Visual Spatial Abilities in Uninhabited Ground ... - MIT Press Journals

Lindsay O. Long* Joshua A. Gomer Jessica T. Wong Christopher C. Pagano Department of Psychology Clemson University Clemson, SC 29634

Visual Spatial Abilities in Uninhabited Ground Vehicle Task Performance During Teleoperation and Direct Line of Sight

Abstract Two experiments investigated the role of spatial abilities on uninhabited ground vehicle (UGV) performance under two different viewing conditions: direct line of sight and teleoperation. The ability to operate a mobile robot was indexed by task completion time and total number of course collisions. Results showed that participants with higher spatial abilities exhibited superior performance in both direct line of sight and teleoperation. Performance under direct line of sight was correlated with both spatial relations and spatial visualization, whereas performance during teleoperation was only correlated with spatial relations ability. Understanding the roles of spatial abilities under different viewing conditions will aid in the advancement of selection criteria and training paradigms for robot operators.

1

Introduction

The use of uninhabited ground vehicles (UGVs) has grown in recent decades, especially in military applications and urban search and rescue. UGVs provide operators with the ability to perform various functions from a distance, including those related to payload delivery and retrieval, target identification, search and rescue, and survey and monitoring activities (Messina & Jacoff, 2006). In many cases the operator must rely solely on a video feed from the robot. As a result of the physical separation between the operator and the robot, operators are presented with limited perceptual information about the distal environment. Consequently, task success can depend heavily on the cognitive abilities and motor skills of the operator. In particular, spatial abilities have been shown to be potential predictors of teleoperation success (Chen & Terrence, 2008; Sekmen, Wilkes, Goldman, & Zein-Sabatto, 2003; Lathan & Tracey, 2002). Spatial abilities are broadly associated with the cognition involved in comprehending 3D forms, understanding positions in space, and transforming positions relative to objects and differing points of view. These abilities are an integral component of UGV operation, and may be moderated by different operational environments. Remote operators must be able to perceive their environment, understand the position of the UGV with respect to the environment, and be able to predict how to manipulate the UGV and target objects in order to achieve their mission. Presence, Vol. 20, No. 5, October 2011, 466–479 ª 2012 by the Massachusetts Institute of Technology

466 PRESENCE: VOLUME 20, NUMBER 5

*Correspondence to [email protected].

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Controlling a UGV typically involves one of two viewing conditions: direct line of sight (DLS), where the operator is in proximity to the device and controls the device while looking at it, or teleoperation (TO), where an operator is unable to see the device and must control it through a video feed. Because of the physical separation between the device and the operator, remotely operating a robot in either viewing condition is cognitively demanding and requires multiple perceptual and attentional processes. Nevertheless, in contrast to DLS, TO has particularly challenging characteristics as a result of degraded visual stimuli from the camera feed (e.g., Fong & Thorpe, 2001). The very nature of TO provides significantly less sensory and depth information to operators of robotic systems because a 3D environment is displayed on a 2D screen with a limited field of view, which is often referred to as the remote perception problem (Tittle, Roesler, & Woods, 2002; Woods, Tittle, Feil, & Roesler, 2004). This problem includes a lack of binocular vision and other depth information, a destruction of the natural coupling between head and body motions and the resulting optic transformations, as well as a visual mismatch between the remote camera height and the teleoperator’s natural eye height (Gomer, Dash, Moore, & Pagano, 2009; Tittle et al.). This degraded sensory information causes difficulties in accurately perceiving both the teleoperated robot and the remote environment (e.g., Casper & Murphy, 2003; Moore, Gomer, Pagano, & Moore, 2009; Murphy, 2004). While many technological advances in robotics have helped to improve visual perception, another research approach has focused on examining the cognitive and perceptual capabilities of operators. While there are many cognitive and perceptual-motor abilities that are necessary to operating robotic platforms, one domain that has generated recent attention is the operators’ spatial abilities. Carroll (1993) broadly classified visual perception and spatial abilities into five separate aptitudes that allow one to mentally comprehend, organize, and manipulate visual information. Others have broken down visual spatial abilities into the two subcomponents of spatial visualization and spatial relations (e.g., McGee, 1979), with research suggesting that ‘‘general spatial ability can be thought of as being composed of two primary subfactors: spatial visualization and spatial orien-

