Investigation of Motion Guidance With Scooter Cobot and Collaborative Learning

IEEE TRANSACTIONS ON ROBOTICS, VOL. 23, NO. 2, APRIL 2007 245

Investigation of Motion Guidance With ScooterCobot and Collaborative Learning

Eng Seng Boy, Etienne Burdet, Member, IEEE, Chee Leong Teo, and James Edward Colgate, Member, IEEE

Abstract—This paper investigates how collaborative robots(cobots) can assist a human by mechanically constraining motionto software-defined guide paths, and introduces simple and effi-cient tools to design ergonomic paths. Analysis of the movementsof seven subjects with the Scooter cobot reveals significant dif-ferences between guided movements (GM) and free movements(FM). While FM requires learning for each novel task, movementsin GM are satisfying from the first trial, require little effort, arefaster, smoother, and with fewer back and forth corrections thanin FM. Operators rely on path guidance to rotate the Scooterand direct it along curved trajectories. While these advantagesdemonstrate the strength of the cobot concept, they do not showhow guide paths should be defined. We introduce tools to enablethe cobot and its operator to collaboratively learn ergonomic guidepaths and adapt to changes in the environment. By relying onthe haptic sensing, vision, and planning capabilities of the humanoperator, we can avoid equipping the cobot with complex sensorprocessing. Experiments with human subjects demonstrate theefficiency and complementarity of these guide paths design tools.

Index Terms—Assistive devices, effort, ergonomics, haptics,human–machine interaction, motion guidance.

I. INTRODUCTION

TO PLACE A windowpane or a car door in its frame is ahard operation, requiring simultaneous control of six de-

grees of freedom (DOF). If the pane were guided in orientationand position by guideways, this operation could be performedby simply pushing it. Following a similar idea, collaborativerobots or Cobots [1] are robotic devices conceived to providemechanical guidance along software-defined paths or surfaces.Cobots are passive in that they do not generate motion, but onlysteer their wheels to direct motion [2]. Forces perpendicular tothe headings of the wheels are balanced by frictional forces, sothat the motion is constrained to the direction of the headings.The passive nature of cobots means that, unlike conventionalrobots, cobots are safe to work side-by-side with humans.

Manuscript received February 11, 2006; revised July 21, 2006. This paperwas recommended for publication by Associate Editor W. F. Chung and EditorH. Arai upon evaluation of the reviewers’ comments. This work was supportedby the National University of Singapore. This paper was presented in part atthe International Mechanical Engineering Congress and Exposition, New Or-leans, LA, 2002, in part at the International Conference on Intelligent Robotsand Systems, Lausanne, Switzerland, 2002, in part at IEEEVR’03, and in partat Eurohaptics’03.

E. S. Boy is with the Victoria Junior College, Singapore 449035 (e-mail: [email protected]).

E. Burdet is with the Department of Bioengineering, Imperial CollegeLondon, SW7 2AZ London, U.K. (e-mail: [email protected]).

C. L. Teo is with the Department of Mechanical Engineering, National Uni-versity of Singapore, Singapore 119260 (e-mail: [email protected]).

J. E. Colgate is with the Department of Mechanical Engineering, NorthwesternUniversity, Evanston IL 60208-3111 USA (e-mail: [email protected]).

Color version of Fig. 1 is available online at http://ieee.ieeexplore.org.Digital Object Identifier 10.1109/TRO.2006.889488

Fig. 1. Experiments were carried out with the Scooter cobot (a), in which sub-jects performed a pin-in-hole task in given environments with styrofoam obsta-cles (b).

Cobot kinematics, design, and control have been investigatedin [1]–[3], and several planar and spatial cobots have been real-ized for the automotive industry [1]. Motion guidance may alsobe used to assist human actions in other fields. In surgery, forexample [4], it could facilitate manipulation and keep a scalpelout of dangerous areas. It has been shown to increase accuracyin micromanipulation [5]. Motion guidance may facilitate phys-ical rehabilitation [6], be used for robotic white canes [7], [8],and help maneuver a wheelchair [9]–[12]. Further, motion-guid-ance systems form the cornerstone of lane-keeping systems forcars and trucks [13], [14], and a recently developed omnidirec-tional power-assisted cart [15] has similar functionality as theScooter cobot of Fig. 1.

