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International Journal of Production ResearchVol. 50, No. 1, 1 January 2012, 161176Impact of dynamic virtual and real robots on perceived safe waiting timeand maximum reach of robot armsParry P.W. Nga, Vincent G. Duffybcd*and Gulcin YucelbeaSchool of Industrial Engineering and Engineering Management, The Hong Kong University of Science and Technology,Clear Water Bay, Kowloon, Hong Kong SAR, China;bSchool of Industrial Engineering, Purdue University, West Lafayette,Indiana, USA;cRegenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, Indiana, USA;dSchool ofAgricultural and Biological Engineering, Purdue University, West Lafayette, Indiana, USA;eSchool of IndustrialEngineering, Istanbul Technical University, Istanbul, Turkey(Final version received February 2011)This research examines perception of dynamic objects and robots in a virtual and real industrial workenvironment. The studies are modelled after those of Karwowski and Rahimi from the early 1990s. Byapplying virtual reality technology, the real workplace can be simulated in the virtual world for theimprovement of facility design. Perception of hazard and risk, safe waiting time, maximum reach of robot armare measured related to the impact of parameters such as robot size, speed and type and exposure to a virtualaccident. Analysis includes techniques such as sequential experiments to compare results in the virtual and realenvironments. These methods may be considered as a model for studying perception and transfer in otherdomains. The comparison of the analysed data in the virtual and real environments helps to further determinethe transferability of performance and perception from virtual reality to real. Results show similarity inperceived safe waiting time, but there are large differences in perceived maximum reach of robot arms betweenthe virtual and real environments. Using the preliminary results from the integrated data in the sequentialexperiments, potential guidelines for using virtual facility layout in industry are discussed.Keywords: humanrobot interaction; perception of hazard and risk; maximum reach of robot arms;sequential experiment; data bridging1. IntroductionMany industrial companies utilise industrial robots to perform dangerous tasks in industry to avoid possiblehazards. Still, others are attempting to reduce musculoskeletal disorders through the use of hybrid automation suchas assist devices (Nussbaum 2000). According to the World Robotics 2007 published by Union Nations EconomicCommission from Europe (), there were approximately 951,000 units in 2006 and it is expected to be1,200,000 units in 2010 (Table 1).Since the robots are used more frequently in workplaces, issues related to humanrobot interaction (HRI) aremore often considered by researchers and practitioners. According to Dhillon et al.s (2002) survey, 523 papersabout robot safety and reliability were published between 1973 and 2000. Most of this research was done between1982 and 1990, and robot safety and reliability research has been incrementally decreased since 1986 (Dhillon et al.2002). On the other hand, between 1995 and 2000, multidisciplinary research in HRI has been more recently startedby the collaboration of researchers from human factors, robotics, cognitive science, psychology and naturallanguage, and during that time many conferences and workshops were dedicated to HRI such as IEEE InternationalSymposium on Robot and Human Interactive Communication (RoMan), Association for the Advancement ofArtificial Intelligences (AAAI) Symposia Series, IEEE International Conference on Robotics and Automation(ICRA) (Goodrich 2007). Since 2006, international HRI conference has been held annually.One of the issues related to HRI is the safety of the users. In reference to the statistics on Occupational Injuriescompiled by the Labour Department of Hong Kong Government in 2007, there were 3967 injuries and 21 fatalitiesin the manufacturing industry in total. They indicated that there had been 491 workers injured due to strikingagainst or being struck by dynamic moving objects (the number one type of accident) in the manufacturing industry(Hong Kong Labour Department 2007). Previous research on robot safety has been done along two separate*Corresponding author. Email: duffyISSN 00207543 print/ISSN 1366588X online? 2012 Taylor & Francis/10.1080/00207543.2011.571452lines: physical approach and cognitive approach (Laschi et al. 2007, Pervez and Ryu 2008, Santis et al. 2008). In thephysical approach, in order to ensure safe interaction, studies related to mechanical design and actuation(Yamada et al. 1997, Bicchi and Tonietti 2004, Stopp et al. 2005) and system controlling and planning (Brock andKhatib 2002, Heizman and Zelinsky 2003, Ikuta et al. 2003, Kulic and Croft 2005, 2006) were done. Also, collisiontests with a robot manipulator and dummy or test bed were conducted to evaluate the potential injury risk bycollisions (Haddadin et al. 2007, 2008, 2009). In the HRI safety increasing approaches through mechanical redesign,electronic or physical safeguards or operator training, only the robots perception is considered and the humansperception of safety during the interaction is not taken into account (Bartneck et al. 2009). However, only physicalaspects of HRI research alone cannot provide safety. Cognitive aspects such as the human perception of the robothave been shown to improve physical interaction and increase robot safety (Santis et al. 2008).This article focused on cognitive aspects of robot safety. Studying operators perception of robot operationalcharacteristics can help to understand the human perception of hazard, therefore it can lead to develop safetyinterventions and guidelines or hazard prevention strategy development. For safe and productive HRI, not onlyphysical safety supports based on robots perception but also cognitive supports based on humans