Neural anti-collision system for Autonomous Surface Vehicle

Neural anti-collision system for Autonomous Surface Vehicle

Tomasz Praczyk

Polish Naval Academy,Institute of Naval Weapon,Gdynia,Poland

a r t i c l e i n f o

Article history:

Received20February2013

Received in revised form

4April2014

Accepted5August2014

Communicated by Bin He

Available online19August2014

Keywords:

Autonomous Surface Vehicle

Collision avoidance

Evolutionary neural networks

a b s t r a c t

Autonomous Surface Vehicles(ASV)are robots destined for the operation at water basins like lakes,

canals,harbors and even open sea.They are used for different purposes,e.g.for patrol tasks,as scouts,or

as a support for Navy ships.One of the main tasks of ASV is to move along a?xed path to a destination

point.To this end,an anti-collision system(ACS)has to be used with the ability to lead ASV along a path

and to simultaneously avoid all additional objects present at the basin,e.g.ships,sailing boats,?shing

cutters,icebergs.To perform this task,the ACS implemented as an evolutionary neural network can be

used.The paper describes architecture of the neural ACS,presents two neuro-evolutionary methods used

to build the system,and reports the whole process of constructing it.

&2014Elsevier B.V.All rights reserved.

1.Introduction

Robots are more and more often present in our life.They

successfully perform tasks which until recently have been exclu-

sively a domain of a human being.They are more and more

effectively replacing us in tasks in which they are better or

cheaper,wherever we cannot reach,or where our life can be in

danger.In the beginning,there were only robots which executed

exclusively activities strictly programmed by a designer.Such

robots are used so far,for example,to produce cars,they work in

invariable conditions and for well-de?ned tasks which do not

require intelligence.A next category of robots which also are

broadly exploited so far are robots supervised by a man-operator,

e.g.unmanned underwater vehicles,sapper robots.Because of

variable conditions in which they usually act and diversity of tasks

they are responsible for,their behavior cannot be preprogrammed.

They work as a continuation of a“human hand”and,often

perform tasks which are too danger for a human being.Autono-

mous robots which constitute a next class of robots are the most

technically advanced.Tasks which they execute frequently overlap

with tasks of remotely operated robots,however,they work

without supervision from outside,each action is initiated by a

robot itself.Often,robots are both remotely operated and auton-

omous.In a typical operational regime,they carry out commands

given by an operator,however,in some circumstances,e.g.when a

communication system between a robot and the operator is down,

they change a work mode into autonomous.

Nowadays,autonomous robots are exploited on shore,at sea,

under water,in the air,and even in the space.The robots destined

for the operation at water basins like lakes,canals,harbors,open

sea,are called Autonomous Surface Vehicles(ASV).They are used

for different purposes, e.g.for patrol tasks,as scouts,or as a

support for Navy ships.One of the tasks of an ASV,in the case of

patrol functions,is to visit a number of basin areas indicated by an

operator.An order of the monitored areas and an exact moving

trajectory for the vehicle may be?xed automatically or by the

operator as well.A similar task is also performed by a remotely

operated vehicle which has to autonomously come back to a base-

ship due to the breakdown of the communication system.This

time,the path to follow by ASV has to be determined by the

vehicle itself.It should keep the ASV away from all constant

elements of the basin,https://www.wendangku.net/doc/7a15492329.html,nd,buoys,shallows,wrecks jutted

out of the water,etc.However,to effectively perform the task

above,it is insuf?cient to follow only the path leading to a

destination,it is also necessary to avoid collisions with objects

which may appear along the way,e.g.ships,sailing boats,?shing

cutters,icebergs.For that purpose,the anti-collision system(ACS)

which is the main subject of this paper can be applied.

It is assumed that the task of the ACS is to make high-level

decisions concerning direction and velocity of move,decisions

taken by the ACS are converted into control signals for the ASV

engine and rudder by means of low-level controllers(e.g.PID or

fuzzy controllers)adjusted to the speci?c type of the ASV.To work,

the system requires external sources of information supplying it

with the information about the current position of the vehicle,

constant elements of a basin surrounding the vehicle(land,buoys,

etc.),and temporary objects visible on the basin(ships,icebergs,

etc.).Such information can be provided by devices such as GPS

(Global Positioning System),electronic navigational chart(or its

simpli?ed black-and-white variant indicating areas accessible and

inaccessible for the vehicle),AIS(Automatic Identi?cation System

–it provides a detailed information,e.g.course,velocity,exact

position,about all objects equipped with this system),and radar.

Moreover,in order for the vehicle to not only avoid collisions but

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Neurocomputing

https://www.wendangku.net/doc/7a15492329.html,/10.1016/j.neucom.2014.08.018

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Neurocomputing149(2015)559–572

also to move along a?xed path,the ACS has to know coordinates of the closest turning point which has to be visited(it is assumed that the ASV path is a sequence of turning points whereas a trip along the path is moving from point to point–Line-Of-Sight–LOS guidance law).

Much work has been done on the collision avoidance problem to date,and a lot of different solutions have been proposed[1].Many of them are planning solutions in the sense that when a collision situation is detected they try to determine a safe path for a controlled vehicle,the path which avoids all dangerous objects. The last point of the path is a point in which there is no danger of a collision.To?x a path,different techniques are used,e.g.heuristic search methods[9,23,22](A*algorithm,its modi?cations,and others),evolutionary[4,10,17,18,21],and ant colony algorithms[24].

The main problem with applying these methods to the collision avoidance is their computational complexity which results from their inherent search or population nature.They generally cannot give a guaranty of?nding a collision-free solution in an assumed short period,how long they will work mostly depends on condi-tions they deal with.In most situations at sea they will work properly and without delay,however,there can be situations,e.g. heavy traf?c in restricted areas,navigation in the presence of many very fast objects,when they may fail by providing a collision-free plan too late.All this makes the above methods suited rather for either off-line path planning or planning in conditions when there is no need to rapidly make decisions.

Other methods use neural networks or/and fuzzy logic to determine the collision risk and to select an object to avoid.Then, they identify a collision situation(out of a number of well-de?ned situations)and use COLREGs1to maneuver the vehicle[3].

As before,these methods cannot be applied in highly complex and highly dynamic environments.2First of all,they assume that each collision situation,even the one with many colliding objects, can be considered as a combination of simple one-object avoid-ance collision problems.Moreover,to solve different collision problems,they use a number of standard maneuvers,each of which is adjusted to a pattern situation.The problem is,however, when a collision situation is far from each pattern and it cannot be reduced to a combination of standard maneuvers.

A next approach to collision avoidance is to copy maneuvers which appeared to be effective in a previous collision situation,or in other words,to use the so-called case base reasoning(CBR) technique[8].The main problem,in this instance,is time con-suming analysis of sample collision cases included in the database. This feature of CBR approach makes it inappropriate for highly dynamical environments when situation at sea may change rapidly and lightning decisions are sometimes necessary.

Rule-based expert systems[6,7]and fuzzy expert systems [5,11]can be considered as a next class of collision avoidance techniques.In this case,we deal with decision rules,each of which corresponds to some collision situation and proposes a solution to this situation.If more than one rule matches an input collision case,a maneuver is proposed which is usually a compromise between decisions of all matching rules.

To create the systems,navigators'experience,traf?c regula-tions,encountered collision scenarios or navigation theory are used.The way of preparing the systems seems to be main reason for their restricted applicability only to cases which already took place or can take place in the opinion of navigators.Since they are built by a human being and based on learning data which are result of human being experience,collision avoidance expert systems may have problems with generalization in unusual colli-sion situations which are not taken into account by designers while preparing the systems.

V-obstacle[16]is an algorithmic collision avoidance technique. It searches for anti-collision maneuvers in the velocity domain taking into consideration the behavior of all colliding objects. Knowing motion parameters of the objects the v-obstacle deter-mines for each of them a cone-shaped area(linear variant of v-collision)which represents collision risky velocities of the own vehicle.To avoid collision,it is necessary to select maneuvers which do not correspond to any of such areas.

The drawback of this method is that it looks only one step forward,that is,when calculating a next move it takes into account only a current situation,which means that it does not propose a solution to a whole collision problem but it only indicates maneuvers which in a given point in time are safe.For more complex collision problems,such an approach may produce trajectories far from optimality,in some circumstances,a sequence of v-obstacle maneuvers may even lead to a dead-end situation in which there will not be any collision-free maneuver.