Among these systems, cobots are distinct as they are passiveand based on direct physical interaction with the human oper-ator. Path guidance provided by cobots may improve object han-dling performance and reduce injuries. This paper makes a step

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246 IEEE TRANSACTIONS ON ROBOTICS, VOL. 23, NO. 2, APRIL 2007

Fig. 2. The operator moves the Scooter by applying force and torque on thehandle. The transverse force redirects motion in the (x; y)-plane (a), the parallelforce accelerates it linearly, and the torque lets it rotate along the (x; y)-path.(b) The path controller computes a reference pointR (s ) on the desired path,as well as tangent vector T(s) and curvature vector �N(p) to asymptoticallyconverge to the path.

toward testing this, by investigating how guide paths assist mo-tion and how ergonomic guide paths can be designed.

To illustrate the cobot concept and its motion modes, webriefly describe the equipment used in the work reported here.The Scooter cobot Fig. 1(a) is a triangular vehicle moving on aplane, with a steerable wheel at each corner. These three wheelsenable controlling the Scooter’s position and orientation duringmovement. The force and torque exerted by the operator on thecobot are measured by a force-torque sensor mounted on thehandle, and used to infer how the operator wants to maneuver.The configuration space of the Scooter is 3-D:

. The steering wheels can confine motion to a guidepath or to a 2-D or 3-D subspace.

In free movements (FM), the wheels are specified to turnlike casters and to align with the force/torque exerted bythe operator. The resulting dynamics are similar to those ofa rigid body moving on a plane. The normal force to thepath in the -plane (relative to the scalar product

) curves this path, the tangen-tial force accelerates it along the path, and the torque rotates it[Fig. 2(a)]. In guided movements (GM), the three wheels aresteered by the path controller in order to follow a desired guidepath in configuration space.

Ergonomics studies have shown the impact of push/pull tasksrequiring spinal rotation on low back pain (LBP) [16]–[18], andbody posture plays an important role in the force capabilityboth in pushing and in the etiology of push/pull LBP injuries[19]. An interesting feature of the Scooter cobot is that a guidepath can modify the orientation of the pushed object withoutrequiring the operator to apply torque. Producing rotation byjust exerting force, the operator avoids torsion of the back whilemaneuvering. Similarly, an operator might only push the cobotforward, which would turn thanks to the guide path.

To investigate how operators actually use path guidance, weperformed experiments in typical maneuvering environments[Fig. 1(b)]. By measuring the force and torque exerted by theoperator, as well as the trajectory followed in various condi-tions, we could analyze the strategy used. In particular, we couldassess the efficiency of this assistive device by comparing be-havior in guided versus free mode. Related work on the multi-joint arm investigated forces at static positions in the presenceof kinematic constraints [20], as well as force [21] and stiffness[22] adaptation in constrained movements.

Another issue is how to design ergonomic guide paths. Theseconstraints in the configuration space of the cobot must be spec-ified before they can help the human operator maneuver aroundobstacles. Optimal path planning has been investigated exten-sively in robotics, see, e.g., [23]. [20] has proposed using anergonomic cost function to derive guide paths for cobots. Sincethis approach requires knowledge of the environment, it is lim-ited by the complexity and high cost of sensor processing and bysuboptimal sensor properties. Moreover, an optimal guide pathfor a given task and operator may be not adapted to another taskor person. It seems hard to find a cost function suitable to everytask, cobot, and operator.

However, a device assisting a human operator, such as thecobot, may not need complex sensor processing or mathematicaloptimization. Our idea is to rely on the well-developed sensingand inference capabilities of the operator to design ergonomicguide paths. We envision the operator collaborating with his orher cobot on designing ergonomic guide paths. Similar to thecollaborative control strategies developed for teleoperated mo-bile platforms [24], the human operator and the robot should di-alog to succeed in the task using the best of their respective capa-bilities. However, we use a more direct dialog for our co-movingplatform, based on physical interaction and vision, rather thanon language.

Realizing this collaborative strategy requires providing suit-able tools to design guide paths by feel and experience, as in-troduced in this paper. Experiments are performed to analyzemovements and user satisfaction in paths that were designedusing several distinct methods, providing information on howthese tools should be used.

II. DESCRIPTION OF THE SCOOTER COBOT

A. Hardware

The Scooter cobot is described in detail in [25]. Briefly, eachof the three steering wheels is controlled by a separate DC motor[Fig. 1(a)]; its steering angle is measured by an optical encoderplaced on top of the wheel. A force torque sensor fixed to the

BOY et al.: INVESTIGATION OF MOTION GUIDANCE WITH SCOOTER COBOT AND COLLABORATIVE LEARNING 247

shaft fixed to the centroid of the triangular body measures theoperator’s input. Plastic wheels equipped with an optical en-coder are mounted at fixed angles on the Scooter body in orderto measure the displacement [26]. These glidewheels can glidein the direction normal to their headings, so they can measuredisplacement without affecting motion. They are lightly pressedagainst the ground by ribbon springs such that frictional forceis kept small and they can be sensitive to the movement of theScooter.

