A Universal Control Architecture for Maritime Cranes and Ro botsUsing Genetic Algorithms as a Possible Mapping Approach

F. Sanfilippo, L. I. Hatledal, H. G. Schaathun, K. Y. Pettersen and H. Zhang

Abstract— This paper introduces a flexible and general con-trol system architecture that allows for modelling, simulationand control of different models of maritime cranes and,more generally, robotic arms by using the same universalinput device regardless of their differences in size, kinematicstructure, degrees of freedom, body morphology, constraintsand affordances. The manipulators that are to be controlledcanbe added to the system simply by defining the correspondingDenavit-Hartenberg table and their joint limits. The models canbe simulated in a 3D visualisation environment, which providesthe user with an intuitive visual feedback.

The presented architecture represents the base for theresearch of a flexible mapping procedure between a universalinput device and the manipulators to be controlled. As acase study, our first attempt of implementing such a mappingalgorithm is also presented. This method is bio-inspired andit is based on the use of Genetic Algorithms (GA). Using thisapproach, the system is able to automatically learn the inversekinematic properties of different models.

Related simulations were carried out to validate the efficiencyof proposed architecture and mapping method.

I. INTRODUCTION

In the maritime field, even though the operating environ-ment can be very challenging, it is still quite common touse relatively simple control interfaces to perform offshorecrane operations. In most cases, the operator has to handle anarray of levers, throttles or buttons to operate the crane jointby joint. Moreover, each input device can normally controlonly one specific crane model. When considering working ef-ficiency and safety, this kind of control is extremely difficultto manage and extensive experience with high control skilllevels is required of the operators. Therefore, low controlflexibility and non-standardisation are two crucial issuesofthe current maritime crane control architecture that need tobe overcome.

Robotic arms and cranes are very similar in the way theyoperate and in the way they are designed. Both have a num-ber of links serially attached to each other by means of joints

This work is supported by the Innovation Programme for MaritimeActivities and Offshore Operations (MAROFF) which is promoted by theResearch Council of Norway.

F. Sanfilippo, L. I. Hatledal and H. Zhang are with the Depart-ment of Maritime Technology and Operations (Avdeling for Maritimteknologi og Operasjoner), Aalesund University College (Høgskolen iAalesund), Postboks 1517, 6025 Aalesund, Norway,[fisa, laht,hozh]@hials.no.

H. G. Schaathun is with the Department of Engineering and NaturalSciences (Avdeling for ingeniør- og realfag), Aalesund University Col-lege (Høgskolen i Aalesund), Postboks 1517, 6025 Aalesund,Norway,hasc@hials.no.

K. Y. Pettersen is with Department of Engineering Cybernetics, Nor-wegian University of Science and Technology, 7491 Trondheim, Norway,kristin.y.pettersen@itk.ntnu.no.

that can be moved by some type of actuator. In both systems,the end-effector of the manipulator can be moved in spaceand be placed in any desired location within the system’sworkspace and can carry a certain amount of load. However,traditional cranes are usually relatively big, stiff and heavybecause they normally need to move heavy loads at lowspeeds, while industrial robots are ordinarily smaller, theyusually move small masses and operate at relatively highervelocities. This is the reason why cranes are commonlyactuated by hydraulic valves, while robotic arms are drivenby servo motors, pneumatic or servo-pneumatic actuators.Most importantly, the fundamental difference between thetwo kinds of systems is that cranes are usually controlledby a human operator, joint by joint, using simple joystickswhere each axis operates only one specific actuator, whilerobotic arms are commonly controlled by a central controllerthat controls and coordinates the actuators according to somespecific algorithm. In other words, the controller of a craneis usually a human while the controller of a robotic arm isnormally a computer program that is able to determine thejoint values that provide a desired position or velocity forthe end-effector.

Maritime cranes, compared with robotic arms, rely on amuch more complex model of the environment with whichthey interact. These kinds of cranes are in fact widely usedto handle and transfer objects from large container ships tosmaller lighters or to the quays of the harbours. Therefore,their control is always a challenging task, which involvesmany problems such as load sway, positioning accuracy,wave motion compensation and collision avoidance.