In the paper,a new collision avoidance solution is proposed. Since its main application is to avoid collisions in very complex, multi-object,rapidly changing environments,e.g.during military operations in the presence of many fast alien objects,it differs from all the solutions presented above.First,due to speed requirements, it does not plan a motion trajectory,as the planning path solutions, but it indicates a single maneuver which should be performed to avoid collision in a given situation.Second,because of multi-object environment in which we cannot expect the other objects to obey COLREGs,the solution proposed in the paper is not restricted by any kind of regulations and it can produce any collision-free maneuver. Third,to prepare the system to work in different conditions, sometimes even unusual conditions,conditions which are assumed to be dif?cult to predict,it was trained based on diverse collision scenarios designed by a navigator,navigational dilettante,and even a genetic algorithm.Fourth,even though the system does not look forward and makes decisions based on the information from a single point in time,it is sensitive to not perform maneuvers which in the future may lead to dead-end situations.This property of the system is achieved thanks to training process during which the system learns which collision-free maneuvers can be in a given situation inappropriate and should be avoided.

The ACS described above is implemented as an evolutionary neural network.The network calculates the course and the speed for the ASV based on the appropriately processed information from different ASV devices.To build the network,two neuro-evolutionary techniques are used:Assembler Encoding with Evol-vable Operations(AEEO)and Cooperative Co-Evolutionary Neural Networks(CCENN).They were selected out of many other meth-ods because of their effectiveness con?rmed in experiments with underwater vehicles.3They appeared to be more ef?cient in evolving neuro-controllers for a team of the vehicles than Neuro-Evolution of Augmenting Topologies[19,20],i.e.one of the most successful state-of-the-art neuro-evolutionary methods of recent years.

To produce a reliable anti-collision network,each neuro-evolu-tionary method was run many times.In each run,a very intensive “training”process took place during which the networks were tested on many different training https://www.wendangku.net/doc/7a15492329.html,works which brought the ASV to a destination point without any collision and wander-ing in all the tasks were then put to a generalization tests.Their

1International Regulations for Avoiding Collisions at Sea–The COLREGs describe potential collision scenarios such as crossing,head-on,and overtaking and suggests possible maneuvers to avoid a collision.

2A lot of fast objects with unpredictable behavior,e.g.objects which want to intentionally lead to a collision.

3Description of both methods is given in the papers which are currently under review.

T.Praczyk/Neurocomputing149(2015)559–572 560

goal was to ultimately con?rm anti-collision capabilities of the networks and to select the most effective ones.An ultimate evaluation was,however,carried out by a human being who chose a few networks which according to his subjective opinion oper-ated in a way the most appropriate for the anti-collision system.

The main goal of the paper is to present the entire neural ACS and to describe all the efforts which eventually resulted in building it.It is organized as follows:Section2describes the framework of the system,Section3outlines neuro-evolutionary methods used to construct the system,Section4describes how the system was built,and Section5summarizes the paper.

2.Framework of the system

This section consists of two subsections:the?rst describes the input layer of the ACS,that is,it speci?es which information is necessary for the system to be able to effectively perform the task, and the second is devoted to the output layer,that is,it shows how the system controls the vehicle.The inner architecture of the system,i.e.the number of hidden neurons,types of neurons,and inner connectivity,is not described in this section because it completely depends on a neuro-evolutionary method used to build the system.As mentioned in the Introduction,two different methods were used for that purpose,both are detailed in the following section.

2.1.Input layer

In order to safely control the vehicle along a?xed path,the ACS has to be supplied with two types of information,i.e.?rst,the information about the direction in which the vehicle should move to reach a next turning point,and second,the information about all dangers surrounding the vehicle,https://www.wendangku.net/doc/7a15492329.html,nd,ships.Whereas the ?rst information can be simply calculated based on coordinates of a current vehicle position(e.g.from GPS)and the position of a next turning point,the second information,because of different sources of this information,different volumes and different signi?cances for the vehicle,before passing it on to the system,?rst has to be ?ltered and reduced to a compact form.

The range of vision of all the devices and systems whereby the vehicle can acquire the information about external world and thereby avoiding collisions is assumed to be very large compared to its size and maneuverability.For example,radar can inform about all objects at the distance of24Nm(Nautical miles)from the vehicle or even further(it depends on radar settings).The same applies to AIS and the navigational https://www.wendangku.net/doc/7a15492329.html,ing all the accessible information about objects visible on a basin when maneuvering the vehicle would lead to the situation in which the ACS would have to learn to ignore a part of this information due to its insigni?cance for safety of the vehicle.In order to make the task of the system easier,it is necessary to inform it exclusively about objects which are in the vicinity of the vehicle or,in other words,it is necessary to reduce the range of vision of the whole system.The problem is,however,how to properly determine this range.Too large range is obviously associated with information overload,and as mentioned above,the necessity of learning which objects pose a threat for the vehicle and which do not.In this case,the system would have to simultaneously perform two different tasks,i.e.on one hand,it would have to?lter the incoming information,and on the other hand,it would also have to control the vehicle.Building such a system would be a very serious challenge.The opposite situation,that is,too short range of vision could,in turn,lead to a frequent surprising the system by suddenly emerging dangerous objects which to be not taken into account when making previous decisions.The consequence of such a state of affairs could be an ineffective anti-collision strategy consisting in avoiding exclusively the closest object and neglecting the remaining ones.Such a strategy would lead to often changes in motion parameters of the vehicle,and in consequence,the vehicle moving trajectory far from optimality.To?x the vehicle range of vision,experiments were carried out whose results are presented in a further part of the paper.

The other problem connected with the input layer of the anti-collision network is the form of the input information.This information can be provided by different sources,it can be formatted in a different way,it can relate to different object parameters,and what is more,it can also involve a different number of objects.For example,the chart can give the information about the distance to the nearest land visible at a given bearing(in principle,to be able to give such information,an additional function has to be implemented in the chart),whereas radar and AIS,in addition to object position(radar–relative position: bearing and distance,AIS–absolute position:longitude and latitude),also provide the information about motion parameters of objects(speed and course).Moreover,radar can also calculate collision parameters such as the minimum distance to an object (DCPA–Distance at the Closest Point of Approach)and the time to the minimum distance(TCPA–Time at the Closest Point of Approach),4whereas AIS can also give the information,for example,about the size of the object.As a whole it is a huge mass of diverse information whose exploitation in full measure to control the vehicle is a very hard task.To make the task of the ACS easier,and generally viable,it was necessary to reduce all the accessible information about each single object to only one scalar which was called threat.5In this approach,land is considered to be a sequence of threats visible at a number of subsequent bearings. To represent different objects as threats,different concepts were considered(e.g.a separate neural network responsible for calcu-lating the threat based on different parameters of an object), however,ultimately,the simplest of them were used.According to it the threat T of an object o is de?ned as follows:

TeoT?

RàD

R

object o is colliding

àRàD

R

otherwise

8

>><

>>:e1T

where R is the vehicle range of vision,D is the distance from vehicle to object,D r R.

Both parameters necessary to?x the threat of an object,i.e.D and if it is colliding or not,can be acquired directly from the vehicle devices or have to be calculated based on other para-meters.As mentioned above,radar provides the information about the current and minimum distance(DCPA)to an object and time to the minimum distance(TCPA).The last two parameters can serve to determine whether an object is colliding or not.DCPA o threshold and TCPA40mean a collision situation(threshold determines a collision distance,TCPA o0means that a collision situation would take place if both objects moved in an opposite direction)whereas each other combination of both parameters implies an object does not pose a threat for the vehicle(assuming the same motion parameters of both the object and the vehicle).

4In fact,this function is performed by the ARPA system(Automatic Radar Plotting Aid)which supports radar.

5In the literature devoted to the collision avoidance the term collision risk is very often used,the difference between threat and collision risk is that collision risk indicates what is the degree of collision danger for current motion parameters of our vehicle and an analyzed object whereas threat gives more complete informa-tion,that is,on one hand,as collision risk,it points out colliding objects and categorizes them but on the other hand it also shows objects which may be danger for other than current motion parameters.

T.Praczyk/Neurocomputing149(2015)559–572561

In the case of AIS,parameters necessary to ?x the threat have to be calculated.Distance D can be worked out based on the geographical coordinates of an object (from AIS)and the vehicle (from GPS),whereas to determine DCPA and TCPA,their motion parameters (speed and course)can be used.

