The integration of ergonomics in the design of naval ships

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THE INTEGRATION OF ERGONOMICS IN THE DESIGN OF NAVAL SHIPS H. Schuffel, TNO Human Factors Research Institute, Soesterberg, The Netherlands

ABSTRACT

Under contract to the Royal Netherlands Navy a study was conducted concerning the design of interfaces for supervisory control of remotely controlled, unmanned

minesweepers. The framework of the study is a system ergonomic design approach including mission and function analysis, function allocation, task analysis, performance prediction and interface design. Simulator experiments were carried out to predict performance as affected by alternative function allocations.

The present article highlights some aspects of the project of interest, the Trojka

Sweeping System, concerning a mothership with four unmanned sweeping craft. The

aim of the project is to minimize crew members' life threat and to rationalize

efficiency of operations. By means of a man-in-the-loop simulator experiment the manual and semi-automatic control of the unmanned craft was examined in conditions inferred from mission and function analysis. Results show that manual control of four unmanned sweepers will not meet standards of the Royal Netherlands Navy. Besides unsatisfactorily system performance the operator workload appears to exceed accept-able standards. Semi-automatic control by means of path prediction and automated heading control is recommended.

INTRODUCTION

Most often the starting point of the design of a manned system is the identification of

operational needs.

To transform the operational needs into a system description, systems engineering follows a series of steps, involving various types of analyses, trade-off studies, simula-tion and other experimental tests. The sequence of system ergonomic design steps follows the same general pattern as systems engineering, including mission analysis, function analysis, function allocation, task analysis and performance prediction (Beevis, 1992). These steps are repeated several times in the course of the design process (see Figure 1). By analysing the mission, system functions are determined (see Figure 2). The analysis of system functions leads to functional requirements which are the basis of allocating the function to human or machine.

TECHNISCHE UNIVERSITIT Laboratorium voor Scheepshydromechan;ca Archlef Mekelweg 2,2628 CD Deft lei. O15.786B73-Faj 015.781838

Design and development activity

interface & workspace design

Information generated

Figure 1: The systems ergonomics approach (after Döring, 1983)

The detailed function analysis identifies the task performance required to the operator and to the machine. Finally the analysis of the operator and the machine performance

gives the data for interface design and workstations, environment design, workload evaluation and personnel selection and training (see also Booher, 1990). One of the advantages of this concurrent engineering approach over the linear design approach is its iterative nature, offering the opportunity to predict systems performance by means of man-in-the-loop simulation in the forward design phase and to improve overall system performance through adjusting human-machine interactions.

MISSION cOflauCt operation 2.1 observe air and water .2.3.1.1 - i perceive heading error 122 observe fairway and traffic 1,2.3.1 control heading 12.3.111 I I adjust heading monitor heading and speed 2

Figure 2: Example of a hierarchical decomposition of functions into tasks as a preparation for the function allocation process.

I 5 I 6

maintain maintain maintain maintain navigate platform cargo statuscrew communicatie ship

status status passengers routine

human functions

required task

performance f-workload evaluation.-\ display and control requirements, work station design requirements, working environment, personnel selection & training J mission requirements system functions machine functions required machine performance

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For the design of remotely controlled mine sweeping craft, the Royal Netherlands Navy followed the system ergonomics approach. The project of interest is the "Trojka Sweeping System" and concerns a mothership with four unmanned sweeping craft,

called drones. The aim of the project is to minimize crew members' life threat and to

rationalize costs of operations.

The mission of Trojka is the destruction of naval influence mines. The mine sweeping

operation is planned ashore or during transit. When arriving at the sweeping area, the

drones will be directed to their first tracks and the magnetic and acoustic sweep

systems will be activated. From that moment on, the drones will automatically execute the mission plan. During the sweeping phase the drones will be monitored from the mothership in order to verify whether the execution of the operation complies with planning and safety constraints. When necessary, the mission plan can be adjusted by

using the radio link. Depending on the situation it can be decided to overrule the

automation by manual input. Emergency routines will be available to ensure naviga-tional and system safety in circumstances when quick manual takeover is imperative or when normal control is lost due to malfunction.

