co-design for wireless networked control...

CO-DESIGN FOR WIRELESS NETWORKED CONTROL OF ANINTELLIGENT MOBILE ROBOT

Amine Mechraoui, Zeashan Hameed Khan, Jean-Marc Thiriet and Sylviane GentilGipsa-Lab, Control departement INPG-UJF-CNRS, BP 46, 38402 Saint Martin d’Heres, France

amine.mechraoui, zeashan-hameed.khan, jean-marc.thiriet, [email protected]

Keywords: Autonomous mobile robots, Networked control systems, Wireless network, 802.15.4 protocol, Horizontalhandoff, TrueTime simulation.

Abstract: This paper describes a wireless network based control of a Khepera mobile robot moving in a distributedinfrastructure. Due to critical dependence on wireless communication, a procedure for reconfiguration of thenetwork is proposed as a possibility to maintain communication between control station and the mobile robotin a successful manner. The network handoff is made under a criterion that takes into account key applicationdependent performance parameters. The controlled system and the communication network are simulatedrespectively with Matlab/Simulink and TrueTime.

1 INTRODUCTION

Networked control systems (NCS) in mobile roboticsare getting very popular today. With the rapidprogress in communication techniques, especially thewireless networks, distributed control and decisionhave become mandatory to reduce the onboard pro-cessing overhead. It includes control, decision, obsta-cle avoidance etc; as it effects the battery consump-tion in miniature robots with space and weight as keydesign constraints. However, introducing a wirelessnetwork in the control loops presents some disadvan-tages such as band limited channels, sampling delaysand packet dropouts (Hespanha et al., 2007). Further-more, the mobility of the robot also adds some prob-lems, e.g. increasing the distance between the con-trol station and the robot increases the number of lostpackets due to decreased signal strength and increasedbit error rate (BER) (Zhu et al., 2005).The communication architecture in mobile roboticsmay be centralized, in which case there is a fixedor mobile node, which communicates with all theother nodes, or decentralized, where individual mo-bile nodes should ideally operate without any centralcontrol (Schwager et al., 2007). In the decentralizedcontrol scheme, each component solves a part of theproblem and shares memory without having a globalview of the mission. There is less emphasis on com-putation than communication. In distributed controlsystems, communication is an important parameter

and individual components don’t need to share mem-ory (Martinez et al., 2007). In related research work,many approaches have been used for distributed con-trol of mobile robots. In (Fierro and Lewis, 1996) thedynamic model of the mobile robot is controlled bymeans of neural networks. In (Aicardi et al., 1995)and (canudas de Wit and Sordalen, 1992), a nonlinearcontrol approach has been introduced. Another re-search area, related to the hybrid architecture of con-trol for autonomous navigation robots is studied in(Benzerrouk et al., 2008).The objective of this paper is to define a communi-cation architecture for a mobile robot moving in a2D space with several fixed stations (wireless infras-tructure communication). According to the positionof the robot, communication is possible in a specificcoverage area with one or several stations (see Fig.1). When several stations are reachable, the robotwill choose one of the stations (Horizontal Handoff(HHO) strategy (Wang et al., 2007)) that can allocatesufficient resources to ensure a good level of commu-nication. This comprises of optimal choice of payloadand delay based on distance between robot and stationand thus maintaining the necessary Quality of Service(QoS) in order to ensure that the Quality of Control(QoC) is sufficient (distributed control). When thereis no station in the reachable space, the robot willhave to be absolutely autonomous (embedded con-trol), maintaining a sufficient QoC despite a degrada-tion of the communication QoS. The QoC in wireless

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Figure 1: Problem description.

NCS is defined as the performance delivered by eachclosed-loop operation. Stability is the main propertythat must be guaranteed but control error and responsetime are also important to analyse.In the literature, many researchers have proposed aHHO strategy. They proposed making a decisionby taking into account the Received Signal Strength(RSS), the power consumption and the cost of com-munication (Chen et al., 2004). We propose to addthe packet dropouts caused by the propagation delayor the distance and orientation between the robot andthe station, which have a consequence on the QoC.The paper is organized as follows. The second sectionpresents a brief description of the model of the Khep-era robot, notably the kinematic model and the dy-namic one. In the third section, the controller designis described and simulations of the tracking trajectoryare presented. Section 4 presents the control over Zig-Bee wireless network and the influence of the inte-gration of this network on control performance. Afterthat, the proposed HHO architecture is described withone, two and three stations. Finally, the conclusionand perspectives are presented.