perceptionshould be considered. Perception of safety in HRI has been measured by questionnaires, physiological sensors anddirect input device (Bartneck et al. 2009). Rani et al. (2002, 2004) used heart-rate analysis and multiple physiologicalsignals to estimate human stress levels. Nonaka et al. (2004) designed a virtual humanoid robot and measuredhuman reactions through heart rate measurements and subjective responses. Kulic and Croft (2005) used both aquestionnaire and physiological sensors data to estimate the users level of anxiety and surprise during interactionswith an industrial robot. Koay et al. (2005) measured human reactions to robot motions online. Aria et al. (2010)used both physiological parameters such as skin potential response and questionnaire to evaluate the mental straincaused by robot motion.In this study, the impact of size, speed and type of robots and accident exposure on human perception of hazardand risk of robot motion, safe waiting time of robots during system halts, improper pauses of robot operations andmaximum reach of robot arms in the virtual and real workplaces are investigated. Since the methodology ofinvestigating the varying robot parameters is well developed and constructed by Karwowski, the methodology wasreplicated to examine the human perception of idle times and the work envelope of the robot on the perception ofhazard and risk for Asian participants (Hong Kong Chinese people). Duffy et al. (2006) investigated the perceptionof safe robot speed in virtual and compared it to other published data about real industrial environments. Theresults show that the virtual environment can simulate the conditions for testing human perception of safe speed.Aria et al. (2010), evaluated the mental strain of the human operator related to the following design parameters:distance between the human operator and robot, speed of robot and existence of a notice of robot motion. Also, Oret al. (2009) investigated the perception of safe idle time of an industrial robot in a virtual environment andconsidered the following factors: simulated accident exposure, gender, robot speed and size. Evaluation of theperception of the risks and hazards for the other dependent variables such as maximum reach of robot arm and saferobot idle time is also needed (Duffy et al. 2006). Therefore, in this article, maximum reach of robot arm and saferobot idle time are considered.Goodrich and Schultz (2007) defined the accepted practices in the HRI area; one included experiments both withsimulated and physical robots. Because of cost and reliability concerns, most of the time it is not possible to conductexperiments with real robots. On the other hand, in simulation experiments, the real worlds detail situations cannotbe represented very well. Therefore, in this study the experiments were done both in real and virtual environments.Analytical results are compared in the dynamic virtual and real environments with moving hazard using theTable 1. Summary of number of robots working in industry worldwide.Number of robotsWorking in industry worldwide951,000Projected to be working by 20101,200,000Per 10,000 manufacturing employees in Japan349Per 10,000 manufacturing employees in Republic of Korea187Per 10,000 manufacturing employees in Germany186Per 10,000 manufacturing employees in the United States99Source: World Robotics (2007), United Nations Economic Commission for Europe.162P.P.W. Ng et al.sequential experimental techniques (Snow and Williges 1998). Based upon the results obtained from the experiment,the transferability of the experience, perception from virtual to real worlds and similarities and differences of theresults given by Karwowski (Karwowski et al. 1988a, 1988b, Karwowski and Pongpatanasuegsa 1990, Rahimi andKarwowski 1990, Karwowski and Rahimi 1991) and this research can be shown.1.1 Safe robot speedThe causes of accidents related to robots could be ascribed to some human perceptual, physical and psychologicallimitations including human perception of robot size, speed and range of motion that can affect the humanbehaviour (Carlsson 1984). Different speed of robots can cause different perceptions of hazards. Kulic and Croft(2006) and Ikuta et al. (2003) used velocity as an input while developing a danger index during HRI. The forceexerted by robot arms is high with fast speed of robot motion. However, it should be noted that Haddadin et al.(2007) conducted crash tests with robot and dummy head to decide the impacts of collisions between robot andhuman. They reported that a robot, with arbitrary mass driving moving at speeds up to 2m/s cannot be dangerousto a non-clamped head with respect to the severity indices used in the automobile industry that are based on headacceleration. It can also be noted that other research reported that the human was not in danger for impact with thehuman chest, abdomen and shoulder at robot velocities up to 2.7m/s. Beside robot velocity, robot masss affect onhead injury criterion (HIC) was also investigated and it was reported that a heavy robot cannot pose a significantthreat to the human head by means of HIC (Haddadin et al. 2008, 2009). Even though, the safe robot operatingconditions (such as speed under 2.7m/s, mechanical output under 150N, etc.) remove physical risks, HRI stillinvolves risks related to the mental strains caused by robot motion (Aria et al. 2010).It should be emphasised that this study is focused on cognitive aspects of robot safety. In this study, robot speedsare chosen 25 and 90cm/s for experiments, above and below the thresholds of concern, since it was previouslyshown that people feel threatened by robot speed above 64cm/s (Karwowski and Rahimi 1991). Aria et al. (2010)studied mental strains of a human operator in a cell production system where an operator assembles a product withthe aid of parts feeding by the robot. Based on their physiological assessment and subjective assessment results, theoperator