The chart similarly to radar can directly provide the informa-tion about the distance to land.In this case,a collision takes place when land lies exactly at the course of the vehicle.However,in addition to the information about colliding land for the current ASV course,the chart is a source of other useful information.Knowing GPS position of ASV the chart component of ACS can ?x distances from this position (generally,from any position)to the nearest land for different bearings,for example,every 11.This way,ACS has the information about surrounding land (generally,about unsafe areas for ASV)and is able to effectively plan a maneuver avoiding both moving and static objects.

A separate problem for the ACS is a variable amount of information passed on to the system.Once,it may work sur-rounded by many different objects,whereas some other time it may navigate at open sea without any potential threats.Since,however,the ACS is implemented as a neural network,a situation at a basin characterized by a variable threat has to be shown to the system in the form of a vector of a constant length.To this end,the solution has been applied in which the whole area of a basin visible for the system is divided into observation sectors,each of which corresponds to other angle range of observation and other input of the system.What is more,each sector is associated with one value of the threats which represents all objects situated within the sector.To represent the sector threat,three solutions were tested,i.e.a total,average,and maximum threat.Results of the tests are presented in a further part of the paper.

Since in the ?nal variant of the ACS the number of observation sectors has been set seven,the input layer of the entire system can be ultimately de ?ned as follows:1st input –an angle at which the course of the vehicle has to be changed left (negative value)or right (positive value)for the vehicle to directly move toward a next turning point (the value scaled to the range ?à1;1?),inputs 2–8–threats for sectors (1801,2471?,(2471,2921?,(2921,3371?,(3371,3591?and ?01,221?,(221,671?,(671,1121?,(1121,1801?.For example,the situation presented in Fig.1could correspond to the following input signal:1st input ?20/180(course to a next turning point is equal to the current course t201–because the value is positive,ASV should turn right),2nd input ?0.25(because the value is positive,object from sector (1801,2471?is colliding),3rd input ?0(there is no threat in sector (2471,2921?),4th input ?à0.3(because the value is negative,object from sector (2921,3371?is not colliding),5th input ?0(there is no threat in sector (3371,3591?and ?01,221?),6th input ?0(no threat in the sector),

7th input ?à0.6(there is a not colliding object in sector),8th input ?0(no threat in the sector).2.2.Output layer

One of the key issues when designing the anti-collision net-work is to determine its scope of the responsibility,i.e.when it should control the vehicle and when it should not.The simplest solution to this problem is to make behavior of the vehicle completely dependent on network decisions.In this case,the network,regardless of the situation at sea,determines course and speed for the vehicle (how to determine motion parameters for the vehicle is described further).In other solution,the network decides whether the vehicle should behave according to its indications or it should move exactly towards a next turning point.To this end,a separate output of the network can be used,a signal less than 0.5at this output may mean –go directly to a turning point,whereas opposite situation may mean –operate according to network commands.This solution divides the whole input space into two disjoint sets and makes the network responsible for controlling the vehicle only in one of them.Of course,an additional task of the network is to differentiate states when the vehicle is safe and can proceed toward a turning point and states when the intervention of the network is necessary in order to avoid navigational dangers.The third solution makes the task of the network even more easier.In this case,the network is activated exclusively in the situation when an object is visible near the vehicle,otherwise the vehicle moves directly towards a turn-ing point.As in the previous case,the input space is divided into two sets,one set corresponds to safe states whereas the other one to states requiring network activation.This time,however,the division is made authoritatively and it does not depend on the network at all.All the three solutions described above were tested experimentally and compared in a further part of the paper.

To determine course and speed for the vehicle two solutions were considered.The ?rst is to assign a separate output of the network to each motion parameter.In this case,output signals determine directly a next move of the vehicle which ranges ?à1801,1801?for course,where a negative value means a turn left whereas a positive one a turn right,and ?5,20?knots for speed.Moreover,to protect the vehicle from slight ?uctuations of output signals which could result in often ineffective small changes in motion parameters,the update on course and speed is made only when the difference between consecutive indications of the net-work is larger than a threshold.

The second solution reduces the set of possible vehicle beha-viors to only seven maneuvers:turn left at 1351,turn left at 901,turn left at 451,go straight,turn right at 451,turn right at 901,and turn right at 1351.Each maneuver corresponds to other output and to select a next move for the vehicle the outputs are compared.An output with the highest signal indicates a maneuver which is performed in the following decision step.Only seven maneuvers on one hand means the ability to control the vehicle and on the other hand means also not too complex architecture of the network which may be tuned to the task –more maneuvers would mean more outputs.

Both solutions described above were tested within the frame-work of tunning experiments whose results are presented further.

3.Neuro-evolutionary methods used to build the system 3.1.Assembler encoding with evolvable operations

AEEO originates from a generative neuro-evolutionary method called Assembler Encoding (AE)[14,15].In AE,an arti ?cial neural

Anti-collision

network

Course Speed

Course To Goal

in2

in3

in4

in5

in6

in7

in8Fig.1.Input of anti-collision network (lines with arrows –network inputs,dotted lines –inputs with signal ?0,solid lines –inputs with signal a 0,ships –objects located in different observation sectors).

T.Praczyk /Neurocomputing 149(2015)559–572

562

network(ANN)is represented in the form of a linear program consisting of operations with prede?ned implementations and data.The task of the program is to create a Network De?nition Matrix(NDM)de?ning a single ANN.To form the program,and in consequence an ANN,Cooperative Co-Evolutionary Algorithm (CCEGA)[12,13]is used.The genetic algorithm generates opera-tions(parameters of operations;as mentioned above,implemen-tations are de?ned beforehand)and data which combined together form a program.Each program builds NDM which is then transformed into an ANN.To this end,the matrix has to store all the information necessary to construct a network.This infor-mation is included both in the size and individual items of the matrix,scaled always to the range?à1;1?.The size of NDM determines a maximum number of neurons in an ANN whereas individual items of the matrix de?ne weights of interneuron connections,i.e.an item NDM?i;j determines a link from neuron i to neuron j.Apart from the basic part,NDM also contains additional columns that describe parameters of neurons,e.g.type of neuron(e.g.sigmoid,radial,linear),and bias(see Fig.2)[14].

In AEEO,the operations with prede?ned implementations are replaced with ANN-operations with the same task as in the classic variant of the method.Moreover,the data used previously by the operations are completely removed from the programs which in AEEO include exclusively the ANN-operations.The ANN-operations operate together on one NDM which encodes one?nal ANN,once the matrix is completely formed it is then transformed into the network.To perform the task,ANN-operations have two inputs,a number of hidden neurons,and three outputs.The inputs indicate an item in NDM updated by the operation(a number of rows and columns)whereas the outputs are used for three different purposes,i.e.to determine a negotiation strength of each ANN-operation,to determine whether the item should by mod-i?ed or should not,and to determine a new value for the item.To indicate which ANN-operation should determine the value of a given item,negotiation outputs of all ANN-operations are com-pared.An ANN-operation with the greatest output value is entitled to modify the item.In order for the item to be updated,the second output of the selected ANN-operation is tested.If the output value is greater than an assumed threshold the item gets a value from the third output of the ANN-operation(see Fig.3).Otherwise,the item remains intact and the corresponding connection between neurons in the?nal ANN is not established.

ANN-operations as operations and data in AE evolve according to CCEGA.In CCEGA,each part of a solution evolves in a separate population.To form a complete solution,selected representatives (usually,the best ones)of each population are combined together. Application of this evolutionary scheme in AEEO consists in evolution of individual ANN-operations in separate populations. The number of ANN-operations in a complete program corre-sponds to the number of populations(see Fig.4).Each population delegates exactly one representative ANN-operation to each pro-gram.During the evolution,programs expand gradually.In the beginning,they contain only one ANN-operation from one existing population.When the evolution stagnates,https://www.wendangku.net/doc/7a15492329.html,ck of progress in ?tness of generated solutions is observed over some period,a set of populations containing ANN-operations is enlarged by one population.This procedure extends all programs by one ANN-operation.Each population can also be replaced with a newly created population.Such situation takes place when the in?uence of all ANN-operations from a given population on?tness of generated solutions is de?nitely lower than the in?uence of ANN-operations from the remaining populations(a population

can be replaced when,for example,?tness of a population, measured as the average?tness of all ANN-operations from the population,is de?nitely lower than the?tness of the remaining populations).

In individual populations,the evolution proceeds according to Canonical GA[2].At the genotypic level,each ANN-operation is represented in the form of a variable length chromosome consist-ing of two parts.The?rst short part de?nes topology of an ANN-

in

out

Fig.2.The way of encoding ANN in the form of Network De?nition Matrix(NDM) [14]

.