2 MODELING AND CONTROL

This section presents the study of a unicycle Kheperarobot (Lambercy and Caprari, 2007). Consider a uni-cycle robot (Khepera) as shown in Fig. 2. Let x, y andθ be the state variables where x ∈ℜ and y ∈ℜ are theCartesian coordinates, θ ∈ [0,2π[ is the robot’s orien-tation with respect to the X-axis. We consider ‘v’ and‘ω’ respectively as the linear and the angular veloci-ties of the robot. The kinematic of the robot can bemodeled as

x = vcosθ, y = vsinθ, θ = ω (1)

The kinematics model of the mobile robot has twocontrol inputs ωle f t and ωright i.e. the left and rightwheels velocities. These are related to the linear ve-locity v and the angular velocity ω of the robot ac-cording to the following equations

Vright = v+Rω, Vle f t = v−Rω (2)

where R is half the distance between the two robot’swheels. The dynamic model of the robot wheels is

characterized by the equations of the DC motors driv-ing the wheels. They are represented by a first ordermodel

ω∗(le f t,right)

U=

Kτs+1

(3)

where U is the voltage applied to the motor andω∗(le f t,right) are angular velocities generated by eachmotor. τ is the time constant (τ = 0.63s) and K is thegain (K = 5.3).

2.1 Controller Design

The objective of this section is to present how to con-trol the robot to track any trajectory. Two levels ofcontrollers are required. The first one is needed tocontrol the angular velocities of the motors. PI con-trollers are implemented. The second one controlsthe linear and angular velocities of the robot. Letus consider the controller presented in (Toibero et al.,2007), where the robot can reach a desired target point[xd yd θd ]. Errors are defined as

x = xd− x, y = yd− y (4)

and the tracking error (Eq. 5a) and the orientationerror (Eq. 5b) are calculated as

d =√

x2 + y2 (5a)

θ = θd−θ = tan−1(y/x)−θ (5b)

According to (Toibero et al., 2007), the followingcontrol actions are defined

v =vmax

1+ |d|d cos(θ) (6a)

ω =vmax

1+ |d|cos(θ)sin(θ)+K

θtanh(kθθ) (6b)

where vmax is the maximum linear velocity that therobot can reach (vmax = 0.3m/s) and K

θ,kθ are con-

stants. Those controllers are stable according to(Toibero et al., 2007) using the Lyapunov candidatefunction Vt .

Vt = θ2/2+d2/2 (7)

Figure 2: Robot Model.

CO-DESIGN FOR WIRELESS NETWORKED CONTROL OF AN INTELLIGENT MOBILE ROBOT

319

Figure 3: Real trajectory of the robot.

Fig. 3 shows the reference trajectory with initial con-ditions [X0,Y0,θ0] = [0,10,0] and the real trajectoryof the robot using those controllers. X , Y and θ aremeasured each sampling time (Ts = 0.03s). These re-sults are obtained with Matlab/TrueTime. The PI con-trollers are discretized and the voltage applied to themotor is obtained with a zero order hold. The trajec-tory is known in advance and the references xd , yd andθd are updated every 0.28s.

3 CONTROL OVER NETWORK

In this section, the controller is digitized and Zigbeewireless network is integrated (see Fig. 4). The ef-fects of network on control system are analyzed.

3.1 WPAN 802.15.4

ZigBee is a specification for small, low-power digitalradios based on the IEEE 802.15.4 standard for wire-less personal area networks (WPANs). The low costallows the technology to be widely deployed in wire-less control and monitoring applications. The lowpower-usage allows longer life with smaller batter-ies. The mesh networking provides high reliabilityand larger range. The main reasons for adopting thiswireless network in our application is its low powerconsumption and its ad-hoc networking capabilities.

Figure 4: Control over Zigbee network.

3.2 Control Over Zigbee

Control feedback loops are closed through a real-timenetwork as shown in Fig. 4.To perform this study, the following conditions areconsidered:• A bit rate of 250 kb/s for Zigbee in the physical

layer.• The sensor flow and the controller flow use 248

bits each. The sensor flow uses 3.3% and the con-troller one uses 3.3% of the network capacity witha sampling period Ts equal to 30 ms.

TrueTime simulator is used to simulate Zigbee Net-work. Two tasks are programmed, the first is the con-troller task that generates the controller flow and thesecond one is a periodic sensor task that generatessensors flow. The controller task is event-triggered,which means that the controller calculates and sendsthe control signals Vk and ωk when it has received allmeasures Xk,Yk and θk. With the TrueTime Simula-tion of ZigBee Network, there is only the CAP period(Contention Access Period which uses CSMA/CAprotocol), therefore there is no priority mode like CFP(Contention Free Period) which allows GuaranteedTime Slots (GTS) (see (van den Bossche et al., 2007)for more information about 802.15.4 protocol).The sensor and control data are critical to keep thestability of the system, hence packet losses are unde-sirable. Practically, loss of packets cannot be entirelyeliminated in wireless networks. A lot of methods e.g.Forward Error Correction (FEC) (Kurose and Ross,2004) are proposed to reduce the probability of packetlosses. The retransmission of lost packets is proposedtoo but for a real-time application, this is an inappro-priate solution. When a packet has been discovered tobe lost and is retransmitted, the sensors state evolve toa newer one and thus the retransmitted packet will bebased on the old information and the calculated con-trol will be wrong. The solution to ensure the bestcontrol is to do over sampling, with a sampling ratehigher than what is needed, which therefore makescontrol more tolerant to packet losses. However,this solution increases the Use Request Factor (URF)and causes more packet delays and losses (Mechraouiet al., 2008). The minimal sampling period to ensurea good QoC is 0.3 s in our example. To increase theQoC over wireless network and decrease the proba-bility of losing packets of critical data, the samplingperiod of 30 ms is required. Fig. 5 shows that therobot can communicate with the station only withinthe coverage range of the station. When it losses thecommunication, it keeps the last value of the linearand angular velocities and it deviates from the refer-ence trajectory. The progress of WPAN and the util-