The position and orientation of the Scooter is computed bydead reckoning. The Scooter is controlled by a PentiumPro200 MHz PC computer operating under QNX environment. Allcode is in Watcom C.

Force and torque have different units. A factor specifies howthey will be combined in free mode. This factor is set by trialand error to let the operator comfortably maneuver the Scooterin position and orientation like a rigid body.

B. Path Controller and Kinematics

Guide paths are coded as cubic B-splines in configurationspace with the scalar product

. Let and bethe planned and actual paths, parameterized by the path-lengthparameters and , respectively [Fig. 2(b)]. Path following isrealized by the controller of [2] using a feedback linearizationof the curvature equation. This controller integrates and com-putes the curvature to make the difference

(1)

between the current position and the correspondingon the guide path converge asymptotically to 0. Let

(2)

be the difference between the normalized tangential vector onthe current path and the corresponding normalized tan-gential vector on the guide path. The acceleration of thelength parameter and desired curvature is

where and are positive definitematrices, and

(3)

This desired curvature in configuration space is transformed intothe space of the three steering wheels via differential kinematicstransformations [27]. The motors realize it by controlling theirsteering velocities according to

(4)

where are the wheel velocities resulting from the operatorforce and torque on the Scooter and its dynamics, is given by

(5)

is the curvature vector along the actual path, andis the position vector from the center of the platform to the axisof the th steering wheel relative to a Cartesian frame fixed tothe platform.

The velocity and position of the Scooter are measured by thethree glidewheels [Fig. 1(a)]. The steering wheel velocitiescome from the translational velocity and rota-tional velocity of the Scooter using

(6)

with . The velocities , , and are determinedfrom the glidewheel velocities according to

(7)

where , and are the distances from the platform centerto the three glidewheel centers, and is their average. System-atic tests showed that odometry is sufficiently accurate for theexperiments of this paper [27]. For long movements, odometrycan be fused with global positioning information from simplesensors, as in [28].

III. TOOLS TO DESIGN AND EDIT GUIDE PATHS

Our approach to designing guide paths relies on the operator’shaptic sense and vision to build paths around the obstacles. Nosensor or sensor processing need be added to detect obstacles orrecognize the environment.

A dedicated graphical user interface (GUI) path editor is pro-vided for offline definition and modification of guide paths. Thecontrol points of the cubic B-spline defining a guide path can beset/modified by placing/manipulating on the GUI with a mouse(Fig. 3), or by setting/changing their values. The B-spline con-trol points have clear geometric meaning and are intuitive to usein manipulating the path. The number of control points requireddepends on the complexity of the path expected. In Fig. 3, 14control points were used for fairly complex paths.

Walk-through programming (WTP) defines a guide pathby physically tracing it with the cobot in free mode (Fig. 4).This path, least-squares fitted in configuration space with cubicB-splines, is used as a guide path for subsequent motions. If


Fig. 3. Guide paths can be defined by placing and manipulating the B-splinecontrol points coding the paths on a dedicated GUI. The control points canbe used to improve a path. The circles in the two lower panels represent themagnitude of the applied torque, black meaning clockwise torque, and greyanticlockwise.

Fig. 4. Guide-path design using WTP.

the user is not satisfied with this path, he or she can use thededicated GUI to modify it offline, or retrace it altogether.While tracing the path in free mode the operator can evaluatethe motion environment and feel forces, and infer whether theresulting guide path will be suitable.

A major advantage of the WTP is its great simplicity. It nei-ther requires modeling or mapping of the environment nor anyobstacle sensor, and there is no complicated procedure to follow.Coding the path by cubic B-splines presents several advantages:the path is smooth and can be modified locally; the coding iscompact; B-splines are differentiable up to the second order asrequired by the path-following controller [2].

IV. BEHAVIOR IN GUIDED AND FREE MODES

A first experiment, approved by the Institutional ReviewBoard of Northwestern University (Evanston, IL), analyzedhow users move with the Scooter cobot in free and guidedmodes in various environments. It was performed by seven sub-jects (with mean age 24 and standard deviation 2 years) withoutknown sensorimotor disability, who had no prior experiencewith the Scooter cobot.

Fig. 5. Training to use the Scooter cobot in free and guided modes. (a,b,c)Environments used to train maneuvering. (d) Moving in GM (right) requireslittle effort from the first trial, while maneuvering in free mode (left) requiresadaptation. The circles represent the rotational effort along the movement, blackin the counterclockwise direction, and grey in the clockwise direction.