AEP

i-th row

j-th column

z1>z2>z3, y1>threshold

Fig.3.Example operation of programs in AEEO(ANN-oper1modi?es[i,j]th item in NDM,value x1is inserted into the matrix because y14threshold and the negotia-tion strength of ANN-oper1–z

1is greater than strengths of other ANN–operations, i;j–inputs,x1;y1;z1–outputs of ANN-oper1,all items in NDM are modi?ed one after another).

Fig.4.Evolution of programs in AEEO.

T.Praczyk/Neurocomputing149(2015)559–572563

operation whereas the second part includes parameters for the network (see Fig.5).Construction of an ANN-operation proceeds in three phases.First,topology of an ANN-operation is determined based on the information contained in the ?rst part of the chromosome.To this end,binary values from this part are directly copied into NDM,a single bit corresponds to a single item in the matrix.When the number of bits is insuf ?cient to completely ?ll in the matrix,the whole sequence of bits is used again.In the following phase,the parameters from the second part of the chromosome are successively introduced into NDM.In this case,only items equal to one are modi ?ed.The remaining items,i.e.items equal to zero,remain intact.As before,transfer of the parameters is performed in a loop until all the elements in NDM have a value assigned.In the last phase,NDM is transformed into an ANN-operation.

3.2.Cooperative co-evolutionary neural networks

In CCENN,an ANN is composed of a set of unconnected sub-ANNs and a vector of numbered output registers.The task of sub-ANNs is to collectively produce an output of the ANN and to enter it into the registers.Each register corresponds to a different output and is updated independently of other registers.A single run of the set of sub-ANNs generates a value for one register.In consequence,to obtain a complete output of the entire ANN,sub-ANNs have to be run a number of times.6In each run,only one sub-ANN is entitled to modify a register.In order to select an active sub-ANN,a negotiation process between all the sub-ANNs is carried out.A sub-ANN with the greatest negotiation strength is allowed to set a value of a selected register (see Fig.6).

The maximum number of inputs in each sub-ANN is the same and corresponds to the size of input space of a problem to be solved.The exact number of inputs depends on decisions made during the evolutionary process and,in consequence,it can be different for each sub-ANN.In addition to inputs corresponding to a problem,each sub-ANN also includes an extra input which indicates the number of a register to be currently modi ?ed.As mentioned above,the complete output of the entire ANN is produced in a number of iterations.The number of iterations which corresponds to the number of a register to be modi ?ed is every single time fed into the extra input of each sub-ANN.The

remaining inputs,in all the iterations,are supplied with the same input signals.

Like the number of inputs also the number of hidden neurons has its maximum value,the same for all sub-ANNs.As before,the exact number of hidden neurons is determined in the evolutionary way and it can be different for each sub-ANN.

Two outputs of each sub-ANN are used for two different purposes.The ?rst one informs about the negotiation strength of a sub-ANN whereas the second one determines a value which is inserted to a register once the sub-ANN wins a competition with other sub-ANNs.

In CCENN,evolution of sub-ANNs proceeds exactly in the same way as evolution of ANN-operations in AEEO.The same is the evolutionary scheme,that is,CCEGA (see Fig.7),evolutionary algorithm used in each single population,that is Canonical GA,and the same is also the way of encoding individual sub-ANNs into chromosomes (see Fig.5).

4.Building the system

Building the system took place in simulation conditions and in three separate phases.In the ?rst phase,the ?nal con ?guration of the system was determined,that is,the range of vision,the way of representing the threat for each observation sector (maximum,average,total threat),scope of the anti-collision network respon-sibility (when the n etwork controls the vehicle),and the way of indicating course and speed for the vehicle (one output per motion parameter –two outputs,one output per one course plus separate output for speed –eight outputs).In addition,three different methods for calculating ?tness were also tested in this phase.In the second phase,attempts were made to build anti-collision networks effective in diverse collision situations.To this end,both neuro-evolutionary methods were run many times,in each run networks had to control vehicles in many various training https://www.wendangku.net/doc/7a15492329.html,works which successfully performed all the training tasks were in the third phase tested in terms of general-ization abilities.The ones which passed all the generalization exams were subject to next testes during which they were evaluated by a navigator.4.1.Testing scenarios

In order to evolve anti-collision networks for the ASV,a total of 170testing scenarios (or tasks)were designed (see Fig.8).In all the scenarios,the task of the ASV was to cover a distance of about

Topology Parameters

Fig.5.The way of building NDM representation of ANN-operation.

6

To speed up calculations,each register can be associated with its own copy of the set of sub-ANNs.This way,the calculations necessary to generate the output do not have to be performed in many sequential steps.

T.Praczyk /Neurocomputing 149(2015)559–572

564

2Nm to a destination point,without a collision (approaching any object at the distance less than 100m was considered to be a collision,each collision meant interruption of a scenario),in the presence of 10moving objects,for different relative locations of starting and destination points.All other objects moved along a straight line and with a constant speed.They usually moved in such a way that ASV could not avoid them one after another.An additional requirement in each scenario was to reach a destina-tion point in an assumed time: 1.5T 10,where T 10is a time necessary to cover 2Nm along straight line,i.e.without any

Fig.7.Evolution of a modular ANN consisting of three sub-ANNs:each population delegates a single chromosome,each of which is ?rst converted into a corresponding Network De ?nition Matrix (NDM is a matrix including all the information necessary to build a neural network –see Fig.2)and then into a sub-ANN.

ASV Destination ASV

Destination

Fig.8.Example scenarios used to train anti-collision networks (starting positions of all the objects and their courses and speeds).(a)Example dilettante's scenario and (b)example GA scenario.

Fig.6.Modus operandi of two-output modular ANN consisting of three sub-ANNs.First,output register R1is set by sub-ANN1because negotiation strength of this network y11(negotiation strength of network no.1for register no.1)is the greatest for this register –R1?x11(x11–output of module no.1for register no.1).Then,output register R2is modi ?ed,this time,however,sub-ANN3?xes output value –R2?x32(output of module no.3for register no.2).When calculating R1extra inputs of all modules are equal to 1,whereas when R2is modi ?ed all extra inputs are set to 2.(a)1st iteration and (b)2nd iteration.

T.Praczyk /Neurocomputing 149(2015)559–572565

collision avoidance maneuvers,with the velocity equal to 10knots.

To ensure diversity of the scenarios,they were constructed in a different manner.Some of them(40scenarios)were built by a navigator,who introduced into the scenarios the experience concerning typical situations at sea.Next scenarios(70scenarios) were proposed by a navigational dilettante,these scenarios were assumed to represent unusual situations.Since when building the scenarios the dilettante was not driven by any maritime regula-tions and navigational experience his/her scenarios often corre-sponded to situations which should not take place at sea in normal conditions.The last scenarios(60scenarios)were built in the evolutionary way.Some navigator's and dilettante's scenarios were?rst used to evolve a simple anti-collision network.To this end,AEEO described above was applied.The network then was exploited during evolution of next scenarios to indicate the GA in which scenarios are dif?cult and which are not.This knowledge helped the GA to evolve tasks which the simple anti-collision network was unable to perform.

All the scenarios were used for three different purposes.First, they were applied to determine a skeleton of the whole ACS,i.e.its input and output layers,the range of vision,the form of sector threat,etc.(see next section)–30scenarios,then,they were used to evolve a number of neural ACSs(see Section4.4)–110scenarios, and?nally,they also made it possible to measure generalization abilities of the systems and to select the best ones(see Section4.5)–60scenarios which were not used in earlier phases.

4.2.Evaluation of anti-collision networks

Fitness of each network was calculated as a sum of?tnesses from all scenarios in which a given network https://www.wendangku.net/doc/7a15492329.html,work evaluation was interrupted after the?rst case when the vehicle collided with other object or it did not reach a destination within an assumed time.To evaluate networks in each single scenario, three?tness functions were tried:

F1enetworkT?

0conditioneaT

0:5

1

d dest

à0:25k

I max

conditionebT0:5t0:5

1

1td dest

à0:25k

I max

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R destt

I maxàI

I max

à50k

I max

conditionedT

8

>>>

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>>>

>:

e2T

F2enetworkT?

F1enetworkTconditionsea;b;cT

R destà

k1

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F3enetworkT?

F1enetworkTconditionsea;b;cT

R destà50

k

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àk2

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where

d dest is th

e distance to a destination point at the end o

f a

scenario.

k is the unit penalty for changing course by more than 901in situation when the closest object is farther than

0.15Nm.

