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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 (www.euron.org), 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: duffypurdue.eduISSN 00207543 print/ISSN 1366588X online? 2012 Taylor & Francishttp:/dx.doi.org/10.1080/00207543.2011.571452http:/lines: 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