tation’’ (Pak, Rogers, & Fisk, 2006, p. 154). Spatial visualization, as defined by Ekstrom, French, Harman, and Dermen (1976), includes the capacity to mentally manipulate and arrange visual images and patterns. Spatial relations, also known as spatial orientation ability, consist of perceiving the organizational relationships and positions of different objects in space. Spatial abilities play a key role in the early stages of perceptual-motor task learning (Fleishman, 1972) and there is a strong relationship between cognitive ability and performance in conditions with reduced depth information (Ackerman, 1987). Teleoperation studies have also begun to suggest that there is a relationship between higher spatial abilities and superior robot operation (Lathan & Tracey, 2002), target detection performance (Chen, Durlach, Sloan, & Bowens, 2008), and gunnery tasks (Chen & Terrence, 2008). Sekmen et al. (2003), for example, found that TO performance could be predicted by mental rotation ability. Teleoperation often involves an ego-centered frame of reference that corresponds to the forward view of a camera mounted on the robot. This can be contrasted with a world-centered reference frame corresponding to an environmental layout that is independent of the robot’s orientation, similar to a traditional north-up map display (e.g., Aretz, 1991). DLS corresponds more closely to a world-centered reference frame, because the operator’s view of the environment is not altered by changes in robot orientation. During DLS, mental rotation may be required to align the ego-centered reference frame of the robot with the world-centered reference frame of the operator and environment (see Aretz; Hooper & Coury, 1994). For example, if the robot is moving directly toward the operator, the robot will have to be steered to the left in order to turn the robot to the operator’s right (i.e., the robot’s left). Research has shown that operators of flight simulators use mental rotation to bring into alignment a world-centered north-up map display and their egocentric reference frame, and that this strategy is augmented by a mental reversal when the map is rotated 1808 away from the egocentric frame of reference (e.g., Aretz; Gugerty & Rodes, 2007; Wickens & Prevett, 1995). It is important to note that in the case of DLS, the term ‘‘egocentric’’ is with reference to the robot, not the operator. Thus the viewpoint offered by a forward


pointing camera during TO provides an egocentric reference frame that supports local guidance through the immediate environment, while the view of the surrounding environment under DLS may better support global awareness (Wickens & Prevett). It follows that TO performance may be improved by ‘‘over the shoulder’’ tethered views of the robot and its surrounding environment (Ruddle, Savage, & Jones, 2003; Wickens & Prevett), though such views may be difficult to implement in urban search and rescue and military applications. Although related, spatial visualization and spatial relations are considered to be distinct constructs (Carroll, 1993; Pak et al., 2006). Further, since research has shown a relationship between spatial abilities and teleoperation performance, it may be the case that one of these abilities plays a greater role in UGV operation than the other. In the human–computer interaction (HCI) domain, Pak et al. assessed the relationship between visual spatial abilities and computer navigation search performance. They found a relationship between performance and spatial relations ability when navigation demands were high, but spatial visualization ability was not significantly related to any computer-based task performance measure in their study. In a similar fashion, robot operation may demand more of one particular type of spatial ability, especially in certain viewing situations. The present studies further examined the relationships between two types of visual spatial abilities and performance under both DLS and TO conditions. It is hypothesized that participants with higher spatial abilities will be expected to operate a UGV through a course in less time and with fewer collisions, though it is unknown which ability will contribute more to performance under each of the separate viewing conditions. 2

lead to better training paradigms and selection criteria for robot operators that are expected to function in either or both viewing situations. Sekmen et al. (2003) combined TO and DLS viewing environments into an intermediate category, but it is of interest to understand which context places more visual-spatial demands on the operator. Therefore, the purpose of the current study was to investigate the relationship between spatial abilities and robot operation under both DLS and TO. It was hypothesized that participants with higher spatial abilities would have superior performance in both DLS and TO tasks when compared to individuals with lower spatial abilities. Two performance measures were employed: time to complete a course, and the number of collisions while completing the course. It is important to note that the purpose of the experiment was to investigate a possible relationship between these performance measures and spatial abilities and to test for this relationship under the viewing conditions of DLS and TO. The experiment was not designed to determine whether participants performed better in one viewing condition or another, but rather to ascertain whether or not performance is related to spatial abilities in either or both of these viewing conditions.