I max is a maximum number of steps(decisions of anti-collision

network)which the vehicle can make when moving towards a destination(this number was dependent on the distance between starting and destination points).

I is an actual number of steps the vehicle made to reach a

destination.

R dest?100is a reward for reaching a destination(destination is reached when distance to it is less than0.1Nm).

k1?∑I i?1ΔCRS i is a penalty for each change of course CRS, greater changes?greater penalty

k2?∑

I

i?1

1ifΔCRS i a0

0otherwise

(

is a penalty for each change of course CRS

more changes?greater penalty,condition(a)–network led to a collision.

condition(b)–network did not lead to a collision,the ASV did not reach a destination and is more than1Nm away from it.

condition(c)–network did not lead to a collision,the ASV did not reach a destination and is closer than1Nm to it.

condition(d)–the ASV reached the destination.

In all the cases,an evaluated network obtains?tness zero if it leads to a collision(condition(a)).As mentioned above,each collision automatically interrupts the whole evaluation process.If the ASV effectively avoids collisions,however,it also cannot reach a destination point,evaluation process is also interrupted whereas the network obtains reward inversely proportional to the distance between the ASV and the destination and simultaneously a penalty dependent on the number of forbidden maneuvers(see value k).When the ASV is more than1Nm away from the destination(condition(b)),the network reward ranges o0;0:5), otherwise(condition(c)),the reward is greater than0.5and close to1.The evaluation method described above,common for all the three?tness functions,promotes networks which can lead the ASV as close the destination as possible,and simultaneously do not perform sweeping maneuvers when there are no dangerous objects in a close proximity of the ASV.

When the ASV reaches the destination,evaluation of the anti-collision network depends on the?tness function applied.Func-tion(2)promotes networks which lead the ASV to a destination point as fast as possible.It also forces the networks to minimize the number of forbidden maneuvers.This function yields the evolution a great freedom of action and is focused on neural solutions which are able to quickly lead the ASV to the destination. How the destination is reached is insigni?cant,voyage duration is important.The only requirement for the evolution is to avoid forbidden maneuvers which when happen are drastically pena-lized.Generally,function(2)concentrates rather on rewarding and encouraging to searching for de?nite neural solutions,penalties are very rare in this case.

Unlike(2),function(3)is oriented rather towards punishments than towards rewards.In this instance,solutions are promoted which lead the ASV along a gentle trajectory.In(3),there is no element which promotes quick moves towards the destination because it is assumed that the vehicle which avoids sweeping maneuvers goes more or less directly and,in consequence,quickly towards a goal point.The characteristic of(3)is that all maneuvers are penalized.The magnitude of penalty depends on the magni-tude of course change.Such solution forces the evolution to search for networks which are able to predict a collision situation and to perform a gentle maneuver in advance.

Function(4),like(3),is also oriented rather towards punish-ments than towards rewards.In this case,all maneuvers are also penalized,however,each of them is penalized in the same way.Such solution promotes networks which push the ASVs towards a goal using a minimal number of maneuvers for this purpose.As before,effective collision avoidance entails necessity of both predicting collision situations and performing maneuvers in advance.

T.Praczyk/Neurocomputing149(2015)559–572 566

4.3.Tunning the system

In order to determine both the skeleton of the ACS architecture and the fundamental assumptions with reference to the way of constructing the system,series of experiments were carried out.At the very start,an initial architecture of the system was proposed and then it was successively improved.The initial architecture can be described as follows:

range of vision?1Nm,

eight inputs:one input for course towards a next turning point,

and seven inputs for sector threats,

each sector threat corresponds to a maximum threat located in

this sector,

two outputs:one output for speed and one output for course, ACS all the time indicates course and speed for the vehicle, to evaluate each neural ACS?tness function(2)is used.

In all the tunning tests,AEEO was used to evolve neural ACSs. For each architectural variant,AEEO was run a total of240times–30runs for eight different parameter settings.To compare indivi-dual variants,results achieved for the best setting of each variant were applied.As mentioned above,the networks were trained with the use of30selected training scenarios.

4.3.1.Range of vision

The?rst activity during the tunning tests was to optimize the ACS range of vision,that is,the area around the vehicle in which all objects are considered to be danger and it is necessary to take them into account when planning a next move for the vehicle. Results of the tests are presented in Table1.

The experiments showed that lower ranges of vision yield generally better results than the greater ones.It means that the networks are more effective when they are not?ooded with excess information which only obscures picture of the situation. The in?uence on the results above had also high maneuverability of the vehicle which,as was assumed,is able to very quickly change direction of move.

Even though the best results were achieved for the range0.3Nm the decision was made to use in further tunning tests in the range of 0.5Nm.It seems that the value0.3Nm could be insuf?cient in real conditions.During preliminary tunning tests veri?cation of each neural solution was made based on the30training scenarios which do not represent all possible situations which can happen at sea.It seems that a better solution is to supply the network with slightly more information than to force it to work in conditions of insuf?cient information necessary to carry out an effective decision.

4.3.2.Threat

A next factor which has in?uence on effectiveness of the anti-collision networks is a way of calculating the sector threat.In the tunning tests three solutions were tested,i.e.:

maximum threat:T

maxesT?max o A O s TeoT,where O s is set of all objects in sector s, sum of threats:T

sumesT?∑o A O s TeoT,

average threat:T

averesT?1=j O s j∑o A O s TeoT.

Results of the tests presented in Table2revealed that applica-tion of the maximum threat to represent the sector threat is the best solution.Once again it appeared that to work properly the networks need only local information about objects which pose for them the greatest danger.The ASV is suf?ciently fast and maneu-verable that it does not require complete information from the whole sector to avoid collisions.

In further tunning tests,maximum threat was used to repre-sent the sector threat.

4.3.3.When the anti-collision networks control the ASV

This time,three further architectural solutions were tested,i.e.: Solution a:The anti-collision networks control the ASV regard-less of the situation at sea(the ASV all the time moves according to course and speed output signals from the networks).

Solution b:The anti-collision networks decide if to control the ASV or to order it to directly proceed to a destination(extra output applied to indicate when the ASV should move directly towards a goal and when it should obey network commands regarding course and speed).

Solution c:The anti-collision networks control the ASV only when some objects are visible within the range of vision, otherwise,the ASV moves directly towards a destination.

The results of the tests included in Table3showed again that the anti-collision networks are the most effective when they are relived of excessive duties as?ltration of input data or control of the ASV when it is unnecessary.Once again it turned out that the best solution is to maximally narrow down network scope of responsibility and to leave them only key decisions.

4.3.4.How to determine motion parameters for the ASV

In this case,two solutions were tested,i.e.:

Solution d:2-output networks–one output indicates course and the second one speed(Course output determines how the course should be changed in a next move,values from the range?à1801,1801?are available.To protect the vehicle from slight?uctuations of course output signals,the update on course is made only when the difference between consecutive indications of the network is larger than51.Speed output can select four speeds:5,10,15,and20knots.).

Table1

Results for different ranges((a)average number of scenarios in which ASV reached destination point,(b)average number of evolutionary iterations necessary to evolve network successful in all training scenarios,for ranges1and0.8the last parameter is not given because for these ranges not all evolutionary runs were successful).

Range(Nm)10.80.50.3

(a)26.3129.453030

(b)23,665.2115,822.16Table2

Results for different variants of sector threat(meaning of(a)and(b)as in previous table).

Threat Max Sum Average

(a)3028.2228.45

(b)23,665.21

Table3

Results for solutions a,b,and c(the table includes only average numbers of evolutionary iterations necessary to evolve successful network because in all analyzed cases,all evolutionary runs ended with producing networks which led the ASV to a destination in all30training scenarios).

Solution Solution a Solution b Solution c

(b)23,665.2111,394.234509.51

T.Praczyk/Neurocomputing149(2015)559–572567

Solution e:8-output networks–seven outputs determine course and one speed(Each course output indicates other maneuver:turn left at1351,turn left at901,turn left at451,go straight,turn right at 451,turn right at901,and turn right at1351.As before,a separate speed output can select four speeds:5,10,15,and20knots.).

