chart.

that it copes with global localization (no a priori information)and position tracking (given a priori information). The sensorfusion with some dependencies and special cases can beimplemented easily. Generally the combination of sensorspecific advantages and the compensation of sensor specificdisadvantages is called sensor fusion.

In this work the MCPF uses distance measurements fromthe IEEE 802.15.4a WSN for global localization. The globallocalization task, which has to be done before the MCPFstarts, is shown as a red block in Fig. 8. The probabilitydensity function of the IEEE 802.15.4a measurement errorwhich is used to compute the importance factor, is presentedin the next section.

A. IEEE 802.15.4a Measurement Model

In the update phase, the measurement model is usedto calculate the importance factor w for each sample s.The measurement model is the probability density functionp(dnanoLOC,k | si

k,m) which characterizes the measurementproperties and error. The measurement set dnanoLOC,k containsdistance measurements to A anchors. The density functiondepends on sensors and environment. To estimate the densityfunction for IEEE 802.15.4a distance measurements, lineof sight measurements to four anchors are taken while amobile robot moves a straight path between them. Whilethe robot moves, an accurate position was estimated by lasermeasurements to two walls. In Fig. 9, error histograms ofmeasurements to four anchors are shown. The error is thedifference between measured distance da

k and the Euclideandistance from robot position to anchor a.

The histograms show, that all measured distances are toolarge, the average error is 107 cm. The error depends on the



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0 200 4000

5

10

15

20

25

error in cm

fre

qu

ency in

%anchor 1

0 200 4000

5

10

15

20

25

error in cmfr

equ

ency in

%

anchor 2

0 200 4000

5

10

15

20

25

error in cm

fre

qu

ency in %

anchor 3

0 200 4000

5

10

15

20

25

error in cm

fre

qu

ency in %

anchor 4

Fig. 9. Error histograms of IEEE 802.15.4a distance measurements to fouranchors. The x-axis is the error in centimeter and the y-axis show theirfrequency in %.

position of the anchor and on the environment. The medianand standard deviation of the error distributions are differentbut they all have a Gaussian structure. Owing to that fact, itis possible, to use a Gaussian distribution as IEEE 802.15.4aprobability density function:

N(x, µ, σ2

)=

1√

2πσ2exp

(−12

(x − µ)2

σ2

)(13)

To calculate the importance weight of sample sik, the Eu-

clidean distance da,i∗k between this sample and the anchor a

is calculated as

da,i∗k =

√(xi − xa)2 + (yi − ya)2, (14)

where (xa, ya) is the position of anchor a and (xi, yi) are theCartesian coordinates of sample si

k. The Euclidean distanceand the measured distance da

k are used with an anchor specificconstant da

c in a fixed Gaussian distribution:

p(da

nanoLOC,k | sik,m

)= N

(da,i∗ − (da

k − dac ), 0, σ2

)(15)

where dac is the median of the distance errors shown in

the histograms. The advantages of this fixed Gaussian dis-tribution are, that a normalization during the localization,to guarantee

∑p = 1, is not needed and that the domain

can be restricted. This last advantage can be used to detectestimation failure. If a lot of samples are out of range, newsamples can be drawn in the environment. This fact enablesthe MCPF to re-localize the mobile robot.

The importance factor of a sample i is calculated with:

wik =

A∏a=1

p(da

nanoLOC,k | sik

)·

2∏l=1

p(dl

laser,k | sik

)(16)

The importance factor w is the product of the probabilityof measurements to A anchors and to two landmarks. The

probability p(dllaser,k |s

ik,m) is a fixed Gaussian with σ =

28 mm. The landmarks are equipped with reflectors, in orderto allow easy detection by the laser range finders. If nolandmarks are detected, the importance factor is equal to theprobability of the distance measurements to A anchors.

The next section presents the global localization approachwhich uses distance measurements of the IEEE 802.15.4aWSN.

B. Anchorbox

For global localization range measurements of the IEEE802.15.4a WSN are used to reduce the area in which particlesare distributed. This method is based on a technique whichwas presented in [33]. Fig. 10 shows an example of anAnchorbox which is computed by using range measurementsto four anchors. The red dot is a robot which is equipped witha node.

xw

yw

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xmin xmax

ymin

ymax

Fig. 10. Anchorbox example by using range measurements to four anchornodes with known positions.

In the first step of the MCPF the particles are distributedin the area defined through calculation specifications 17 and18, where (xi, yi) is the position of anchor i and di is theaverage of I range measurements.

xmin =I

maxi=1

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)xmax =

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)(17)

ymin =I

maxi=1

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)ymax =

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)(18)

One disadvantage of the Anchorbox approach is that theorientation can not be estimated by IEEE 802.15.4a rangemeasurements. To overcome this disadvantage more particleswith a random orientation are distributed at the beginningof the algorithm. After the particles are distributed therobot drives 1 m in positive x robot direction. Because ofthis technique, the adapted MCPF is an active localizationtechnique. Thereby, particles with an incorrect orientationremove themselves from the correct position and are gettinga lower importance factor during the first measurementupdate. Another possibility to overcome this disadvantageis to compute the position through trilateration during therobot drives the path. The orientation can be estimated bycomputing the average orientation between these trilaterationpoints. This approach is not used because these points varysignificantly and the estimated orientation will be erroneous.