In this paper, a general architecture is presented that allowsfor modelling, simulation and control of different models ofmaritime cranes and, more generally, robotic arms by usingthe same universal input device. The main challenge of doingthis consists of finding a flexible way to map the fixed de-grees of freedom of the universal input device to the variabledegrees of freedom of the cranes or robots to be controlled.This process has to be realised regardless of their differencesin size, kinematic structure, body morphology, constraints,affordances and similar. The presented architecture allowsfor designing and testing different mapping procedures. Asa case study, our first attempt at implementing a mappingmethod is also presented. This method is based on the useof Genetic Algorithms (GA) [1] and using this approach, thesystem is able to automatically learn the inverse kinematic(IK) properties of different models.

Note that, since the main focus of this preliminary workis on building the control architecture, all problems related

to rope pendulations or wave impacts on the payload arenot considered in this paper but they can be included in themodel at a later stage.

The paper is organised as follows. In Section II, a reviewof the related research work is given. In Section III, we focuson the description of the system architecture and, as a casestudy, our first attempt of implementing a flexible mappingmethod is also presented. In Section IV, related simulationresults are shown. In Section V, conclusions and future worksare outlined.

II. RELATED RESEARCHWORK

In existing literature, not much work has been done toovercome the low control flexibility and non-standardisationproblems for the current maritime crane control architec-ture. Lebans et al. [2] proposed a tele-robotic controlledhandling system operated by an intuitive controller. In [3],Li and Wang presented a visual simulation system for ashipborne crane. The system can realise the visual simulationfor trajectory-planning, joint control and dynamic analysis.However, most of these previous studies only concern thecontrol of a specific crane/arm. Very little work has beendone regarding the possibility of controlling different armsby using the same input device.

In [4], our research group presented a modular prototypingsystem architecture that allows for modelling, simulationandcontrol of different robotic arms by using theBond GraphMethod. The main drawback of this approach is that thecomplexity of the system tends to rise when considering alarge number of degrees of freedom.

A common assumption in all these previous works isthat the IK model of the arm to be controlled is a prioriknowledge. Classically, this assumption enables researchersto either introduce analytical methods, which offer exactsolutions for simple kinematic chains, or propose solutionsbased on numerical methods. However, when consideringarms with redundant degrees of freedom, the inverse kine-matics can have multiple solutions, and therefore singularityproblems could arise. In addition, this method is not veryflexible, especially when planning to control different armsusing a universal input device because several IK modelsare needed: one for each arm or crane to be controlled. Analternative solution to the problem might consist of usingmethods that do not assume a priori knowledge for theIK model of the arm: a solution that derives its kinematicproperties from a machine learning procedure. In this way thesystem would be able to automatically learn the kinematicproperties of different arms and new models could also beeasily added.

During the last few years, there has been increasinginterest regarding research on learning algorithms and manyefforts have been made to understand how to apply thistechnology to various control problems. In particular, severalGA models have been developed by applying biologically-inspired control mechanisms to robot control tasks. Zhanget al. [5] proposed an approach for a robot inverse velocitysolution using GA. The authors used the principle of robot

motion propagation from link to link to find the robot’srecursive velocity formula, which then was used to determinethe fitness function. Tabandeh et al. [6] used a GA approachto solve for multiple solutions of inverse kinematics usingadaptive niching and clustering. The niching method wasused to modify the GA fitness value to encourage conver-gence around multiple solutions in the search space. Theauthors concluded that the proposed algorithm could begeneralised to solve the IK problem of a robot with unknownDOF and configuration, and that the method worked withgood precision and speed.

On the other hand, most of these intelligent systems areonly able to learn the control of a specific crane/arm. Atthe moment, it is not possible to use a common universalinput device to control various cranes/arms with differentkinematics. Moreover, most of these works require the sameDOF for both the input device and the model to be controlled.In other words, the controlling device has to have the samekinematic structure of the controlled model.