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

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Figure 5: Trajectory of the robot with one Zigbee station.

isation of this kind of wireless networks allow therobot to find another station (PAN) to communicatefor control. Therefore, we assume that we have dif-ferent stations that the robot can communicate with.The next section explains how to change stations toensure sufficient QoS.

4 STATION SELECTION

4.1 Handoff Decision

For several stations, mobility management is pro-posed through redundancy in network communicationby searching for the best available protocol based ona cost function. Horizontal and vertical handoff canbe proposed. A horizontal handoff (HHO) is the linktransfer between two network access points that usethe same network technology while vertical handoff(VHO) is between two network access points usingdifferent network technologies. (Chen et al., 2004)describes a smart decision model to decide the bestnetwork interface and best time for VHO. (Stevens-Navarro and Wong, 2006) gives a comparison be-tween four different VHO decision algorithms w.r.tto bandwidth and delay for different traffic classes.(Wang et al., 1999) describes a policy-enabled hand-off system that allows users to express policies, basedon the estimation of current network conditions anddetermining the best network at any moment by char-acteristics e.g. cost, performance and power con-sumption. (Nkansa-Gyekye and Agbinya, 2007) pro-poses a distributed additive weighting based VHOmechanism to reduce the processing overhead in themobile terminal by delegating the calculation of hand-off metrics for network selection to the visiting net-works. (Angermann and Kammann, 2002) evaluatesdifferent structures for cost functions and presents asimulation for the influence of various parameters.

Figure 6: Network HHO strategy for a mobile robot control.

The challenge in handoff is to maintain the applica-tion session alive while the physical connection inter-face is changed. A generic weighted ‘Network RatingFunction’ evaluation for each network is based on

NRFi = WRSSINRSSI,i +WNLNNL,i +WDND,i (8)

WRSSI +WNL +WD = 1 (9)

where WRSSI , WNL and WD are weighting functionsof RSSI, network load (NL) and delay (D) respec-tively. NRSSI,i, NNL,i and ND,i indicate the scores ofinterface. NRFi is between 0 and 1. Wi is the weightof the factor i, which emphasizes the importance ofeach contributing factor and Ni, j represents the nor-malized score of the interface ′ j′ for factor ′i′.

The embedded logic in the mobile robot, assignsdifferent “weights” to the handoff decision parame-ters in order to determine the level of importance ofeach parameter for the information to be exchangedbetween neighbors. In our case WRSSI > WNL > WD.The best station connection interface at any given mo-ment is then chosen as the one that achieves the high-est score among all candidate interfaces.In our scenario, factors within Ni, j include QoS pa-rameters i.e. the RSSI, the network load (NL) andDelay or latency (D) of the candidate network. Nowwe calculate NRFi and NRFj to choose accordingly(which ever is greater) the station that maximizes the

Figure 7: HHO state diagram.


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decision criteria (Fig. 7).Additionally, there is a corresponding function foreach term NRSSI,i, NNL,i, and ND,i, and the ranges ofthe functions are bounded between 0 and 1. The func-tions are given in Eq. (10).

NRSSI,i =ePr

ePs(10a)

NNL,i = 1/eNL | 0≤ NL≤ 1 (10b)

ND,i =

{1 if Si is the current station & RSSI >−48dBm

0 if not

(10c)

In our case, the transmitted power is equal to −3dBmand the receiver signal threshold is equal to−48dBm.

The RSSI is calculed as follow

Pr =1

dαPs (11a)

RSSI(dBm) = 10log(Pr(mW )) (11b)

where Pr and Ps are the power in mW received andsent respectively. d is the distance between the twonodes in meters, and α is a parameter that can be cho-sen to model different environments.Fig. 8 shows the trajectory of the robot with changesin station if necessary (QoS is the best to ensure thebest QoC). The stations positions are

• For the first station (S1) (0, 5) (m)

• For the second station (S2) (-3, 30)

• For the last station(S3) (40, 35).