feels discomfort when the robots speed is more than 500mm/s. It can also be mentioned that the initialimpact may not be the greatest reason for the concern expressed by the operators. Especially with large robots,operators are aware that the potential for a pin of body parts against other objects after impact is highly likely if acollision occurs since the robot does not necessarily stop after impact whereas in an auto and in transportation,there is nothing to continue to drive the collided objects together after initial impact. Hence, operators perceptionof their own reaction time may be influencing their perceived safe robot speed rather than simply a concern over thedamage at initial impact.1.2 Perception of safe robot idle timeThe American National Safety Standard, American National Standards Institute (1986) ANSI R15.06 wasestablished for robot safety in the United States. Also, the Occupational Safety and Health Administration (OSHA)in the US provided guidelines for robot safety (OSHA 1987). The standards related to robotic safety are summarisedin Table 2.According to Bonney and Yong (1985) and Nagamachi (1986, 1988), the complex robot systems are potentiallyhazardous even in the normal mode of operation. Most accidents happened because robot operators misperceive thereasons for pauses, which are either system malfunctions or programmed stops (Sugimoto and Kawaguchi 1983).Accident reports have shown that people can be injured or be killed by robot arms if they misperceive the workenvelope and enter it during the robot operation.1.3 Simulated accidentA simulated accident can be introduced to influence the behaviour since the expected shift in the processing ofinformation brings the task into the cognitive realm (Lehto and Papastavrou 1993, Park 1997). Rahimi andKarwoski (1990) suggested that the idle times must be considered in designing the facility layout and robotprogrammes. It is expected that the exposure to a simulated accident will influence the waiting time to enter theInternational Journal of Production Research163work envelope for both robots. As suggested by Parsons (1986, 1987), it has been shown that the simulated robotaccidents influence the robot operator after training (Karwowski et al. 1991).Hypothesis 1It is expected that factors of exposure to a simulated accident, size, speed and type of robots will affect waiting times(i.e. idle times) significantly in both the virtual and real environments.1.4 Perception of maximum reach of robot armsThe robot work envelope is defined as the maximum reach of robot arms or the unsafe zone of a robot. Accordingto Karwowski (1991), the maximum reach of robot arms were significantly affected by factors such as accidentexposure, size, speeds and type of robots. The methodology from Rahimi and Karwowski (1990) and Karwowskiet al. (1991) are replicated in this study and the results will be compared.Wright (1995) reported that real world distance perceptions are usually 8790% of actual distances. Lamptonet al. (1995) showed the tendency for underestimating distance in both the virtual and real environments, but thedistance in virtual were more extremely underestimated than that in real. Witmer and Kline (1998) attempted todetermine how accurately stationary observers could estimate distances to objects (i.e. cylinders) in a simple virtualenvironment, given by static cues for distance and defined perceived distance judgement by referring to tasks inwhich stationary observers judge the distance between themselves and a stationary or moving object immediatelyperceivable to them. Based on the results from Witmer and Kline (1998), people generally underestimated distanceto the objects in the virtual and real environments, but the errors in distance estimation was to be greater in virtualthan that in real. Moreover, they showed that the size of the object (i.e. cylinders) influenced the estimated distancesignificantly but floor texture and pattern did not.Hypothesis 2Perception of the work envelope of the robot is related to exposure to a simulated accident, speed, types and sizes ofrobots in both the virtual and real industrial work environments.1.5 Sequential experimentation and data bridgingA sequential experimentation research strategy was proposed by Williges and Williges (1989), which could beutilised for human factors studies to investigate and examine a large number of independent variables using a seriesof small sequential studies. The results from the sequential studies can be integrated to build the empirical models toexplore the effects of different independent variables and predict human performance (Han 1991).Table 2. Robot safety standards.ANSI/RIA R15.06-199The American National Safety Standard Robot safetyIncludes risk assessment, methodology andguidelines for safeguarding robotic systemCSA 2434:2003Canadian Standards AssociationSimilar to US standards by minor differencesISO 12100International Standard Office Safety ofMachinery StandardBasic concepts, general principles for designISO 10218International Standard Office Robots forindustrial environments safetyrequirementsRequirements and guidelines for the inherentsafe design, protective measures, and infor-mation for use of industrial robots.IEC61508Functional safety of electrical/electronic/programmable(E/E/PE) electronicsafety-related systemsRequirements to minimise dangerous failuresin E/E/PE safety-related systems.OSHAOccupational Safety and HealthAdministrationAn interpretation of ANSI standards and adirective concerning of robotics safetySource: Spada (2005).164P.P.W. Ng et al.Data bridging can be treated as a statistical method for integrating results across sequential studies (Han 1991).If there are no significant differences in the responses from the common data points, the data can be considered asfrom the same experiment and combined into a common data set in order to build the model (Snow and Williges1998). Based on these