III. SYSTEM ARCHITECTURE AND CASE STUDY

A. Architecture

Since the main focus of this work is on building a universalcontrol architecture for different models of maritime cranesand, more generally, robotic arms, a generalised manipulatormodel is considered. The generalised model consists of akinematic chain that can be controlled by setting the positionor the velocity of the joints.

From a kinematic point of view, the end-effector of anoffshore crane usually consists of a wire which is used tolift and transfer objects, while robotic arms are commonlyequipped with more complex devices like grippers or tools.In a wider sense, in both cases, the end-effector can be seenas the part of the manipulator that interacts with the workenvironment and it may be modelled as part of the samekinematic chain. The proposed architecture, however, alsoallows the end-effector to be modelled as a distinct sub-chainthat can be controlled separately. So, in general, a mappingcontrol method may or may not consider the control of thewhole manipulator. Decoupling the model of the end-effectorfrom the model of the manipulator can, in some cases, greatlysimplify the mapping control algorithm since the complexityof the system generally increases more than linearly with thenumber of DOF.

With simplicity in mind, in this simplified model, thenature of the crane actuators - whether they are hydraulic,pneumatic, electric or mechanical - is not considered, but itmay be included in the model at a later stage.

The proposed control system architecture is shown inFig. 1. It is a client-server architecture with the input devicerunning as a client and communicating with a server wherethe logic of the control algorithm is implemented. The con-trolled arms are simulated in a 3D visualisation environment,which also acts as a client and provides the user with anintuitive visual feedback. With the information provided bythe visual feedback, the operator has a better sensing of the

Visualization environmentD-H table 1

Universal input device

Operator

Client

Mapping control

algorithm

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Manipulator 1

Manipulator 2

Manipulator 3

Manipulator n

444

D-H table 2

D-H table 3D-H table n

444

Workspace scaling Gripper/last-

sub-chain

control algorithm

Server

444

Fig. 1. The proposed control system architecture: a client-server architecture with the input device running as a client and communicating with a serverwhere control algorithm logic is implemented.

working area and can easily drive the end-effector of thecrane into the target position.

The control objective is that when the operator makesa movement such as lifting, handling, transportation orother manipulations by using the universal input device,the controlled robot should make an analogous motion. Theproposed architecture provides the possibility of controllingthe arms in position mode or velocity mode. The userexperience is substantially different in each case. When usingthe position control mode, the operator simply controls theposition of the tip of the crane with constant velocity; whenoperating in velocity control mode, the operator also setsthe velocity of the end-effector by using the universal inputdevice. In the first case, when the operator releases the inputdevice, the tip of the crane moves back to its starting point,while in the second scenario, the crane just stops moving butit keeps the last given position.

To realise these two possible operation modes, when theoperator manoeuvres the manipulator, a vector signal withno semantic,s, is sent from the universal input device to theserver. Here, according to the operation scenario, the vectorsignal is interpreted as the desired positionxd or the desiredvelocity vectorẋd.

Additionally, in order to adjust the size of the inputdevice’s workspace to the arm to be controlled, a scalingfactor is introduced to calculate the coordinate of the pointto be reached. In fact, the input device and the robotsto be controlled have generally very different sizes and,consequently, very different workspaces. The proposed ar-chitecture allows for expanding and shifting the small-scalephysical workspace of the input device to a virtual expandedworkspace allowing the robot arm for more accurate andprecise movements. In particular, referring to Fig. 2 anddenoting the reference frame of the input device’s physicalworkspace withOi , the reference frame of the input device’svirtual workspace withOv, and the reference frame of themanipulator workspace withOw, the desired scaled position,xds, is calculated as follows:

xds= kpxd + xw, (1)

Input device virtual workspace

Input devicephysical workspace

xi yi

zi

xv yv

zv

xw yw

zw

xds

kp xd

xw

Oi

Ov

Ow

Fig. 2. The proposed architecture allows for expanding and shifting thesmall-scale physical workspace of the input device to a virtual expandedworkspace, thus giving the robot arm the ability to make moreaccurate andprecise movements.