2.1 Method 2.1.1 Participants. Thirty-one Clemson University students participated in Experiment 1 after providing informed consent (11 males, 20 females; age, M ¼ 21, SD ¼ 2.0). Participants received either course credit or $10 for their participation. All participants were tested for a minimum high contrast visual acuity of 20/40 measured binocularly from 6 m, and they self-reported full use of their neck, arms, and hands.

Experiment 1

Previous research suggests that a positive relationship exists between visual spatial ability and robot operation, and it was of interest to evaluate its contribution to performance under different viewing conditions. TO has perceptual challenges that are inherently different from those in DLS, and a better understanding of how performance relates to individual spatial abilities may

2.1.2 Materials and Apparatus. 2.1.2.1 Visual Spatial Ability Measures. Spatial visualization was assessed using the Paper Folding Test, and spatial relations ability was assessed using the Cube Comparison Test (e.g., Chen & Terrence, 2008; Chen et al., 2008; Lathan & Tracey, 2002; Pak et al., 2006). The Paper Folding Test is composed of 20 items, each consisting of images depicting a folded piece of paper (Ekstrom et al.,

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Figure 1. Side view of the camera-mounted robot (left) and the remote controller used to operate the robot (right) in Experiment 1.

Figure 2. The lower difficulty course (left) and the higher difficulty course (right) used in Experiment 1.

1976). The final image of the folded sheet of paper includes a circle which represents a punched hole through the folded paper. Participants were to choose which of five images correctly displayed the unfolded, hole-punched paper. The Paper Folding Test has a testretest reliability of .84. The Cube Comparison Test consists of 42 items, each composed of a pair of cubes (Ekstrom et al.). Using the information provided on the cubes’ three visible sides, participants decided whether the two cubes were the same or different. It also has a test-retest reliability of .84. Both visual spatial measures were scored according to the Ekstrom et al. recommended methods. Scores from each ability measure were standardized into Z-scores for analysis.

CMOS USB transmitter and receiver that displayed live camera feed on a 38.1 cm Dell LCD monitor. The resulting image appeared as a 320 240 pixel array in a 8.9 cm 6.4 cm window in the center of the computer screen. The camera was mounted 21 cm above the floor and was oriented to approximately 108 below the horizontal. Under both DLS and TO, participants navigated the robot through a high and low difficulty course (see Figure 2). Both courses consisted of a series of wood pieces (10.2 cm 10. 2 cm 61 cm) and cones. The higher difficulty courses included additional wood pieces and cones, reducing the amount of space in which to navigate. For each viewing condition, half of the participants started on the straight portion of the course on the left side and then returned via the curved portion on the right side. The remaining participants moved through the course in the opposite direction. During TO, the participants also navigated the robot into the alcove at the far end of the course, and they were asked to identify any object that they saw in the alcove. The alcove was ignored during DLS.

2.1.2.2 Tasks and Measures. The ability to operate a mobile robot was assessed by four performance tasks: two under DLS and two in a remote location using camera feed from the robot, under TO. The robot was a 1:6 scale radio-controlled H2 Hummer with its outer body removed (New Bright, Wixon, MI, 24.5 cm 28 cm 64 cm; see Figure 1). A remote control input device consisting of two joysticks (one forward and back, one left and right) was used to control the movements of the robot (see Figure 1). During DLS, participants viewed the robot and the entire course (see Figure 2). During TO, the participants’ view of the course was occluded and they relied solely on a live camera feed from a camera mounted on the robot (see Figure 1). The course was the same under both viewing conditions. The camera used was a Grandtec USA (Dallas, TX) wireless ‘‘Eye See All’’ wireless security camera system. The camera system used an RF