results, it is believed that a comparison of virtual and real experiments could be allowed if thedata could be merged into a common data set based on the use of data bridging in the virtual experimentalconditions.2. Methods2.1 Subjects for robot experimentSixty-four (32 males and 32 females) engineering students were recruited from the Hong Kong University of Scienceand Technology (HKUST). The subjects of the experiment had a basic understanding about robot programmingand operations. The experiments took about 2h. Each participant was paid 200 Hong Kong dollars(7.8HKD1USD) for their participation. All participants were divided into eight groups with eight participantsin each group.2.2 Equipments (robot experiment)Two industrial robots (Yaskawa MOTOMAN-K10S and SONY SRX-410) were investigated in this research. Bothrobots are located in the CAD/CAM laboratory of the Hong Kong University of Science and Technology. YaskawaMOTOMAN-K10S is a vertically articulated robot with six degrees of freedom and is mounted on the floor. Itscontroller is a servo-drive controlling system. The payload capacity of the robot is 10kg. The position repeatabilityof the robot is 0.1mm. The base rotation of the robot is 320 degrees about the base. The maximum reach of robotarm of MOTOMAN-K10S is 1555mm, and the combined linear speed of all axes is 1500mm/s. Its positionrepeatability is 0.1mm.The SONY SRX-410 is a SCARA-type high-speed assembly robot. It is a compact desktop design with four axesDC servo motor control. The work envelope of the robot is 600mm (first arm: 350mm; second arm: 250mm). Themaximum speed of linear motion (first and second arms combined) is 5200mm/s. The weight of the robot is 60kg(132.2lbs). Its payload capacities are 5kg (at low speed), 3kg (at medium speed) and 2kg (at high speed). Theposition repeatability of robot for the X/Y-axis and Z axis are 0.025mm and 0.02mm, respectively. Figures 1 and 2show the Yaskawa MOTOMAN-K10S and SONY SRX-410 robot, respectively.The real workplace with two robots is simulated in the dynamic virtual world by using the Virtual RealityModeling Language (VRML). A Pentium III (600MHz) computer with a Sony 1700Video Display Terminal (VDT)monitor (V-frequency 75Hz; H-frequency 60kHz) was used and Java Script provided animation in the VRMLenvironment. In the VRML environment, the robot motion was controlled (start and stop) through the program(buttons of control panels) using an Internet Explorer browser with Cosmo player plug-in. Since the VRMLprogram is a virtual internet-based system, the computer is needed to connect the network to the Internet WorldWide Web (WWW). Figures 3 and 4 show the MOTOMAN K10S and Sony SRX-410 robots in the virtualenvironment, respectively.2.3 Experimental designIn this study, the four experiments can be divided into two main categories: (1) idle time experiment and(2) maximum reach of robot arm experiment. Each category of the experiment had two parts: one was thevirtual part and the other one was the real part. All participants belonging to the real group were requiredto perform some virtual trials to fulfil the requirement of data bridging for the sequential experiment in orderto do the common point testing. For the real group, half of the subjects (16 subjects) performed tasks inthe real environment first and the other half performed some tasks in the virtual environment first. Asimulated accident was shown to half of the subjects (16 subjects in the real group and 16 subjects in thevirtual group).International Journal of Production Research1652.3.1 Idle time experimentThis experiment was a six-factor ANOVA mixed design (2 genders?2 accident exposures?2 speeds?2 sizes?2 types of robots?2 lighting levels). All independent variables had two levels. The between-subject variables aregender (males or females) and accident exposure (Yes or No). The other four within-subject independent variablesare speed of the robot (10cm/s or 90cm/s), size of the robot (large or small), types of robots (Sony SRX-410 orYasakawa MOTOMAN-K10S) and lighting condition (bright or dark). The dependent variable perceived idleFigure 1. Yaskawa MOTOMAN-K10S.Figure 2. Sony SRX-410.Figure 3. Virtual Yaskawa MOTOMAN-K10S.Figure 4. Virtual Sony SRX-410.166P.P.W. Ng et al.time of robot is the time that the subjects wait as long as they feel that it is safe for them to enter the work envelopeof the robot when an unexpected stop occurred that may be some trouble with the robot. The idle time of the robotis the time elapsed between the robot stop and subjects decision to enter the work envelope (Karwowski andPongpatanasuegsa 1990).2.3.2 Maximum reach of robot arm experimentThis experiment was setup considering a mixed ANOVA design. It had between-subject variables (gender andaccident exposure) and within-subject variables (lighting condition, speed of robot, size of robot, type of robot andangle of approach). All factors had two levels. Perceived maximum reach of the robot arm can be defined as themaximum reach of the robot arm in relation to the actual robot envelope (Karwowski et al. 1991). The distancefrom the base of robot to the tip of toe of the subject was measured in the real environment by the experimenter.In the virtual environment, the internal measurement system was developed to display the X, Y and Z coordinates ofviewpoints instantaneously. These coordinates can help the experimenter to calculate the accurate stopping position.2.4 Experimental procedureAt the beginning of the experiment, subjects filled in and signed the Informed Consent Form to give their personalinformation (e.g. age, gender, weight and height of the subject) and guaranteed their willing and honestparticipation in the experiment. If any participants had problems seeing when they wore corrective lenses, then theywould not be allowed to participate further in the experiment. The immersive tendency and personality of eachsubject were also assessed. Then, the