wherekp is the position scaling factor andxw is a shiftingvector that defines the position of the virtual reference framewith respect to the global reference frame. Similarly, thedesired velocity vector can also be scaled to allow theoperator to execute slower or faster movements accordingto the task to be accomplished. The desired scaled velocityvector, ẋds, can be obtained as follows:

ẋds= kvẋd, (2)

where,kv is the velocity scaling factor.Then, according to the desired mode of operation, the

mapping control algorithm parses those values to the desiredjoint anglesθd or desired joint velocitieṡθd of the manipu-lator, respectively. Essentially, for all the different models tobe controlled, the mapping methods have to implement theclassic inverse kinematic functions that can be generalisedas follows:

θd = f−1p (xds), (3)

concerning position control, and

θ̇d = f−1v (θa, ẋds), (4)

for velocity control, whereθa is the the actual joint anglesvector.

The calculated desired joint anglesθd or joint velocitiesθ̇d are then forwarded from the server to the visualisationenvironment in order to actuate the crane model. As feedbackfrom the visualisation environment, the actual joint angles θaand actual joint velocitieṡθa are sent back to the server andcan be used by the control mapping algorithm.

Note that the proposed architecture allow for implement-ing different mapping methods. Each mapping control algo-rithm has to realise the mapping between the fixed degreesof freedom of the universal input device and the variabledegrees of freedom of the manipulator to be controlled. It isimportant that each control algorithm be implemented as anindependent and interchangeable module and that it satisfiesthe interface specified by the system, (3) and (4), in order torespect the modularity of the proposed architecture.

Another relevant feature of the proposed architecture isthat the robot model can be separated from the controlalgorithm to be used. In particular, no matter which controlalgorithm is used, the manipulators to be controlled can beadded to the system simply by defining their correspondingstandard Denavit-Hartenberg (D-H) tables [7] and their jointlimits. Hence, by using the D-H method, the system is ableto analytically auto-generate the forward kinematic modelofthe arms to be controlled. However, even if this approachprovides a fundamental tool to compute the position ofthe end-effector from specified values for the joint values,nevertheless, a mapping procedure to determine the jointvalues that provide a desired position of the end-effectoraccording to the universal input has to be developed. Inparticular, as a case study, our first attempt of implementinga flexible mapping method is presented in the next section.

B. Case study: a mapping method based on GA

The proposed mapping method is based on the use of amachine learning algorithm. In particular, a continuous GAis employed to automatically learn the mapping functions,(3) and (4), for the manipulators to be controlled. Thisapproach only requires the forward kinematic model. Notethat the same set-up of the proposed algorithm is adoptedindependently of which manipulator is being controlled andwhether the selected control mode is position or velocity.Moreover, when controlling each specific manipulator andonce selecting the particular control mode, the same instanceof GA is continuously used; what differs are the semanticsand the size of inputs and outputs.

The flowchart of the proposed algorithm is shown inFig. 3. It is an iterative procedure and essentially, at eachit-eration, a population of candidate solutions or chromosomesis evolved toward better solutions according to a particularcost function. In the following, the key steps of the algorithmare described.

1) Define genetic representation and cost function:ini-tially, the genetic representation, the cost function and thetarget cost are defined. In particular, each chromosome en-codes its own properties or genes that consist of a set of joint

Define genetic representation and cost

function

Generate initial population

Find cost for each chromosome

Select mates

Crossover

Mutation

Convergence and time check

Acquire manipulator model, control mode and

target position

done

Present ouput

Fig. 3. Flow chart of the proposed mapping method.

angles,θg, constrained within their corresponding limits. Thelength of each chromosome is equal to the number of jointsto be controlled.