2.1.3 Procedure. Participants first completed the two spatial ability measures. Following this, they were trained on the use of the robot until they reached a minimum standard of performance. In other words, they were instructed on how to operate the robot, were allowed to practice for 1 min, and then they were asked to perform basic robot maneuvers. If they successfully operated the robot, then they began the experiment; otherwise, they were given another minute and this was repeated until they were trained to standard. Because the


Table 1. Measures of Performance for the Four Conditions of Experiment 1 (n ¼ 31) Course completion time (s)

Collision score

Condition

Mean

SD

Mean

SD

DLS low complexity DLS high complexity TO low complexity TO high complexity

96.2 125.1 89.6 107.7

70.2 76.0 48.4 51.8

18.6 22.5 14.5 16.0

13.9 15.6 8.8 11.1

goal of training was to familiarize participants with operating the device, it was conducted prior to beginning the experimental tasks. All participants indicated they were comfortable with the robot within 5 min. Following training, participants completed the four performance tasks. All participants completed the DLS first and TO second, and within each of these they completed the lower difficulty course followed by the higher difficulty course. This order was chosen to mimic common UGV teleoperation training paradigms in which operators operate the device while viewing their performance directly and are then trained to control the device from a remote location. Performance was assessed by course completion time and collisions. Course completion time was recorded in seconds, while collisions were assessed according to three levels of severity. A collision was regarded as minor if it did not alter the course, moderate if it did alter the course (i.e., the robot moved one of the wood blocks or cones), and major if the collision required assistance from the experimenter (i.e., the robot got stuck in a part of the course and needed to be moved). Collisions were reported as a single overall composite score by summing the severity scores of 1, 2, or 3 for minor, moderate, or major collisions, respectively. Under TO, participants were seated in front of the computer in a chair that was positioned at a fixed 30.5 cm distance from the desk. In order to complete the task and make judgments about the remote environment, participants could only rely on the video feed. Participants viewed a brief training video that demonstrated what it would look like to operate the robot while viewing the computer screen. To reduce practice effects associated with the specific course, for each participant the

Table 2. Correlation Coefficients for Each Performance Measure and Viewing Condition for Each of the Spatial Abilities Measures in Experiment 1

DLS Time Collisions TO Time Collisions

Paper folding test (spatial visualization)

Cube comparison test (spatial relations)

–0.38* –0.41*

–0.50** –0.45*

–0.12 –0.21

–0.52** –0.47**

NOTE: n ¼ 31; *p < .05; **p < .01.

starting position of the robot was opposite from the starting position he or she received during DLS. 2.2 Results and Discussion Means and standard deviations for each measure of performance under each of the viewing conditions are given in Table 1. Overall, superior performance was associated with faster course completion times and fewer collisions. Correlation coefficients were computed to assess relationships between visual spatial ability and performance under both viewing conditions (see Table 2 and Figure 3). Under DLS, superior visual spatial ability was negatively correlated with faster course completion times and fewer collisions. More specifically, spatial relations showed a higher correlation with time than did spatial visualization. Under TO, superior spatial relation scores

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Figure 3. Aggregate collision scores and aggregate time for both courses predicted by Paper Folding Test scores (top) and Standardized Cube Comparison scores (bottom) for direct line of sight (DLS) and teleoperation (TO).

were also correlated with faster course completion times and fewer collisions. However, spatial visualization scores were not significantly correlated with either course completion times or the number of collisions under TO.