detailed introductions about experimental procedures were explained. Industryguidelines for robot safety and safety regulations in the CAD/CAM laboratory and workshop were told to theparticipant. During the preview portion of the testing scenario, the participants observed robot motion in both thevirtual and real environments. Also, they saw the simulated accident if the subjects belonged to the accidentexposure group. These subjects could see a scenario with a virtual simulated accident that was created beforehand.In this scenario, a virtual human was knocked down by the industrial robot while it was rotating. Figure 5 shows theaccident scenario after the virtual mannequin human was knocked down by the industrial robot.2.5 Idle time experiment2.5.1 Experiment in virtual realityThe robot motion randomly paused 10 times at four different time intervals: 5s (4 times), 10s (3 times), 15s (2 times)and 20s (1 time). For each pause, the subjects were instructed to estimate the idle times of the pause. The subjectcircled one answer of time estimation from the three choices: greater than 10s, less than 10s or cannot tell. Then,subjects were told that the next pause could be a pre-programmed stop or a stop due to system malfunction. Theywere required to enter the work envelope of the robot to perform the diagnostic checking. Subjects waited as long asthey felt the robot would not move again. Before the subjects had actually walked into the work envelope, theyverbally informed the experimenter of their decision.In addition to measuring the time taken between the robots stop and subjects decision to enter the workenvelope, the experimenter would turn-off the main power supply of the robot to ensure that the robot would notmove again. Then, the subjects were allowed to enter into the work envelope of the robot. When the viewpoint ofsubjects moved closely towards the robots, the subjects could observe two diagnostics buttons (which indicate twocharacters: B and 8) placed at the base of the robot in the virtual environment. The experimenter instructed thesubject to press one of these two buttons.After the subjects pressed the button, a message was displayed on the screen that was either malfunction orpre-programmed stop. The subjects were asked to repeat the previous steps until they finished all the trials. At theend of this virtual experiment, the subjects answered additional questions to test their sense of presence.2.5.2 Experiment in real environmentThe 10 pauses at four different time intervals were randomly generated during 5min of robot motion. After that, thesubjects were instructed to estimate the time of each pause by selecting one of the three choices: greater than 10s/lessthan 10s/cannot tell. Subjects were told that the next pause could be a stop due to a system malfunction or pre-International Journal of Production Research167programmed stop. They were also required to perform the diagnostic checking by entering the work envelope of therobot and were instructed to wait as long as they felt the robot would not move again.Participants communicated with the experimenter verbally to inform them that they had decided to enter thework envelope before they really walked into it. The experimenter measured the time between the robot stop and thesubjects decision to enter the dangerous zone of the robot.Experimenter instructed the subjects to open the box and retrieve the paper to determine whether the stop iseither a malfunction or a pre-programmed stop. Participants evaluated their perception and mental workload foreach trial, and then repeated the previous steps again until all the trials were finished.2.6 Maximum reach of robot arm experiment2.6.1 Experiment in virtual realityParticipants were instructed to move towards the robot along one of the angles of approach. After the subjectsclicked on the start button of the control panel, the viewpoint of the subject approached the robot until the subjectFigure 5. The simulated accident scenario shows that the virtual mannequin was knocked down by the robot.168P.P.W. Ng et al.pressed the stop button to stop their movement. Their stopping position is that which they felt was the maximumreach of the robot arm. By using an internal measurement system, the experimenter recorded the value displayed atthe top left corner of the screen.The displayed values were the coordinates of the viewpoint (X, Y and Z coordinates). Participants were thenasked to return to their original place behind the safety barrier, and they repeated the previous steps for otherremaining angles of approach until they finished under all angles of approach. The experimenter then changed theviewpoint to observe the other robot. The experiment was repeated for other angles of approach and speeds of robotuntil all the trials were complete.2.6.2 Experiment in real environmentThe experimenter switched off the power of the robot in order to avoid the robot moving again when the robotmotion was completed and stopped at a specific position. Then, subjects were instructed to move towards the robotalong one of the two approach angles that they felt would be the maximum reach of robot arm. The experimentermeasured and recorded the distance between the subjects stopping position and work envelope of the robot.Subjects were asked to answer the level of difficulty for every trial and were instructed to return to their originalposition outside the barrier. The previous steps were repeated until the subjects finished all the trials.3. Results and discussion3.1 Idle time experiment3.1.1 Virtual partThe idle time data for virtual experiments is shown to follow a normal distribution (Table 3). For the perception ofidle time in the idle time experiment (VR group), the ANOVA test shows that the main effects of speed(F(1,28)117.522, p50.001) and size (F(1,28)107.914, p50.001) were significant. None of the two factorinteractions were significant. The main effect plots of