The fitness of every individual in the population is eval-uated by using a cost function that assesses the Euclideandistance between the target positionxt and the actual positionxa:

d(xt ,xa) = |xt − xa|, (5)

where, the actual position is calculated by using forwardkinematics, while the target position depends on the inputand it is given by:

xt = xds, (6)

if operating in position control mode, or by:

xt = xa+ ẋds∆t, (7)

if operating in velocity control mode, where∆t is the timeinterval between two successive iterations.

2) Acquire manipulator model, control mode and targetposition: the main iteration loop starts acquiring the propermanipulator model and the control mode according to the op-eration scenario. Moreover, the corresponding target positionis normalised according to the workspace of the manipulatorto be controlled. This will result in relating the cost functionto the corresponding workspace.

3) Generate initial population:subsequently, an initialpopulation of 125 individuals is randomly generated.

4) Find cost for each chromosome:the evolution process,which is a sub-loop of the main loop iteration, starts fromthe initial randomly-generated population and, at each gener-ation, the fitness of every chromosome is evaluated according

to the previously defined cost function (5). Additionally, anyindividual with genes that violate the corresponding jointlimits gets its cost increased by a considerable factor. Thenthe modification process of each individual’s genome startsin order to form a new generation.

5) Select mates:the selection of candidates that will beused as parents in the crossover process is obtained by usingthe stochastic universal sampling method[8], which is afitness proportionate selection method.

6) Crossover: the crossover function is defined as ahybrid function that stochastically switches - with a 50%crossover probability - between using a single-point and auniform crossover method, to create new offspring from theselected parent chromosomes.

7) Mutation: mutation may occur in a chromosome bystochastically adding a random value of±5% to the valueof its genes. In particular, there is a 0.5% mutation chancefor each gene. Additionally, a form of elitism is also used and10% of the fittest chromosomes survives unaltered betweengenerations. Note that, using a form of elitism with 10%of the fittest chromosomes surviving between successiveiterations and consecutive target positions makes sense be-cause, since the operator executes continuous movementswhile operating the manipulator, consecutive input vectors,stochastically, do not differ much in terms of magnitude anddirection.

8) Convergence and time check:this evolution processis repeated until a termination condition is reached. Inparticular, the GA population stops evolving and the fittestchromosome is returned when the cost drops below 0.01or when the overall time spent evolving the populationexceeds 100 ms. Note that, since the target position isnormalised according to the workspace of the manipulator tobe controlled and consequently the cost function is somehowrelated to the latter, there will be a correlation between thetarget cost and the considered model. In this way, a value of0.01 as cost target results in being weighed and proportionateto each specific workspace. A time limit of 100 ms allowsthe population to reach a good level of evolution in thefirst few steps of iterations without effecting the operatorexperience in terms of perception. Moreover, after the firstfew iterations, the time limit is stochastically seldom reachedfor target positions that are located inside the boundariesofthe workspace.

9) Present output:the genes of the fittest chromosome arethen presented as output. In particular, denoting these genesas θ f and according to the operation scenario, the output isobtained as:

θd = θ f , (8)

when operating in position control mode, or as:

θ̇d =θ f −θa

∆t, (9)

when operating in velocity control mode.Note that, in this specific case study, the control of the end-

effector’s orientation is not considered as part of the mapping

algorithm but it can easily be included at a later stage withoutaffecting the effectiveness of the presented architecture. Inparticular, three extra input signals are used to set theRoll-Pitch-Yawof the end-effector.

IV. SIMULATIONS AND EXPERIMENTAL RESULTS

In this preliminary study, a joystick fromLogitech, theExtreme 3d prowas used as a universal input device onthe client side. Each degree of freedom of the joystickcorresponds to a translational axis in the workspace of thecrane to be controlled. When operating in position controlmode, the joystick works as a position proportional replicawhose motion maps exactly to the motion of the craneend-effector with constant speed, while, when operating invelocity control mode, a movement of the joystick in aparticular direction will produce a translational motion inthe same direction at a velocity proportional to the joystickdisplacement. In both cases, when the operator’s hand isremoved from the joystick, the latter returns to automaticallyits starting point. Note that, thanks to the modularity of thearchitecture, any other joystick or input device can be usedwithout affecting the effectiveness of the system.