The results of the first study reinforce that overall, higher spatial abilities are a predictor of superior performance when operating a wheeled robot through an obstacle course. However, while both spatial visualization and spatial relations were related to performance


under conditions of DLS, only spatial relations was related to performance under conditions of TO. It may be the case that the egocentric view under TO conditions reduced the need of operators to rely upon additional mental manipulation that is required under conditions of DLS (e.g., accounting for changes in the orientation of the robot when it conflicts with the orientation of the operator). 3

Experiment 2

The first experiment showed that the ability to successfully navigate a robot, as indexed by completion time and number of errors, was related to the participant’s visual spatial abilities (see also Chen & Terrence, 2008; Park, 1998; Sekmen et al., 2003; Lathan & Tracey, 2002). Participants with higher visual spatial abilities demonstrated superior performance under both DLS and TO tasks. Further, both spatial relations and spatial visualization ability were associated with performance in DLS, while only spatial relations ability was related to performance in TO. The purpose of the second study was to further investigate the connection between visual spatial skills and robot operation while incorporating methodological changes that replicate and extend the findings of Experiment 1. The robot, the course, and the trial order were modified. In Experiment 2, a track-based vehicle was employed which emulated commercially available platforms such as the Foster-Miller TALON, the iRobot PackBot, and the Robotics Design ANATROLLER ARI-50. This robot was also controlled with a different interface than that used in Experiment 1 (see Figure 4). While the course in Experiment 1 was composed of straight, wide passageways and few turns, the course used in Experiment 2 was designed to be more challenging with the addition of more turns, slimmer passageways, diagonal paths, and smaller turning radii. The alcove and object identification task were dropped from the course and the task was identical under both viewing conditions. The order of the experimental conditions was varied to minimize practice effects. Unlike Experiment 1, half of the participants received the Paper Folding Test first and the remainder received the Cube

Figure 4. The modified RC tank and controller used in Experiment 2.

Comparison Test first in an effort to control for any fatigue effects. Half of the participants performed the DLS condition first and the remainder performed TO first. Half of the participants operated the robot in the low complexity course first in each of the viewing conditions and the remainder operated in the high complexity course first. Lastly, to test for a possible relationship between spatial abilities and mental workload, the NASA Task Load Index (NASA-TLX) workload questionnaire was administered to the participants. It is possible that participants with lower spatial abilities may experience greater workload when operating the robot. Workload also serves as a manipulation check for course difficulty level. Similar to Experiment 1, the second experiment was not designed to compare differences in performance between DLS and TO. Rather, the purpose was to determine whether or not performance is related to spatial abilities in either or both of these viewing conditions.

3.1 Method 3.1.1 Participants. Thirty-two Clemson University students participated after providing informed consent (12 males, 20 females; age, M ¼ 22, SD ¼ 4.1). Each participant tested at a high contrast visual acuity of at least 20/40, and reported full use of their neck, arms, and hands. Three participants were unable to complete the task due to equipment failures.

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Table 3. Correlations for Each Performance Measure and Viewing Condition for Each of the Spatial Abilities Measures in Experiment 2

Figure 5. The lower complexity course (left) and the higher complexity course (right) used in Experiment 2.

3.1.2 Materials and Procedure. All methods and procedures were the same as those employed in Experiment 1, except as described below. The robot was a radio-controlled 1:30 scale model Sherman tank (Heng Long Plastic Toys Co., Guangdong, China), measuring 22.9 cm 10.2 cm 10.2 cm. It was a small robot with simplistic control features and a turning radius of approximately 0.3 m2. The robot had slower speed than the robot used in Experiment 1, and utilized track-based locomotion instead of fourwheeled locomotion. The turret of the vehicle was removed to provide a platform for the mounting of the remote camera (see Figure 4). The controller had two controls that were used by participants to move the tank forward and back, and left and right. The course was designed to be more challenging than the one employed in Experiment 1. For both low and high complexity conditions, the width of the course walls decreased and more turns were included. The low complexity course consisted of seven turns and eight straight pathways, within a 3.05 m 1.22 m rectangle (see Figure 5). All participants began with the robot behind the white line shown in the lower portion of Figure 5 and they ended by crossing the white line at the top (farther) portion of Figure 5. The width of corridor space was a minimum of 25.4 cm, or slightly more than twice the width of the tank, and never exceeded 58.0 cm. In the high complexity course, the corridor and turning space were restricted by the addition of wooden