the experiment indicate that size and speed of the robotaffected the waiting time significantly. With the larger size of robot in motion, the subjects had longer waiting timesin making a decision to enter the working envelope. Subjects waited a significantly longer time (p0.014)Table 3. Results of KolmogorovSmirnov test on perception of idle time forthe virtual group and the real group.Virtual groupOne-sample KolmogorovSmirnov testIdle timeN256Normal parametersabMean22.3849SD8.9185Most extremeAbsolute0.049DifferencesPositive0.049Negative?0.026KolmogorovSmirnov Z0.784Asymp. sig. (2-tailed)0.570Real groupIdle timeN128Normal parametersabMean24.0889SD7.5296Most extremeAbsolute0.047DifferencesPositive0.047Negative?0.029KolmogorovSmirnov Z0.531Asymp. sig. (2-tailed)0.941Notes:aTest distribution is normal;bCalculated from data.International Journal of Production Research169before deciding to walk into the work envelope for diagnostic checking when the robot operated at the fasterspeed (90cm/s).3.1.2 Real partIn the normality test (one-sample KolmogorovSmirnov test) of the real experiment I the real group in table,the p-value (p40.05) shows the data of idle time follows normal distribution (Table 3). According to the resultof the ANOVA test, the perception of idle time was also significantly influenced by the speed (F(1,28)34.347,p50.001) and type (F(1,28)5.499, p0.026) of robot, respectively. Similarly, there were no two-factorinteractions in the perception of idle time in this part of the experiment (VR group). The main effect plot of robotspeed showed that the perceived idle times of the fast robot speed were longer than that of the slow robot speed. Thisshowed similar phenomena with the result of the VR group (idle time experiment). The subjects appeared to waitlonger before indicating their decision for the Motoman K-10s robot than for the Sony SRX-410 robot in the realenvironment. Since the size cannot be isolated in the real environment, it is reported that the effect of robot type hasa significant effect on perceived idle time in this experiment without taking the size of the robot into consideration.3.2 Maximum reach of robot arm experiment3.2.1 Virtual partFrom the ANOVA results, the main effects of speed (F(1,28)6.808, p0.014), type (F(1,28)10.812, p0.003)and size (F(1,28)4.722, p0.038) on perception of the robot work envelope are significant, but gender, accidentexposure and angle are not. Moreover, none of the two-factor interactions are significant.Based on the result of main effect plot of maximum reach of robot arm experiment (VR group), the subjectscame significantly closer to the robot work envelope in the low speed than in the high-speed setting. The resultindicates that subjects perceived a significantly larger work envelope for the desktop-type Sony SRX-410 than forthe floor-mounted-type Motoman K10s robot. The main effect of robot size indicates that the perceived workenvelope of the large robot is significantly larger than that of the small robot. The subjects felt safer when operatingthe robot with a small size in the virtual environment.3.2.2 Real partThe result of ANOVA analysis shows that the perceived maximum reach of the robot arm is significantly influencedby accident exposure (F(1,28)4.388, p0.045), speed of robot (F(1,28)20.419, p50.001) and type of robot(F(1,28)302.125, p50.001) at the 5% level. As well, the interaction effect between type and accident shows asignificant effect on the perceived distance of the robot work envelope.The perceived distance of the robot work envelope for the with simulated accident group is higher thanthat for the without simulated accident group. It clearly shows that the exposure of the virtual accident affectstheir perceived distance in the real environment. With accident exposure, the subjects feel that the robot motionis much more hazardous. After the subjects were exposed to the simulated accident, they have a highervigilance. Results indicate that subjects select the higher maximum reach of the robot arm under the fasterspeed of robot motion. When the speed is slower, the subjects felt safer and more comfortable. The perceptionof maximum reach of the robot arm for the MOTOMAN K10S robot is smaller than that for the SONY-SRX410 robot.3.3 Data bridging3.3.1 Comparison virtual parts in VR and real group in idle time experiment(Common data point: Speed of robot10cm/s, type of robotMOTOMAN K10S, size of robotlarge and lightconditionwith light).By using the ANOVA test, it shows that there is no significant difference between the VR and real groups in thevirtual trials of the perceived idle time at the common data point (F(1,56)0.958, p0.332). Therefore, the datacan be combined into a common data set under the same experimental condition.170P.P.W. Ng et al.3.3.2 Comparing the virtual parts in the virtual and real group in maximum reach of robot arm experiment(Common data point: Speed of robot25cm/s, type of robotMOTOMAN K10S, size of robotLarge andangle of approach77.1 degree).From the result of the ANOVA test, there is no significant difference between the virtual and real groups in thevirtual trials of perceived maximum reach of robot arm experiment at the common data point (F(1,56)0.159,p0.691). It is reasonable to integrate and combine the data into a common data set. Therefore, the results of thereal and virtual trials can be compared.3.4 Comparing the results of virtual to real experimentIt can be shown that the perception of safe idle time in virtual is only slightly greater than that in real with regard tothe speed of robot (Figure 6). Moreover, there is a similar trend in the perception of idle time between virtual andreal in the robot idle time experiment.Witmer and Kline (1998) concluded that people generally underestimated distance to the objects in the virtualand real environments, but the errors in distance estimation tended to be greater in virtual than that in real.Figure 7 illustrates