For this experiment, the weight values are WRSSI =0.8, WNL = 0.15 and WD = 0.05.We assume that S1 and S2 have a common coveragearea, and S3 is far. According to the initial position ofthe robot (here (X0,Y0,θ0) = (0,0,0)) the robot com-municates first with S1. It continues its trajectory un-til the robot detects the second station. In this case,Khepera executes Algorithm 1.To understand this algorithm, symbols are used. r isthe mobile robot, Si is the current station, S{1,2,..k} areall the stations and S{m,...,n} are the stations that aredetected by the robot. We note the fact that the robotdetects a set of stations by the symbol ./.

When the robot is out of range of all stations, itkeeps the last values of the angular and the linear ve-locities which is not a good solution because it canresult in an important error with respect to the refer-ence trajectory and cause problems (see Fig.8). Wepropose in this case to change the controller and usean embedded one.Figures 10 and 11 show the change of stations. In

Algorithm 1: Decision algorithm.if r ./ {Sm, ...,Sn} | m ∈ {1, ...,k} andn ∈ {1, ...,k} | m 6= n then

calculate NRF for all Sm, ...Snfor all i and l ∈ {m, ...,n} do

if NRFSi < NRFSl theni = l

else if NRFSi > NRFSl theni = iWait

elsei = i

end ifend for

end if

Figure 8: Trajectory of the robot with HHO with NL=0.

Figure 9: Evolution of the linear and angular velocities.

Fig.10, the simulation is made with NL=0 for all sta-tions. The change from S1 to S2 depends thus on thedistance between the robot and the station. The robotchanges the station only if the RSSI of the current sta-tion is poor. In Fig.11, the simulation is made underthe assumption that the NLS1 = 0.8 which means thatthe network is 80% loaded and NLS2 = 0.2. In thisscenario the robot decides to change the station ac-cording to the RSSI of both stations and the network


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Figure 10: Evolution of communication with different sta-tions with HHO with NL=0 (corresponds to Fig.8).

Figure 11: Evolution of communication with different sta-tions with HHO with NLS1 = 0.8 and NLS2 = 0.2.

load of each station. Comparing Fig.10 and Fig.11 theHHO of the station is quite sensitive to parameter NL.

4.2 Control Decision

As the robot moves outward towards another controlstation, the signal strength decreases. The low valueof RSSI is sensed by the mobile robot, which then re-quests the station for an estimation of next way-point.The control station sends the trajectory information aswell as broadcasts the “standby” message to all con-trol stations in the infrastructure to minimize the timeof connection of robot for the next cell. Thus, a PANslot is reserved in advance for the coming robot asthe trajectory is already estimated. This propositionpermits to maintain a sufficient QoC despite a degra-dation of the QoS.The embedded control design is the same than thedistributed one. The only difference is that the sam-pling period is changed. The controller computescontrol signals each 0.3 s. This change is very im-portant to reduce the energy consumption and alsothe execution time. Algorithm 1 and 2 is executed(“poor” in Algorithm 2 means that the RSSI of thecurrent station comes near the receiver signal thresh-old). Fig.12 show the simulation when the control isswitched within the non covered area.

Figure 12: The trajectory of the robot with switching con-troller.

Figure 13: Evolution of linear and angular velocities of therobot with switching controller.

5 CONCLUSIONS

This work aims to study, in a co-design approach, theinfluence of a wireless network QoS on the QoC ofa teleoperated robot. A wireless network based con-trol of a unicycle mobile robot in a distributed infras-tructure mode is described. The simulations are per-formed using Matlab/TrueTime toolbox. The problemfaced was a loss of communication when the mobilerobot moves out of range or there is an excessive net-work load that prohibits successful communication.The first strategy consists of adapting the networkQoS to the control requirements. An algorithm forreconfiguration by HHO is proposed as a solutionto maintain communication between control stationsand the mobile robot in multi stations scenarios. TheHHO is made with a criterion that takes into account

Algorithm 2: Decision algorithm.if NRF = poor and r ./ Si then

Switch controllerend if


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the QoC for the robot as well as the QoS of wirelessnetwork.The second strategy deals with the reconfiguration ofthe control, for the robot to be autonomous, when thecommunication link is out of order. Therefore, if therobot is out of range of all stations, the control modeis switched to embedded control, increasing the sam-pling period to reduce computations and the robot iscompletely autonomous. Integration of WLAN couldbe a choice for extended zone coverage for mobilerobots.In the future work, a quantification of QoS and QoCwill be dealt. A combination of infrastructure and ad-hoc architecture will also be investigated in order tomaintain sufficient QoC in multi robots perspective.

ACKNOWLEDGEMENTS

This work is partially supported by the Safe NeCSproject funded by the French Agence Nationale de laRecherche under grant ANR-05-SSIA-0015-03.

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