DLS Time Collisions TO Time Collisions

Paper folding test (spatial visualization)

Cube comparison test (spatial relations)

–0.43* –0.53**

–0.39* –0.54**

–0.33 –0.31

–0.41* –0.44*

NOTE: n ¼ 29; *p < .05; **p < .01

boards and cones. Each of the seven turns was reduced from approximately 50.8 to 15.2 cm, or 150% of the width of the robot. The addition of nine cone obstacles (13.3 cm base diameter) placed throughout the course further restricted space and increased necessary turns in the course. Of the 29 participants who completed the experiment, 15 performed DLS before TO. Within this half, seven participants completed the lower complexity course before the high complexity course and eight completed the high complexity before the low complexity. Fourteen participants performed TO before DLS. Eight completed the low complexity before the high complexity course, and six completed the high complexity before the low. Workload was measured using the NASA-TLX (Hart & Staveland, 1988). The NASA-TLX was administered immediately following the operational training session and after each experimental trial through the course.

3.2 Results As in Experiment 1, superior performance was defined by faster course completion times and fewer collisions. Table 3 and Figure 6 present Pearson correlations for each performance measure under each viewing condition and for the two visual spatial ability measures.


Figure 6. Aggregate collision scores and aggregate time for both courses predicted by Paper Folding Test scores (top) and Standardized Cube Comparison scores (bottom) for direct line of sight (DLS) and teleoperation (TO).

Under DLS, superior spatial visualization ability and spatial relations ability were related to faster course completion times and fewer collisions. Under TO conditions, superior spatial relations scores were correlated with faster course completion time and fewer collisions. Once

again, spatial visualization scores were not significantly correlated with course completion times or collisions. Mean course completion times and collisions scores for the four experimental conditions are listed in Table 4, along with standard deviations. To examine interac-

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Table 4. Measures of Performance for the Four Conditions of Experiment 2 (n ¼ 29) Course completion time (s)

Collision score

Condition

Mean

SD

Mean

SD

DLS low complexity DLS high complexity TO low complexity TO high complexity

103.3 188.7 107.1 170.7

51.6 22.2 10.1 15.4

6.6 27.4 8.7 24.1

6.8 22.7 8.0 15.6

tion effects between viewing condition and difficulty level, a separate 2 2 ANOVA was conducted for each of the performance measures. There was no main effect of viewing condition on course completion time, F(1, 124) ¼ 0.22, p > .05, though there was a significant effect of course difficulty, F(1, 124) ¼ 24.24, p < .001. There was no significant interaction, F(1, 124) ¼ 0.52, p > .05. Thus participants took significantly longer to complete the high difficulty courses, but time did not differ between DLS and TO. Regarding collision scores, there was again no main effect of viewing condition, F(1, 124) ¼ 0.06, p > .05, but a significant effect of course difficulty, F(1, 124) ¼ 48.50, p < .001, with no significant interaction, F(1, 124) ¼1.04, p > .05. Thus, collision scores were higher under the more difficult conditions, though they did not differ between DLS and TO. Problems during data collection resulted in complete workload scores for 26 of the 29 participants who completed the experiment. Mean ratings for total workload were significantly higher in the high complexity course than in the low complexity course for both DLS, t(25) ¼ –5.65, p < .001, and TO, t(25) ¼ –4.69, p < .001. Thus, the higher complexity course was perceived as more challenging than the lower complexity course in each viewing condition (see Table 5). There was no difference in mean ratings of NASA-TLX scores between DLS and TO for either low complexity condition, t(25) ¼ 1.88, p > .05, or high complexity condition, t(25) ¼ –0.06, p > .05, indicating that participants did not perceive one viewing condition to be more challenging than the other for either level of course complexity. Most importantly, there were no significant correlations

Table 5. Mental Workload (NASA-TLX) Scores by Viewing Condition and Course Complexity for Experiment 2