that there is a big difference in the perception of maximum reach of robot arm between thevirtual and real experiments in robot maximum reach of robot arm experiment, and the perception of maximumreach of robot arm in virtual is relatively higher than that in real. However, the trends in the perception of maximumreach of robot arm between the virtual and real are similar with regard to the type of robot. Our findings are similarto the results of Witmer and Kline (1998). Our subjects distance judgments were highly underestimated in thevirtual environment compared to those in the real environment.3.5 Tests of hypotheses3.5.1 Result of hypothesis 1The speed and size of the robot in the virtual group significantly affected the perception of safe idle time. Subjectswaited for significantly longer (p50.05) time when the robot operated at the faster speed (90cm/s). Robot type hasa significant effect on perceived idle time in the real experiment, without considering the robot size (p50.05). Thishypothesis is partially supported (Table 4). Gender also did not show any significant effect from accident exposure.3.5.2 Result of hypothesis 2The perception of maximum reach of robot arm is significantly influenced by the speed and type of robots in boththe virtual and real environments; it can give us some indication that perceived reach distance is dependent on themotion type. Subjects came closer to the robot work envelope in the low speed. In addition, size was consistentlyfound to be a significant factor in idle time and robot work envelope perceptions in virtual experiment (Table 5).The perceived distance of robot work envelope with simulated accident group is significantly higher than thatwithout simulated accident group (Table 5).Figure 7. Comparison of the results between virtual and real inrobot experiment II with regard to type of robot.Figure 6. Comparison of the results between virtual and realin robot experiment I with regard to speed of robot.International Journal of Production Research1713.6 Comparing our findings in the robot experiment to the Karwowskis resultsThe perception of idle time and maximum reach of robot arm in both the virtual and real environments werestudied. The comparison of our results to the previous studies will be discussed in this section. In fact, themethodologies of our robot experiments in this research are replicated from Karwowskis studies. There aresimilarities and differences between the results of Karwowskis study and our research in the real environment.Table 6 summarised the results of real trials in maximum reach of robot arm experiment with Karwowski et al.sfindings (1991).From Table 6, it appears that subjects perceived closer distances for maximum reach of robot arms at slowerrobot speed (25cm/s) than that at fast speed (90cm/s). With the exposure to a simulated accident in the virtual,subjects give more care to the robot motion in the real environment. Perception of maximum reach of robot arm forsimulated accident group is relatively higher than that for no-accident group. Furthermore, when the angle ofapproach is at 77.1 degrees, the Karwowski et al. (1991) found that the subject slightly underestimated the maximumreach of robot arm and intruded into work envelope of the robot. However, our findings showed that the subjects,on average, overestimated the true work envelope of the robot arm in the real environment consistently. Based onTable 6. Comparison of the results of real trials in robot experiment II with Karwowskis findings.Perception of maximum reach of robot armMeanFactorsLevelsKarwowskisresult(P50 robot)Ourfindings(K10S robot)CommentSpeed ofrobot25cm/s10.25cm2.05cmRobot speed (p50.05), accident exposure(p50.001), and angle of approach (p50.001)significantly influence the human perception ofmaximum reach of robot arm in Karwowskisstudy. Perceived work envelope of robot is signif-icantly influenced by accident exposure (F4.388,p0.045) and speed of robot (F20.419,p50.001) at the 5% level in our study.90cm/s11.57cm18.64cmAccidentexposureWithaccident21.9cm19.25cmWithoutaccident?0.09cm1.44cmAngle ofapproach77.1 degree?0.41cm10.34cmTable 5. Summary of maximum reach of robot experiments.For max. reach of robot armVirtualRealSpeed of robotSignificantSignificantSize of robotSignificantNon-significantType of robotSignificantSignificantAngle of approachNon-significantNon-significantGenderNon-significantNon-significantAccident exposureNon-significantSignificantTable 4. Summary of idle time experiments.For idle timeVirtualRealSpeed of robotSignificantSignificantSize of robotSignificantNon-significantType of robotNon-significantSignificantGenderNon-significantNon-significantAccident exposureNon-significantNon-significant172P.P.W. Ng et al.the ANOVA test from both studies, the perception of maximum reach of robot arm was significantly affected byrobot speed, accident exposure and angle of approach in Karwowskis study. Perceived maximum reach of robotarmissignificantlyinfluencedbyaccidentexposure(F(1,28)4.388,p0.045)andspeedofrobot(F(1,28)20.419, p50.001).The differences between Karwowskis experiment and our studies are discussed as follows:(1) Six angles of approaches are examined in Karwowskis experiment. However, due to the limitation of layout,one approached angle can only be used for real trials in maximum reach of robot arm experiment.(2) A 15 min preview session will be given to the subject before performing the experimental sessions inKarwowskis study, but in our study, a 2-min preview session was provided for the subject.(3) Differences in robot programming for palletising tasks between Karwowskis study and this maximum reachexperiment.(4) Subjects knowledge backgrounds are different in the two studies. Karwowski recruited 12 industrialworkers as subjects. However, 64 engineering students participated in our study.