The experiments reported in Table4show that8-output networks are more effective compared to2-output ones.The solution with8-outputs extends the size of the output layer of each anti-collision network in relation to the solution with 2-outputs and restricts maneuverability of the vehicle(only a few maneuvers),however,it also results in more stable work of the network.In2-output networks,each above-threshold change of the course output leads to a maneuver of the vehicle.For the network to smoothly control the vehicle,it has to learn that not each change of the input should result in change of the output. Parameters of the network and its topology have to be appro-priately set so that slight input?uctuations not to be a reason for drastic changes in network decisions.This may be a serious problem for a network construction method because in order for each network input to have in?uence on network decisions,each of them,directly or indirectly,has to be connected with the network output.In other words,to take effective decisions,an output neuron has to have complete information about the surrounding of the vehicle and,in consequence,it has to somehow be linked with each input.In turn,a connection with each input may result in a situation in which even a slight change of an input state entails a change in the course of the vehicle.

In the second solution,output values do not directly provide motion parameters of the vehicle.Each of them only indicates a maneuver which should be made in a next step.This causes slight input?uctuations not to result in changes of network decisions. The network is more stable and in spite of a greater number of parameters which have to be set it may be easier to construct. 4.3.5.Fitness function

In this part of the tunning experiments,three?tness functions de?ned in Section4.2were tested.The experiments whose results are presented in Table5revealed that evolution with?tness function(2)works faster than in combination with other func-tions.This function does not indicate the evolution which speci?c anti-collision behaviors of networks should be promoted and which should not.It encourages to create networks which quickly reach a goal and no matter what strategy they use for that purpose.Generally,function(2)does not impose constraints as for the number of maneuvers and their amplitude.The only exception are changes in course by more than901when there is no a direct danger for the ASV.Meanwhile,the remaining func-tions are more restrictive for the evolution and force it to search for networks with a speci?c behavior.Function(3)prefers net-works which lead the ASV in a very gentle way,whereas in the case of function(4)tendency is reinforced for evolving networks which rarely change course of the vehicle.Both functions narrow down the set of possible neural solutions which makes the task of the evolution more dif?cult and,in consequence,negatively affects its speed in searching for effective anti-collision networks.

Ultimately,after all the tuning tests reported above,the skeleton of the ACS architecture and the fundamental assumptions with reference to way of constructing the system were de?ned: range of vision?0.5Nm,

eight inputs:one input for course towards a next turning point,

and seven inputs for sector threats,

each sector threat corresponds to a maximum threat located in

this sector,

eight outputs:one output for speed and seven outputs for

course,

ACS decides about behavior of the vehicle exclusively when

some objects are within the range of vision,

to evaluate each neural ACS?tness function(2)is used.

4.4.Learning

A next phase in order to construct an effective,reliable,neural ACS was the learning phase.To increase probability of the?nal success and to have possibility of choosing from many different options,two different neuro-evolutionary methods were used to build ACSs,i.e.AEEO and CCENN.The former method is predis-posed to evolve monolithic neural networks whereas the latter one produces modular networks.Both methods have the capacity to adjust the architecture of the networks,i.e.the number of neurons,types of neurons,topology,and the number of modules, to a problem to be solved.This ability was very important because at the very beginning of the learning phase the nature of the problem(decomposable or indecomposable into sub-problems), and its complexity were unknown.

Table4

Results for solutions d and e(as before,the table includes only average numbers of evolutionary iterations necessary to evolve successful network because in all analyzed cases,all evolutionary runs ended with producing networks which led the ASV to a destination in all thirty training scenarios).

Solution Solution d Solution e

(b)4509.511074.32

Table5

Results for different?tness functions.

Function(2)(3)(4)

(b)1074.321507.112288.54In1

In2

In3

In4

In5

In6

In7

In8

Fig.9.Example anti-collision network produced by AEEO.

T.Praczyk/Neurocomputing149(2015)559–572 568

In1

In2

In3

In4

In5

In6

In1

In5

In7

In8

In5

In6

In7

Fig.10.Example anti-collision network produced by CCENN(the network presented in the?gure included a total of?ve modules,the?fth module was supplied with all accessible input information and was almost fully–connected).(a)Module1,(b)Module2,(c)Module3,and(d)Module4.

Fig.11.Example collision avoidance behavior of the ASV in the presence of land and other object,(a)planned path of ASV,(b)description of all the elements included in ?gures(length of the course line indicates speed of object),and(c–e)consecutive steps of simulation.

T.Praczyk/Neurocomputing149(2015)559–572569

To evolve the networks,110training scenarios were applied.The scenarios were arranged according to their complexity.Initi-ally,the networks had to face the simplest scenarios in which no object was colliding.All of them were visible for the networks,however,none of them crossed the course line between the starting and the destination point.Next scenarios were more and more dif ?cult,they inserted objects which moved in areas where potential routes of the vehicle led or concentrations of objects which created areas with a heavy traf ?c.

Both neuro-evolutionary methods were run many times for parameter settings which appeared to be the most effective in the tunning tests.As it turned out,25%of AEEO runs and 13%of CCENN runs ended with generating anti-collision networks which were able,without collision,and within an assumed time limit,

to

Fig.12.Example collision avoidance behavior of the ASV in the presence of other objects,(a)description of all the elements included in ?gures (length of the course line indicates speed of object)and (b –f)consecutive steps of simulation.

T.Praczyk /Neurocomputing 149(2015)559–572

570

lead the vehicle to a goal in each learning scenario.In total,we managed to generate more than50networks,effective in the learning https://www.wendangku.net/doc/7a15492329.html,works produced by AEEO included3–4hidden neurons and in most cases they were densely connected.The ones evolved according to CCENN usually contained4–6modules,each without hidden neurons.Individual modules often worked based on a fragmentary picture of the situation outside the vehicle,i.e. they were supplied with only a part of input information.Example effective networks produced by both neuro-evolutionary methods are presented in Figs.9and10.

4.5.Generalization

At the beginning,all the effective anti-collision networks from the previous phase of the experiments were tested on tasks which were not used before to build the networks(60scenarios).As it turned out,only a few of them coped with all the new tasks.It is necessary,however,to note that these networks made as many as 170collision-free voyages,mostly in dif?cult conditions of heavy traf?c,which means that they were very intensely tested and should be well prepared to avoid collisions.

To carry out the ultimate veri?cation of the networks which passed the?rst generalization exam,they were also veri?ed by a navigator in conditions close to the real ones.To this end,black-and-white variant of electronic navigational chart was used with the ability to simulate radar objects,AIS objects,and the ASV(see Figs.11and12).During each simulation,the goal of the ASV was to reach a destination point along a?xed trajectory consisting of a number of turning points.The vehicle was controlled by a network whereas motion parameters(speed and course)of additional objects were?xed by a navigator.Unlike in all the scenarios used before,the number of the objects as well as their parameters could be changed at each point of a simulation,moreover,trajectory of the ASV often led in the vicinity of the land.

As it appeared,all the networks which were put to the extra tests on the chart positively passed also this exam.They avoided land and properly controlled the ASV in the presence of objects which navigated close to the vehicle.The only problem which appeared during the simulation was when the ASV had to steer clear of a large obstacle,e.g.a large wreck located on the path from one turning point to another.In this case,the ASV controlled by an anti-collision network moved cyclically in opposite directions along the obstacle and could not?nd a way to a next turning point.A simple solution to this problem appeared,however,to be re-planning the route of the ASV when the cyclical behavior of the vehicle was detected.

5.Summary

The paper presents application of evolutionary neural networks to the collision avoidance task and the navigation problem in a complex,multi-object,rapidly changing environment.The colli-sion avoidance solution proposed in the paper is dedicated for fast, highly maneuverable Autonomous Surface Vehicle which has to be able to navigate among other fast objects which do not behave according to any widely accepted regulations.

To prepare anti-collision networks a series of experiments were carried out.Preliminary tests were necessary to establish the framework of the whole system,i.e.the range of vision,the input and the output layer,the form of input information,etc.Then intensive training of the networks was performed.To this end,two neuro-evolutionary methods were used,i.e.AEEO and CCENN.To ensure high reliability of the anti-collision networks and to prepare them to work in diverse conditions,training tasks applied during the evolutionary process were designed by a navigator,a navigational dilettante,and even by a genetic algorithm.The networks successful in all the training tasks were put to intensive generalization tests which selected a few of them whose skills were additionally con?rmed during extra tests on a navigational chart.Generally,all the experiments reported in the paper showed that neural networks produced in the evolutionary way can constitute an effective solution to the collision avoidance problem.