DLS High complexity Low complexity TO High complexity Low complexity

Mean

SD

62.5 49.0

14.9 16.0

63.7 55.0

15.4 15.6

between workload scores and either spatial visualization (r ¼ 0.06, p > .05) or spatial relations (r ¼ –0.15, p > .05), indicating that subjective workload was not related to spatial ability. 4 General Discussion The purpose of this work was to investigate the role of visual spatial abilities on UGV performance under DLS and TO viewing conditions. The results from these studies reinforce previous findings that higher spatial abilities are associated with superior robot operation performance when operating wheeled and tracked robots (Chen & Terrence, 2008; Lathan & Tracey, 2002; Sekmen et al., 2003). Performance under DLS was related to both spatial relations and spatial visualization, while performance under TO was related only to spatial relations ability. One reason for this discrepancy may be the difference in egocentric versus exocentric views that the operator had to utilize under the two experimental conditions. It is


possible that less spatial visualization is required with egocentric views similar to the present TO condition. Spatial visualization is associated with preserving spatial patterns and manipulating them into different arrangements. The largest difference between the egocentric views of TO and the exocentric views of DLS was that the DLS conditions required additional transformation to match the perspective of the operator with the perspective of the robot moving through the course. It may be the case that this perspective matching under DLS tapped into the construct of spatial visualization, while TO provided participants with a continuous first-person perspective that required fewer spatial transformations. The TO conditions were essentially a first-person driving task involving an egocentric view that corresponded to the forward view of a camera mounted on the robot. The DLS conditions, in contrast, involved an exocentric (world-centered) view of the platform that had to constantly be compared to the orientation of the operator in order to properly operate the input device (see Aretz, 1991; Hooper & Coury, 1994). Thus it is somewhat intuitive that the present DLS conditions relied on both spatial abilities. Even though DLS was not subjectively perceived as more challenging than TO, DLS seemed to require additional visualization ability. For instance, when controlling the robot toward themselves, the operator was forced to make mirrorimage judgments, because turning the controller to the right would make the device go to the operator’s left, and vice versa. This spatial translation may have relied on the participants’ visualization ability (e.g., Aretz, 1991; Gugerty & Rodes, 2007; Hooper & Coury, 1994; Wickens & Prevett, 1995), thereby providing a possible explanation for the relationship with both spatial visualization and relations ability. This may also have accounted for the observed higher completion times and more collisions under DLS conditions. The present studies utilized a single camera view for the TO condition. However, multiple camera views (i.e., side and rear views) may rely more or less on different types of spatial abilities. Additional camera views may also compensate for performance decrements that occur as a result of lower spatial abilities. Teleoperation has also been implemented with additional decoupled camera

feeds, such as cameras located on a UGV that can be controlled independently and are not related to vehicle navigation (Hughes, Manojlovich, Lewis, & Gennari, 2003). Similarly, TO performance may be improved by over the shoulder tethered views of the robot and its surrounding environment (see Ruddle et al. 2003; Wickens & Prevett, 1995). DLS performance may be improved by the addition of a display from a camera mounted on the robot. During many current robot applications, the operators have such a TO camera view that is also available during DLS, creating a hybrid between the DLS and TO conditions that were investigated presently. The utility of such a scheme, as well as an analysis of optimum strategies for switching between the two views when DLS is available, should be a topic of future study. Last, many modern teleoperation interfaces present telemetry displays regarding the distal environment, such as UGV orientation relative to elements in the remote environment. Future studies are needed to investigate the roles of spatial abilities when attending to and interpreting a telemetry display in the presence of a camera feed. Identifying the spatial abilities that are relied upon during UGV performance in different viewing situations will aid in the advancement of both training paradigms and selection criteria for robot operators. Spatial ability can be enhanced through training and experience (Brinkmann, 1966; Jaeggi, Buschkuehl, Jonides, & Perrig, 2008; Kass, Ahlers, & Dugger, 1998; Lunneborg, 1984), and understanding which types of abilities are more important for different teleoperation tasks may aid in the creation of more effective training protocols. Spatial abilities also play a role in the facilitation of learning a motor task in a simulated environment and then during the transfer of that knowledge to a real-world task (Tracey & Lathan, 2001). Therefore, understanding the spatial abilities of operators may be important when using simulators for the training of complex motor tasks. However, some aspects of spatial abilities appear to be influenced by genetics (Kelley, 1928; Plomin & Craig, 1997), and determining the relative contributions of each type of ability on performance may assist in the development of teleoperator selection criteria. The present findings support the hypothesis that operator selection