(5) Each experimental condition has two replications in Karwowskis study, but only one replication in thisexperiment for testing maximum reach in the real.These results can be helpful for measuring human perception and achieving improvements in HRI safety (Duffyand Salvendy 2000). HRI in industry can be considered as two types; (1) first type, human operator and robot workin a separated area and (2) second type, robot work more close to human operator instead of being isolated in arobot work envelope. In the second type of HRI, an industrial robot can cause more fears, surprise and discomforton operator and this mental stress can affect HRI safety and productivity negatively (Aria et al. 2010). Therefore, itis necessary to consider human perception of safety for the second type of HRI.Lessons learned from this study have potential transfer to other fields that have been more focused onmanufacturing. For instance, the Control, Instrumentation and Robotics area within Mechanical Engineering atMassachusetts Institute of Technology seeks to promote research and education into identifying fundamentalprinciples and methodologies that enable systems to exhibit intelligent, goal-oriented behaviour and to developinnovative instruments to monitor, manipulate and control systems (MIT 2010). With needs in services, includinghealthcare, security and education, some super muscle actuators have been developed by researchers at the NewmanLaboratory at the Massachusetts Institute of Technology that surpass biological muscle in terms of stress, energy,density, efficiency, response speed and degrees of freedom. It will be important to have further insights into HRI toget the best impact from these potential applications. Particularly in the context of human rehabilitation, thecognitive aspects of the human interaction will be important for designers to best enhance the individual capabilitieswith new robotic technologies (Krebs et al. 2003, Fasoli et al. 2004, Reiner et al. 2006, Volpe et al. 2009).4. ConclusionsThe impact of size, speed and type of robots and accident exposure on human perception of hazard and risk of robotmotion, safe waiting time of robots during system halts and improper pauses of robot operations and maximumreach of robot arms in virtual and real workplaces are investigated. Based upon the results obtained from theexperiment, the transferability of the experience and perception from virtual to real world can be estimated. Thecomparison of the analysed data in virtual and real environments shows that variables such as time are transferablefrom virtual to real, while perceived distance, only in desktop virtual environment, still shows significant differences.Since training can be effective on the recognition of hazard and risk (Duffy 2003, Duffy et al. 2004a), itis expected that the results of further testing can be used for industry training in virtual reality environment toreduce hazards in the workplaces. Industries can use VR to train the workers about robot safety. Also, sinceaccident exposure is found as significant on the perception of safety, accident examples can be provided in trainingprogrammes. By VR training programmes, workers can gain experience about robot motions and robot accidents. Itis also possible to integrate virtual models into digital human modelling (Duffy et al. 2003, Duffy 2007) and designanalysis (Duffy et al. 2004b) by developing prediction capabilities.In future research, additional data collected from robot experiments can be analysed to find out the relationshipbetween hazard perception, maximum reach of robot arms, idle times and personality. In this study, experimentswere performed with a student population who had a basic understanding of robot programming and operations.Future studies can be performed with experienced workers to generalise the results. In the late-1980s, robots usageInternational Journal of Production Research173area extended, and the robots have come to peoples daily lives from industry-like assistive robots which supportelderly or disabled people, surgical devices and toys (Goodrich and Schultz 2007, Laschi et al. 2007, Pervez and Ryu2008). Unlike most industrial robots, service robots can be in a close physical contact with humans. Therefore, it isnecessary to achieve a positive perception of safety in order to provide robots acceptance as partners and co-workers in human environments (Bartneck et al. 2009). In future research, perception of the safety of service robotscan be investigated related to the impact of parameters such as robot size, speed and type and exposure to a virtualaccident.AcknowledgementsThis study is sponsored, in part, by the Competitive Earmarked Research Grants (CERG) from the Research Grants Council(RGC) of Hong Kong HKUST/CERG 6168/98E and HKUST/CERG 6211/99E. The authors express their sincere appreciationto Calvin OR, Vivian LAU and Tracy CHEUNG, Gilbert Leung, Denil Chan, K.C. Tin and Colleen Duffy for their assistancethroughout the software development and manuscript preparation. The authors would also like to thank the Fulbright ScholarProgram administered by the Council for International Exchange of Scholars, for their support and for enabling this cooperationand dissemination of results.ReferencesAmerican National Standards Institute, 1986. American national standard for industrial robots and robot systems safetyrequirements. Washington, DC: American National Standards Institute.Aria, T., Kato, R., and Fujita, M., 2010. Assessment of operator stress induced by robot collaboration in assembly. CIRP Annals Manufacturing Technology, 59, 58.Bartneck, C., et al., 2009. Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, andperceived safety of robots. International Journal of Social Robotics, 1, 7181.Bicchi, A. and Tonietti, G., 2004. Fast and soft-arm tactics. IEEE Robotics and Automation Magazine, 11 (2), 2233.Bonney, M.C. and Yong, Y.F., 1985. Robot safety. Berlin: Springer-Verlag.Brocka, O. and Khatib, O., 2002. 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