The present article is focused on conditions of degraded automation, in particular how well operators can maintain safe control manually or with support of a course pilot or

a path predictor. (Gemen and Boer, 1994) In the course of the design process, a

number of functions were distinguished, such as "plan", "conduct" and "terminate" mine sweep operations. These functions were decomposed into subfunctions such as

"navigate' and into tasks such as monitor heading and speed" and "control heading". The accuracy of mine sweeping as affected by various levels of automation and human

supervision and control will be further highlighted in this article. Essential tasks in this process as inferred from the decomposition, are the minimalization of the off-track

distance and the minimalization of the difference between the required and actual

speed. The way these tasks can be supported by automatic devices can be elucidated as follows. When manually controlling a ship, tracking involves mental operations that

can be allocated to the operator or to hard/software. The elements involved in

tracking an externally programmed input (the track) with a ship are best appreciated by considering the man/ship system in Figure 3.

externat

degradations criteria disturbances

desired track

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actuaL heading (change) actuaL position (change)

heading order helmsman or outopitot rudder deflection order steering g ea r

actual rudder deflection

actuaL heading (change)

contrat force

ship

Figure 3: The ship tracking task as a function of the ship handlers' ability, the vessel dynamics and its disturbances, the task, the means for control, and the presentation of ship parameters and ship surroundings. (Schuffel, 1986)

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The most important elements of tracking tasks are prediction of inputs, prediction of outputs and anticipation of future deviations between input and output. (Schuffel,

1986)

With regard to the input, the desired track can be anticipated by the course of the

track ahead. This preview, up to approximately five ship lengths, may help to

compen-sate future deviations due to time lay. Moreover, the future course of an input signal

can be inferred through extrapolation of the input signal changes or from knowledge about input signals.

The same principles hold for anticipating the output of the controlled variables, such as heading and position. Actual heading and position can be perceived or sensed, future heading and position can be inferred through extrapolation of changes in actual values or through knowledge of the procescharacteristics.

The function allocation process should balance the contributions of human and automatic devices for the best performance. In this article the question will be answered whether one operator can supervise and control four unmanned drones

manually, with support of an autopilot or a path predictor, in case of a breakdown of

the automatic control.

It is expected that support of a path predictor (eg. Passenier 1989) will show excellent human ship performance. Experiments (Van Breda a.o. 1988, Bernotat 1971, Kelley

1968) have shown that path predictors improve the accuracy of manoeuvring through

reducing the need for an operator to mentally extrapolate the actual process status to

a future status, comparing this with the track ahead and calculating a future deviation. Support by a course pilot will not help to that extent since still calculations have to be made with regard to future positions and errors. Manual control will show worst performance since all activities regarding the four drones have to be performed more

or less at the same time to compensate for the time lags in handling. To check the

operators' workload in the various conditions, subjective ratings and secondary tasks, reflecting the information processing capacity of the operators were analyzed. These indicators were expected to reveal least workload in the path prediction condition and most workload in the manual condition.

Special attention was given to the design of the interface, but this information is not presented here.

METHOD

Thirteen trained subjects took part in the experiment. The navigation task was directly derived from the scenario and mission specifications giving good resemblance with real-life conditions. The subjects were to sail two or four drones over designated tracks while maintaining a certain sweeping speed. They were instructed to reduce the drones' speed when absolutely necessary only, not to sail the track backwards, to return smoothly to the track when being off-track in order to minimize the overshoot,

and to start a turning manoeuvre where the track ended, to make the turn as smoothly as possible, and to end the turn exactly at the beginning of the new track.

The task was presented in trials of 30 minutes. During a trial, a maximum of three drones were required to make a turn towards their next track. When sailing two drones, both of them were required to make a turn. The distance between successive

drones was such that only one drone had to change track at a time. While manoeuv-ring the subject had to correct for wind and current. The wind amounted a force of

Beaufort 3 across the track, the current varied in speed between 0.5 rn/s and i rn/s in different directions. a Distance To Track 168.00 C 90 30 \/ Manua' /\ V-point \/ SSC-Contro /\ Course 'J Track /\ Remote

Figure 4: This is a part of the navigation display. It represents one drone display. The

information indicated is (a) wind and current, (b) course pilot, (c) power, (d) speed

and (e) rudder deflection. Black triangles represent actual values of the devices and open rectangles represent settings. The black line in front of the drone is the velocity

vector, the bold black line perpendicular to this vector is the rate-of-turn indicator, the dashed line in front of the drone is the track predictor and the black line at the rear of the drone is the rudder.

The Trojka remote control navigation display consisted of four control fields, one for each individual drone (see Figure 4). Moreover, an overview of the sweeping area with intended tracks and positions of drones were shown.