The advantage of the developed technique is a smallerparticle cloud at the beginning of the algorithm. Because



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of this, the first position can be estimated faster. Anotheradvantage is that this technique can also be used to solvethe kidnapped-robot problem. To solve this problem, theAnchorbox can be computed in every MCPF cycle. Insidethis computed Anchorbox a sample subset can be distributedto represent a much larger area where the robot can be. Theadvantage of this approach is, that it does not rely on theodometry data, which is necessary to solve the kidnappedrobot problem.

VII. Implementation and Experimental Results

The protocol is designed for a distribution center with 50mobile robots and three cells.

A. Hardware

Fig. 11. Wireless sensor node for anchors and mobile tags

In order to fulfill the requirements of the target application,a wireless sensor board was developed that can be used as:• Mobile node (tag) on a mobile robot,• Fixed anchor node,• Master node with connection to the distributed system.The board is designed around a STM32 micro-controller

which includes an ARM Cortex-M3 core. The STM32 micro-controller provides interfaces and enough RAM and com-putational power to perform communication and locationtracking using EKF in real-time. IEEE 802.15.4a radio isbuilt with a nanoPAN 5375 module which supports up to20 dBm output power and three frequency channels with22 MHz bandwidth.

The architecture of the wireless sensor board is modular,only necessary components are assembled. Master nodes areequipped with a Xport to connect to an Ethernet. Mobilenodes are equipped with an IMU (inertial measurementunit) which increases localization accuracy of the robots.Mobile nodes are connected via CAN-bus to the robot’s PLC(programmable logic controller). Communication to the PLCis performed with CANopen protocol. As a fall back, theboards are equipped with a serial interface (RS-232).

B. Communication and location tracking using EKF

Several experiments have been conducted, to prove theimplementation of the protocol and the localization accuracy.Fig. 12 shows the result of a roaming experiment. The

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Fig. 12. Wireless synchronisation

robot moves from Cell1 to Cell2 and performs a handoverwhile crossing the boundary between the cells. The positiontracking of the mobile robot is estimated using the EKF asdescribed in section V, the initial position is calculated withtrilateration (Eqn. (3)). The blue dots in Fig. 12 show theposition tracked in Cell1, the black circles show the positionin Cell2. The position error near the border of the cellsare caused by bad radio conditions in this area due to thedirectional antennas of the anchors. In this experiment, onlyrange measurement using four anchors of each cell are usedfor tracking. The tracking error can be decreased, if odometryand laser range finders are included in the tracking algorithm[34].

C. Global localization Using MCPF

To evaluate the proposed MCPF global localization, someexperiments are conducted at the University of Applied Sci-ences and Arts in Dortmund with an omnidirectional mobilerobot, that is equipped with Mecanum wheels. The robot andit’s motion model is described in [34]. It is equipped with twoSICK S300 Professional laser range finders with a scanningangle of 270◦. With both laser range finders, the robotgets a full 360◦ scan of the environment. The laser rangefinders provide a resolution ∆α of 0.5◦. A docking stationfor handing over bins serves as landmark. Two pillars of thedocking station are equipped with reflectors, in order to alloweasy detection by the laser range finders. The mobile robotis also equipped with a IEEE 802.15.4a tag for ranging andcommunication purposes. At the margins of the environmentssix IEEE 802.15.4a anchors are placed. The figures 13(a)– (b) show the first steps of the global localization and theestimated positions with a comparison to the driven path andthe odometry data (dimensions in millimeter).

The mobile robot is moved in manual mode from astarting point into the docking station which is shown bythe black path in Fig. 13(a) and (b). The mobile robot ismoved forwards first, then sidewards and finally forwardsinto the docking station in the upper right corner, alwayswith the same orientation θ = 0◦. During the movement ofthe robot, all necessary sensor data for MCPF are stored.These values are odometry data, distance measurements to



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Fig. 13. Experimental results: (a) and (b) show results of global localization. (c) - (e) show the process of global localization with the Anchorboxapproach.

six IEEE 802.15.4a anchors and the laser range data. The firstmovement should simulate the first step of global localizationif the mobile robot acts in automatic mode with no apriori position information. The second and third movementrepresent the estimated path into the docking station, whichguarantees a precise localization.