When using neural networks to collision avoidance problem it is however necessary to remember that their effectiveness highly depends on learning data used to train them.For example,neural ACS presented in the paper was designed based on learning scenarios which assumed application of ASV exclusively at open sea,the vehicle was also prepared to maneuver near the coast which however could not constitute a dominant element of the environment.To use the system also in other conditions,for example,in narrow passages,where the vehicle can be sur-rounded by land from many sides and generally safe distances to threats should be smaller,learning scenarios should take it into account,that is,they should also include cases with narrow passages.A different solution,in this particular situation,is a modular neural network consisting of two separate networks,one network would be responsible for navigating at open sea whereas the other in narrow passages.Each network should be however prepared based on appropriate learning data.Moreover,in such a case,the entire system should also be equipped with the element whose goal would be to switch between networks.

References

[1]S.Campbell,W.Naeem,G.W.Irwin,A review on improving the autonomy of

unmanned surface vehicles through intelligent collision avoidance man-oeuvres,Annu.Rev.Control36(2012)267–283.

[2]D.E.Goldberg,Genetic Algorithms in Search,Optimization and Machine

Learning,Addison Wesley,Reading,MA,1989.

[3]A.Jin-Hyeong,R.Key-Pyo,Y.Young-Jun,A study on the collision avoidance of a

ship using neural networks and fuzzy logic,Appl.Ocean Res.37(2012) 162–173.

[4]V.Kanakakis,N.Tsourveloudis,Evolutionary path planning and navigation of

autonomous underwater vehicles,in:Mediterranean Conference on Control and Automation.MED'07,2007,pp.1–6.

[5]K.Krishna,P.Kalra,Detection,tracking and avoidance of multiple dynamic

objects,J.Intell.Robot.Syst.33(2002)371–408.

[6]Y.Liu,A design and study on intelligence collision prevention expert system

for navigation(Ph.D.thesis),Harbin Engineering University,China,1999. [7]Y.Liu,W.Yang,The structure design of an intelligent decision support system

for navigation collision avoidance,in:IEEE The Third International Conference on Machine Learning and Cybernetics,vol.1,2004,pp.302–307.

[8]Y.Liu, C.Yang,Y.Yang,Case learning for CBR-based collision avoidance

systems,Appl.Intell.36(2012)308–319.

[9]W.Naeem,G.W.Irwin,A.Yang,COLREGs-based collision avoidance strategies

for unmanned surface vehicles,Mechatronics22(2012)669–678.

[10]I.K.Nikolos,E.S.Zografos,A.N.Brintaki,UAV path planning using evolutionary

algorithm,https://www.wendangku.net/doc/7a15492329.html,put.Intell.(SCI)70(2007)77–111.

[11]L.P.Perera,J.P.Carvalho,C.Guedes Soares,Autonomous guidance and naviga-

tion based on the COLREGs rules and regulations of collision avoidance,in: Proceedings of the International Workshop“Advanced Ship Design for Pollu-tion Prevention”,2009,pp.205–216.

[12]M.Potter,The design and analysis of a computational model of cooperative

coevolution(Ph.D.thesis),George Mason University,Fairfax,VA,1997. [13]M.A.Potter,K.A.De Jong,Cooperative coevolution:an architecture for

evolving coadapted subcomponents,https://www.wendangku.net/doc/7a15492329.html,put.8(1)(2000)1–29. [14]T.Praczyk,Using assembler encoding to solve inverted pendulum problem,

Comput.Inf.28(2009)895–912.

[15]T.Praczyk,P.Szymak,Decision system for a team of autonomous underwater

vehicles—preliminary report,Neurocomputing74(17)(2011)3323–3334. [16]Z.Shiller, https://www.wendangku.net/doc/7a15492329.html,rge,S.Sekhavat, https://www.wendangku.net/doc/7a15492329.html,ugier,Motion planning in dynamic

environments,Auton.Navi.Dyn.Environ.,STAR35(2007)107–119.

[17]R.Smierzchalski,Evolutionary trajectory planning of ships in navigating traf?c

areas,J.Mar.Sci.Technol.4(1999)1–6.

[18]R.Smierzchalski,Z.Michalewicz,Path planning in dynamic environments,

https://www.wendangku.net/doc/7a15492329.html,put.Intell.(SCI)8(2005)135–153.

[19]K.O.Stanley,R.Miikkulainen,Evolving neural networks through augmenting

topologies,https://www.wendangku.net/doc/7a15492329.html,put.10(2002)99–127.

[20]K.O.Stanley,R.Miikkulainen,Competitive coevolution through evolutionary

complexi?cation,J.Artif.Intell.Res.21(2004)63–100.

[21]R.Szlapczynski,J.Szlapczynska,On evolutionary computing in multi-ship

trajectory planning,Appl.Intell.37(2012)155–174.

T.Praczyk/Neurocomputing149(2015)559–572571

[22]A.Yang,Q.Niu,W.Shao,K.Li,G.W.Irwin,An ef?cient algorithm for grid-based

robotic path planning based on priority sorting of direction vectors,in: Proceedings of the International Conference on Life System Modelling and Simulation,LSMS2010,and International Conference on Intelligent Comput-ing for Sustainable Energy and Environment,ICSEE2010,Wuxi,China,2010.

[23]L.Young-il,K.Yong-Gi,L.Kohout,An intelligent collision avoidance system for

AUVs using fuzzy relational products,Inf.Sci.158(2004)209–232.

[24]B.Zeng,Y.Yang,Y.Xu,Mobile robot navigation in unknown dynamic

environment based on ant colony algorithm,in:WRI Global Congress on Intelligent Systems.GCIS'09,vol.3,2009,pp.98–

102.

Tomasz Praczyk is a Polish Navy of?cer working as a

senior lecturer at the Institute of Naval Weapon of

Polish Naval Academy in Gdynia.He received his M.Sc.

degree in computer science at the Military University of

Technology(in Warsaw)in1996.In2001,he received

his Ph.D degree;with thesis focused on using arti?cial

neural networks to identify ships.His research interest

is in intelligent naval systems,neuro-evolution,arti?-

cial immune systems,and reinforcement learning.

T.Praczyk/Neurocomputing149(2015)559–572

572

《驱动电机及控制技术》课程标准-电气自动化专业

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制。结合生产生活实际，培养学生对所学专业知识的兴趣和爱好，养成自主学习与探究学习的良好习惯，从而能够解决专业技术实际问题，养成良好的工作方法、工作作风和职业道德。 【知识目标】 掌握驱动电机的结构原理及应用，掌握功率变换器电路及其应用技术，驱动电机控制技术及新型电机的结构特点与选用。 【能力目标】 能对对驱动电机各种控制电路进行选择、应用和设计，能够准确描述各种电机控制技术的控制原理及特点，并针对不同电机选用不同的控制方式。 【素质目标】 能整体把握驱动电机及控制技术的应用及在日后的工作中解决实际问题。培养学生实事求是的作风和创新精神，培养学生综合运用所学知识分析问题和解决问题的能力，培养学生一丝不苟的工作作风和良好的团队协作精神。 五、课程内容设计 根据学院对机电一体化专业人才培养方案的要求，结合就业岗位的技能需求，按照职业教育理念，本课程设计了三个教学项目，具体内容如下：

驱动电机与控制技术技术试卷(A)