Long et al. 477

can be based in part on visual spatial ability (Chappelle, Novy, Randall, & McDonald, 2010; Chen et al., 2008; Chen & Terrence, 2008). An understanding of the cognitive processes underlying efficient teleoperation could also improve humancentered design of UGV navigation displays. Bowen (2004) found that individual differences in spatial visualization ability affected the type of information utilized from a computer. Interface displays that presented simplified information resulted in higher information acquisition by participants with lower spatial abilities. In contrast, displays that presented more complicated information resulted in increased information acquisition by participants with higher spatial abilities. This suggests that more complex interfaces may be better utilized by individuals with higher spatial abilities, and may be more difficult for individuals with lower spatial abilities. In teleoperation, knowledge of the types and degree of spatial abilities necessary for efficient performance could be used in the future design of UGV displays. These two experiments highlight the roles that spatial visualization and spatial relations have on performance under two different viewing conditions. Future studies should implement a greater battery of spatial abilities measures in order to better determine which abilities play a greater role in performance (Alderton, Wolfe, & Larson, 1997; Pak et al., 2006). Several researchers, for example, have drawn a distinction between spatial relations and spatial orientation, with the latter being the ability to imagine different perspective views of the surrounding environment relative to oneself (Kozhevnikov, Motes, Rasch, & Blajenkova, 2006; Lohman, 1988). Future work should explore the possible differences between these abilities during UGV operation. The present experiments demonstrated that different visual spatial abilities may have more influence on efficient performance under different viewing situations. This may also be true in situations with different types of visual degradation, situations with multiple camera views, and situations using telemetry displays, among others. A majority of participants in Experiments 1 and 2 were female. Past work has shown superior spatial abilities in male participants, particularly for mental rotation tasks, though results have been contradictory for spatial abilities in

general (e.g., Macoby & Jacklin, 1974; Linn & Petersen, 1985; Voyer, Voyer, & Bryden, 1995). Future work should examine gender differences in the relation between spatial abilities and operational performance under different viewing conditions. Both experiments demonstrated that subjects who produced more collisions tended to have longer course completion times. It is likely that collisions resulted in longer completion times, since collisions would slow one down, but it is also the case that differences in spatial ability would contribute to both of these measures. While both performance measures are common in the literature, this design did not allow the present study to distinguish what portion of the correlation between the two dependent variables was due to one causing the other and what portion was due to the third variable of operator skill causing both of them. Future studies should explore this relationship. UGVs are increasingly being used in place of humans in potentially dangerous and otherwise inaccessible environments. Even though technological advancements have furthered the autonomous abilities of UGVs, there will always be a need for some degree of human supervisory control. As UGVs find a wider range of applications, a better understanding of the perceptual abilities of their human operators can be used to improve human–robot interactions in both TO and DLS. References Ackerman, P. L. (1987). Individual differences in skill learning: An integration of psychometrics and information processing perspectives. Psychological Bulletin, 102, 3–27. Alderton, D. L., Wolfe, J. H., & Larson, G. E. (1997). The ECAT battery. Military Psychology, 9, 5–37. Aretz, A. (1991). The design of electronic map displays. Human Factors, 33, 85–101. Bowen, S. A. (2004). The effects of spatial ability on performance with ecological interfaces: Mental models and knowledge-based behaviors. Dissertation Abstracts International, 65(06), 3204 (UMI No. AAT 3134674). Brinkmann, E. H. (1966). Programmed instruction as a technique for improving spatial visualization. Journal of Applied Psychology, 50, 179–184.


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