From an implementation point of view, the logic of thecontrol architecture lies on the server side, which is im-plemented by using theJava programming language. Eachmanipulator to be controlled is modelled as aJava classwhich embodies a D-H table, a set of joints, a workspace asattributes and aSolver as an abstract subclass. TheSolverabstract subclass has two methods -positionSolverandvelocitySolver- which have the prototypes that the mappingfunctions have - (3) and (4) respectively. The GA mappingmethod described in the previous section is a particularimplementation of thisSolver but new mapping methodscan easily be added by simply providing a correspondingimplementation of the same abstract subclass.

To speed up the developing process and to improve thereliability of the system, several libraries were used. Inparticular, theEfficient Java Matrix Library[9] was adoptedto add support for matrix manipulations, while the Ge-netic Algorithm was implemented by using theWatchmakerFramework for Evolutionary Computation[10]. Moreover,the manipulators to be controlled can easily be added to thesystem by simply defining their corresponding D-H tablesand their specific joint limits in aXML document.

Regarding the visualisation environment, in this prelim-inary work, the game engineUnity3D [11] was used tovisualise the different models. However, any other visu-alisation environment could be used without affecting theeffectiveness of the proposed architecture.

The system is based on a distributed structure and thecommunication between client, server and visualisation envi-ronment is realised by using theTCP/IPprotocol. This makesit also possible to remotely control the different manipulators.

Related simulations were carried out in order to test thearchitecture within the particular case study of the proposedmapping method. In detail, a knuckle boom crane, which is

(a)

X

Z

Y

__ target position

__ actual position

1.00 m

(b)

Fig. 4. The simulation of (a) the knuckle boom crane model and(b) the trajectory tracking of its Cartesian paths in X, Y andZ coordinates.

X

Z

Y

O 34.76%

# 10.55%

+ 54.69%

error < 0.01

error < 0.5

error > 0.5

1.10 m

(a)

X

Z

Y

O 37.49%

# 37.51%

+ 25.00%

error < 0.01

error < 0.5

error > 0.5

0.60 m

(b)

X

Z

Y

O 37.49%

# 37.51%

+ 25.00%

error < 0.01

error < 0.5

error > 0.5

0.80 m

(c)

Fig. 5. 3D Scatter plots showing error distribution for 512 equally-spaced target positions in the volume box that encloses the workspace of (a) theknuckle boom crane model, (b) theSCARArobot and (c) theKUKA youBotrobot.

shown in Fig. 4-a, aSCARArobot and aKUKA youBotrobotare modelled and simulated.

For each of these models, a trajectory tracking analysisof the Cartesian paths for X, Y and Z coordinates wasperformed and the results for the knuckle boom crane modelare shown in Fig. 4-b. Generally, the proposed system hasdemonstrated a quite fast reaction to the inputs. However,this kind of test is task-dependent.

In addition, for each of these models, an error distributionanalysis was performed considering a set of 512 equally-spaced target positions in the volume box that encloses theircorresponding workspace. For the three models considered,these error distributions are shown in Fig. 5 as 3D scatterplots. It is probable that the target positions at the boundariesof this imaginary box are less reachable by the manipulatorsdue to their joint constraints, which is why the correspondingerrors are stochastically greater. However, even for thesepoints, the GA is able to find the closest position match,thus avoiding potential singularity problems.

V. CONCLUSION AND FUTURE WORK

Regarding the presented case study, the adopted costfunction that is currently related to the workspace of thecontrolled model, could be also be related, as future work,to the particular manipulation task to be performed. Inparticular, the use of a task-oriented cost function couldconsiderably improve the performance of the system byallowing for heavier weighing of selected tasks that requirehigher accuracy than others.

In the future, new mapping methods which also takeheave compensation and swing related problems into accountcould also be implemented and integrated allowing for betterflexibility and reliability of the proposed framework.

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