院 年 学期新能源汽车驱动电机技术课程试卷 共 3 页第 1 页题次 一 二 三 四 总分 得分 第一部分.概念辨析模块 请判断下列说法是否正确，正确在括号内画“√”，错误则在括号内画“×” （共25分，每空1分） （ ）1、新能源汽车要求驱动电机体积小、质量轻，具有高可靠性和寿命长。 （ ）2、新能源汽车无需要求驱动电机全速段高效运行。 （ ）3、电机驱动系统一般由电动机、功率变换器、传感器和控制器组成。 （ ）4、直流电机一般具有电刷装置和换向器。 （ ）5、电刷装置的作用是把直流电压、直流电流引入或引出。 （ ）6、磁导率是表示物质导磁性能的参数。 （ ）7、直流电机的工作原理是通电直导线在磁场中受力。 （ ）8、交流异步电机的工作原理是由三相交流电在定子绕组中产生旋转磁场，从而在鼠笼中产生感应电流，从而在磁场中受力。 （ ）9、永磁同步电机的工作原理是通过电子开关电路产生旋转磁场，转子根据磁阻最小的原理进行旋转。 （ ）10、无刷直流电机的工作原理是通过电子开关产生旋转磁场，转子跟随磁场旋转。 （ ）11、开关磁阻电机的工作原理是三相交流电在定子绕组中产生旋转磁场，由永磁铁构成的转子跟随旋转磁场旋转。 （ ）12、直流电机调速性能好，启动转矩大。 （ ）13、直流电机控制复杂，易磨损。 （ ）14、交流异步电机具有高可靠性，制造成本高。 （ ）15、无刷直流电机无换向器和电刷，结构简单牢固，尺寸和质量小，基本免维护。 （ ）16、开关磁阻电机一般定子凸极比转子凸极少两个。 （ ）18、开关磁阻电机的成本相对而言最低。 （ ）19、功率二极管基本结构和工作原理与电子电路中的二极管都是相同的。 （ ）20、占空比指的是电力电子开关的导通时间与开关周期之比。 （ ）21、直流斩波电路只有降压斩波电路。 （ ）22、PWM 整流电路采用脉冲宽度调制控制，能够实现电能双向变换。 （ ）23、轮毂电机结构简单、布置灵活，车辆的空间利用率高，传动系统效率高。 （ ）24、开关磁阻电机的噪音较大。 （ ）25、永磁同步电机和无刷直流电机的转子结构相似，都是由永磁铁组成。 第二部分.基本知识模块 下列题目只有一个正确答案，请选择正确答案并将代码填写在括号里。 （共15分，每题1分） 1.交流异步电机的转速为（ ）r/min 。 A 4000-6000 B 12000-15000 C 4000-10000 D >15000 2.永磁同步电机的转速为（ ）r/min 。 A 4000-6000 B 12000-15000 C 4000-10000 D >15000 3.磁通所通过的路径称为（ ） A 磁感线 B 磁场强度 C 磁路 D 磁阻 4.用于制造永久磁铁和扬声器的磁钢的是（ ）。 A 硬磁材料 B 软磁材料 C 矩磁材料 D 普通材料 5.用于制造计算机中磁存储元件的磁芯、磁棒和磁膜等的是（ ）。 A 硬磁材料 B 软磁材料 C 矩磁材料 D 普通材料 6.用于制造电动机、变压器和继电器的铁芯的是（ ）。 A 硬磁材料 B 软磁材料 C 矩磁材料 D 普通材料 7.右图的电路符号所示为（ ）。 A 功率二极管 B 功率MOSFET C IGBT D GTR 8.功率MOSFET 指的是( )。 系 班 级 姓 名 学 号 命题教师 教研室负责人 系 负责人 试卷类型 A ………………………………………密封线………………………………………密封

《驱动电机及控制技术》教学大纲

《驱动电机及控制技术》教学大纲 一、授课对象 本课程适用于汽车服务系新能源汽车制造与装配专业（中、高级）班三年制 二、课程学时 总学时108课时，6课时/周，1学期授完。 三、课程的任务和目的 本课程是中等职业学校电子技术应用与维修专业教材，是一门机电类专业课程。其任务是：使学生掌握常用电动机的结构及其控制方法，培养学生对常用电动机的维护、保养与检修的技能和解决实际问题的能力；对学生进行职业意识培养和职业道德教育，提高学生的综合素质与职业能力，增强学生适应职业变化的能力，为学生职业生涯的发展奠定基础。 本课程目的是：使学生能掌握电动类、制冷类日用电器中主要使用的三种电动机——单相异步电动机、直流电动机和单相串励电动机的结构、原理及应用，以及电动类、制冷空调类电器专用电动机的结构及其控制方法。熟悉对上述电动机进行维护、保养与检修。结合生产生活实际，培养学生对所学专业知识的兴趣和爱好，养成自主学习与探究学习的良好习惯，从而能够解决专业技术实际问题，养成良好的工作方法、工作作风和职业道德。 四、课程内容和要求 第一章：直流电动机8课时 1．教学内容： 第一节：直流电动机的结构和分类 第二节：直流电动机的工作原理与运行特性 第三节：直流电动机的起动、反转和调速。 2．教学要求与建议：了解直流电动机的基本结构和分类，掌握直流电动机的基本工作原理，理解直流电动机的起动、反转、调速的原理和方法，初步了解直流电动机常见故障的检修方法。 第二章：单相异步电动机10课时 1.教学内容： 第一节：异步电动机的结构和工作原理 第二节：单相异步电动机的分类 第三节：单相异步电动机的反转和调速 2．教学要求与建议：了解单相异步电动机的基本结构，掌握单相异步电动机的基本工作原理，理解异步电动机的分类和起动方式，了解单相异步电动机的反转、调速的原理和方法，初步了解单相异步电动机常见故障及其检修方法。 第三章：单相串励电动机12课时 1、教学内容 第一节：单相串励电动机的结构和运转原理 第二节：单相串励电动机的运行特性 第三节：单相串励电动机的反转和调速 2、教学要求与建议：理解单相串励电动机的基本结构和工作原理，了解单相串励电动机的主要特点和应用。 第四章：三相异步电动机16课时 1．教学内容： 第一节：三相异步电动机的结构和工作原理

生化常见异常结果及原因分析

生化常见异常结果及原因分析 反应曲线波动、跳动 一、几乎所有反应曲线跳动 1、电源接地不良：主电源接地不良、光电盒接地不良； 2、反应盘进水：真空泵压力密封圈磨损、真空泵电源接触不良、管接头处漏气、管路漏气、吸废液钢管堵塞、单向阀坏、比色杯破裂； 3、搅拌杆不搅拌：搅拌电机坏、搅拌电机线接触不良、搅拌杆顶住比色杯底； 4、光路歪：灯泡装歪、比色杯装歪、温控锅装歪、透镜装歪； 5、试剂加入异常：试剂针堵塞、试剂注射器脱落、试剂针接头处脱落、快速接头脱落； 6、一次性比色杯不干净；

7、阳光直射反应光电系统； 8、外界干扰因素：如发动机，电钻等。 二、只有个别波长反应曲线跳动 电源接地不良：主电源接地不良、光电盒接地不良。 三、只有个别项目反应曲线跳动 试剂异常：试剂变混浊、变色或试剂放错位置； 试剂经搅拌后起较多气泡，挡住光路。 反应曲线异常 一、几乎所有反应曲线形状正确，反应曲线平稳，但基本无反应 样本未加入：样本针堵塞、样本注射器脱落、样本针接头处脱落、快速接头脱落。

二、个别项目反应曲线形状正确，反应曲线平稳，但基本无反应 第二试剂未加入：第二试剂放错位置 三、个别项目反应曲线形状改变，但反应曲线平稳 试剂加入异常：试剂盘固定销钉脱落、试剂注射器漏气。 四、个别项目反应曲线形状改变，剧烈上升或下降，反应曲线或平稳，或小幅波动 试剂异常：试剂性能差或失效，最易发生在ALP、GGT、AMY等项目上。 五、个别测试反应曲线形状改变，剧烈上升或下降，但反应曲线平稳 1、试剂间交叉污染：最易发生在TG、TC、Glu和Bun等项目上；

2、样本异常：样本中含有某些药物或干扰成分，最易发生在TB、DB等项目上。 反应曲线正常，结果重复性差 一、几乎所有项目重复性差 1、样本加入异常：样本针半堵塞、样本注射器中有气泡、样本针接头处密封不严、样本针内壁阀关闭不严； 2、试剂加入异常：试剂针半堵塞、试剂注射器中有气泡、试剂针接头处密封不严、试剂针内壁阀关闭不严； 3、搅拌异常：搅拌电机坏、搅拌电机线接触不良、搅拌杆顶住比色杯底； 4、试剂/样本内外壁清洗不够，交叉污染大：液泵压力下降、第二级过滤器堵塞； 5、反应盘温度不稳定：反应盘温度波动较大。 二、个别项目重复性差

临床生化检验中反应曲线的应用分析

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with the provisions of the contract. The back street alleys and suburban , to do a good job in environmental health at the same time, do a good job in a timely public facilities maintenance, repair, install, completely solve the piles of garbage, sewage crosscurrent, heidengxiahuo, potholes and other phenomena, do the streets clean, orderly, the convenience of the public life. To some five on the improvement of the urban river construction site. Now our urban river environment really let people see not bottom go to, than the works of Lao She's "Longxugou ditch" is not strong to where to go. The next step, we should insist on treating the symptoms, the strict implementation of the "long river" responsibility system, strengthen the river daily cleaning, planting a number of decomposition and green vegetation, so that Hanoi no garbage, riverside, no debris, the river no smell. At the same time, it is necessary to focus on the effect a permanent cure, accelerate the rain sewage diversion, to carry out the system of comprehensive remediation, accomplish treat a river, into a piece of landscape. The building housing the departments should strengthen the construction site construction supervision, supervise the implementation of construction dust dust measures, the public security traffic police department To