To perform the global localization first of all the An-chorbox is computed by using distance measurements tosix anchors. Then 10000 samples with random orientationare distributed inside the Anchorbox. Fig. 13(c) shows theAnchorbox with the distributed sample set. The sample setafter the first movement part is shown in Fig. 13(d). It canbe seen, that samples with an incorrect orientation havemoved away from the real position. Subsequently the firstimportance factor is computed by using range measurementsof the WSN. The resulting sample cloud of the resamplingstep is shown in Fig. 13(e). The start pose is set to theweighted mean of the sample cloud.

After estimating the start position the MCPF segue fromglobal positioning into position tracking and the sample setis reduced to 2000 samples. The estimated start positiondepends on the IEEE 802.15.4a measurements and on theset of samples with random orientation.

The resulting path of the global localization and position

tracking is shown in Fig. 13(a) and (b). Owing to an unequalfloor contact, the robot has a large slippage when it movessideways. Fig. 13(a) shows odometry in blue and MCPFestimation using all sensor data in red. The sample cloudsresulting from position tracking (after the global localization)are presented in Fig. 13(b). Until the robot detects the land-mark pair, the importance factors were computed by usingthe range measurements of the WSN. This results in biggersample clouds and a higher position uncertainty, which canbe seen in Fig. 13(b). During the last movement the robotdetects the landmark pair with the two laser range finders.Because of this, the resulting sample clouds are compressedand the uncertainty of the estimated position is reduced. Theposition estimated by using IEEE 802.15.4a measurementsis good enough for planning the path and through usingthe sensor fusion a successful docking maneuver can beguaranteed.

VIII. Conclusions and future works

In this paper global localization of autonomous mobilerobots using IEEE 802.15.4a CSS and laser range finderswas proposed. A new communication protocol for a wirelessnetwork and a localization method using EKF and PCPFwas developed, implemented and tested. The network uses



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FDMA to divide the area into cells, TDMA for real-timecommunication and global localization within a cell andCSMA/CA for cell assignment and management services.A sensor node was developed which provides all functionsto act as a mobile node as well as as an anchor or a masternode. In the next step, the system will be implemented in ademonstration center with 50 mobile robots and three cells.

Acknowledgment

This work was supported by the Ministry of Innova-tion, Science and Research of the German State of NorthRhine-Westphalia (FH-Extra, grant number 29 00 130 02/12)and the European Union Fonds for Regional Development(EFRE). Furthermore the project was financially supportedby Nanotron Technologies GmbH in Berlin, Germany and theUniversity of Applied Sciences in Dortmund (HIFF, projectnumber 04 001 79).

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Christof Röhrig received his Diploma degreefrom the University of Bochum, Germany, in 1993,and his Doctor degree from the University ofHagen, Germany, in 2003, both in electrical en-gineering. Between 1993 and 1997 he was Man-ager Automated Systems Engineering at ReinoldusTransport- und Robotertechnik GmbH Dortmund,Germany. From 1997 until 2003 he was with theControl Systems Engineering Group at Universityof Hagen. Since 2003, he is Professor of ComputerScience at the University of Applied Sciences and

Arts in Dortmund, Germany, where he heads the Intelligent Mobile SystemsLab. His current research interests include mobile robots and localizationusing wireless technologies.

Christopher Kirsch received his Bachelor ofScience degree in 2009, and his Master of Sci-ence degree in 2012, both in computer sciencefrom the University of Applied Sciences and ArtsDortmund, Germany. Since 2009, he is working indifferent research projects at the Intelligent MobileSystems Lab, University of Applied Sciences andArts Dortmund, where he is currently workingtoward the Doctor degree. His current researchinterests include sensor data fusion, Bayes Filterand localization of mobile robots.

Julian Lategahn received his Diploma degreein 2008, and the Masters degree in 2011, bothin computer science from the University of Ap-plied Sciences Dortmund, Germany. Since 2009,he is working in different research projects atthe Intelligent Mobile Systems Lab, University ofApplied Sciences and Arts Dortmund, where heis currently working toward the Doctor degree.His current research interests include sensor datafusion, Kalman Filter and localization.

Marcel Müller received his Bachelor degree fromthe University of Applied Sciences Dortmund,Germany, in 2009, in computer science. Since2009, he is working in different research projectsat the Intelligent Mobile Systems Lab, Universityof Applied Sciences and Arts Dortmund, wherehe is currently working toward the Master of Sci-ence degree. His current research interests includeKalman Filter and people tracking.

Lars Telle received his Bachelor of Science degreein 2009, and the Master of Science degree in2011, both in computer science from the Uni-versity of Applied Sciences and Arts Dortmund,Germany. Between 2009 and 2011 he was workingat the Intelligent Mobile Systems Lab, Universityof Applied Sciences and Arts in Dortmund. Heis currently working for Leopold Kostal GmbH &Co. KG in Lüdenscheid, Germany.



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Localization of Autonomous Mobile Robots in a Cellular … · 2012-05-25 · Localization of Autonomous Mobile Robots in a Cellular Transport System Christof Röhrig, Member, IAENG,