Six conditions were presented to all subjects, combining the number of drones (2, 4) with the level of control (rudder control, control by course pilot, and control by course

pilot and a path predictor). (Table 1)

STOP

Table 1: Conditions

Number of drones Rudder control Course pilot Course pilot + path predictor Two drones 2R 2C 2T Four drones 4R 4C 4T 14 _{___ C} 10 0.J.__o

course pilot

and

path predictor

Figure 5: Standard deviation of the distance to track as a function of three automation

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To examine the operators mental workload, in particular with regard to monitoring surrounding ship traffic and platformsystems, secondary tasks were presented and subjective workload was estimated. For the purpose of this article it is only mentioned that these indicators, reflecting the spare information processing capacity of the

subjects, showed a correlation of .95 with a subjective workload index, ranging from low to high.

The displays and control devices for navigation, platform control and monitoring ship surroundings were simulated on two interacting computer systems (Linux 80486 DX2; SUNSPARC, RISK-processor). The mathematical models of the vessels were based on Keizer van Leeuwen models. During the experiment parameters like the drones'

speed, position, heading and turnrate were logged with a frequency of 0.2 H.

Subjects came for two successive days. The subjects were asked to perform primarily the navigation task and at the same time secondary tasks, but without compromising the navigation task. So, subjects were explicitly told to give priority to the navigation task. The first day the subjects were trained on the navigational and secondary task according to a balanced schedule. The second day the experiment was conducted.

Three dependent variables were measured for navigation: (a) root mean square of the distance between a drone and its designated track, (b) mean distance between the drone and the designated track, and (c) standard deviation of the drones' position

relative to the mean position. The standard deviation will only be shown here. RESULTS

Figure 5 shows that manoeuvring with a course pilot and a track predictor is most accurate. The difference between rudder control and both higher automation levels is most pronounced. Additional, the performance differences for the different automa-tion levels are more pronounced when sailing four drones instead of two. From an analysis of variance the influence of the variable "automation level" on performance

proves to be significant (F (2,18) = 71.32, p < < 0.01). The same applies to the influence of the number of controlled drones on the performance (F (1,9) = 78.58, p

< < 0.01) and to the influence of the interaction between the number of drones and the automation level on the performance (F (2,18) = 30.80, p < < 0.01).

E 60 L, CD o 40 u, o .2 20

level.

The subjective workload for both the secondary tasks and the navigation tasks are

presented in figure 6 as a function of the number of drones, with the automation level

as parameter.

The condition 'four drones, rudder" can be considered to offer a 100% workload for the operator. In this condition, all navigation measures and the measures of both

secondary tasks indicated poor performance. Moreover, in this condition only, subjects made errors in the "platform monitoring task". The condition drones, rudder" can be considered to offer a low workload for the operator, say 20%. In this condition, all navigation measures and the measures of both secondary tasks indicated good

performance.

A chi-square ANO VA concerning the ranking of the conditions confirms the

signifi-cance of these relations (Chi-square = 34.34, df = 5, p < < 0.05). loo 80 60 40 20 o

rudder course pi'ot course pi'ot

and

path predictor

Figure 6: Subjective workload as function of the automation level. DISCUSSION

For sweeping performance reasons direct rudder control is not recommended. A course pilot with or without a track predictor is necessary. When, because of planning procedures, it is possible to have two drones sailing parallel to each other or passing each other, within relatively short distance while sweeping adjacent tracks, manoeuv-ring with direct rudder control will not be safe.

Controlling four drones by direct rudder inputs is under no circumstances acceptable. Sweeping and safety standards will not be satisfied. Controlling four drones using a course pilot is possible from the view of sweeping effectivity. Controlling four drones with a course pilot and a track predictor is possible according to all standards;

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The data show that controlling two drones with a course pilot is from an operator's

viewpoint, equivalent to controlling four drones with a course pilot and a track

predictor. The availability of a path predictor makes it possible for the operator to

control twice as many drones compared to not using a predictor. Manoeuvring accuracy improves by a factor 3.5 on average.

Because of unfavourable circumstances in practice (interference from other crew members, stress, motion of the ship, complexity of realistic situations), the workload

on the operator could be higher than measured here. When considering these factors, an experimental workload of 50% or lower can be stated as acceptable. Applying this

standard to the data, it can be concluded that controlling two or four drones using direct rudder control or four drones with a course pilot results in an operator

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