A. Methods

1) Day 1: The data of Day 1 are used to analyze poten-tial learning effects. The subjects were first trained to drive theScooter in guided and free modes in three typical environmentsshown in Fig. 5. In the first two environments [Fig. 5(a), (b)],the subjects were required to follow the line drawn on the floorand place a pin fixed to the Scooter into a hole at the end of thepath. The same lines were used to train the subjects in guidedmotion. The subjects also had to maneuver the Scooter througha passage Fig. 5(c) narrower than the maximum diameter of theScooter and to place the pin into the hole. The obstacles weremade of Styrofoam boards [Fig. 1(b)]. A corresponding guidepath traced by the experimenter was used to train in guided mo-tion. All subjects used the same guide paths in each of the threeenvironments.

Four subjects were trained first in guided mode and then infree mode, while the other three subjects were trained first infree mode. As the results were not different in the two groups,data of all subjects are presented together. The subjects were re-quired to perform at least three trials in each environment. Forthe drawn lines of Fig. 5(a), (b), learning was considered com-plete when the mean distance between the desired and realizedpaths was less than 0.2 m. For the narrow passage of Fig. 5(c),learning was complete when no obstacle was hit. On average,subjects required five trials (range: 3–6) in each environment.The training session took about 70 min for each subject.


Fig. 6. Environments used to investigate movements in free and guided modes(here with a typical path traced in free mode).

2) Day 2: Performance in free and guided modes was testedthe next day in the two environments of Fig. 6. To test freemotion, subjects had to move six times from the start pointto the end point without colliding with any obstacle, and toplace the pin into a hole at the end point. These six paths werethen least-squares approximated in configuration space usingB-splines with 16 control points. The resulting six guide paths,presented in random order, were used to test guided mode.

Using these paths to analyze the behavior in guided versusfree mode avoided using guide paths too different from one sub-ject’s free mode paths. Similar features were observed in the lasttrials on Day 1, where common guide paths had been used, as inthe data measured on Day 2. Further, the results of Day 1 showthat testing first in free mode and then in guide mode, or in thereverse order, does not influence the performance.

B. Data Analysis

We want to analyze the effort to perform movement in freeand guided modes. Obviously, the energy cannot beused for this purpose, as it does not consider internal forces. Forexample, if a subject guided along a path stops at a position andexerts a force normal to the guideway, the corresponding energy(used by the muscles, though not transformed into work done onthe object) is not accounted for in . Instead, we measured theeffort to rotate the Scooter

(8)

and the transverse effort

to redirect movement. In guided mode, it would be theoreticallypossible to complete a movement from the start to the end pointwith zero torque and zero perpendicular force, i.e., with

, as the Scooter needs only a motive force tangentto the guideway. The movement duration and are definedas the time with translational velocity exceeding 0.015 m/s androtational velocity exceeding 0.003 rad/s, respectively. The timemultipliers and insure that for movements with similar

Fig. 7. Comparison of motion behavior in FM and GM. (a) Speed in typical FMand GM. (b) FM with two direction reversals. (c,d) Typical FFT of the force(c) and torque (d) applied by one subject during a movement performed in agiven environment, in free (grey) and constrained (black) modes. The ampli-tudes of the high-frequency components in free motion are higher than that inguided motion, in particular for the torque.

trajectories the effort measures do not depend on the movementduration.1

Operators sometimes needed back-and-forth corrections toaccurately position the Scooter [Fig. 7(b)]. We counted corre-sponding direction reversals by the following criterion: thereis a reversal when the dot product between the current trans-lational velocity vector and any velocity vector less than 2.5 cmaway from the current position is negative, and checked visuallywhether it did correspond to a reversal.

In order to infer the smoothness of movements, we estimatedthe high-frequency content using Fourier transform of the force.The frequency content of the force and torque exerted by the op-erator on the cobot was computed using the fast Fourier Trans-form (FFT). We used the integral of normalized FFT

FFT

FFT (9)

to compare the frequency content of torque and force betweenfree and guided motion in the interval from 0 to the Nyquist fre-quency Hz, 500 Hz (half the sampling frequency).As low frequencies correspond to the path shape and so are sim-ilar in FM and GM, the FFT in either FM or GM was normalizedby the largest amplitude to give FFT . Hence, the differences

and measure the dif-ference in high-frequency content between FM and GM. Zero

1Motions fx (t ); 0 � t � T g for i = 1; 2 are similar whenx (t =T ) � x (t =T ). Let M � fx(t); 0 � t � Tg be anequivalence class under this relation and x 2M such that T = 1. The effortmeasure is then "(F) = (1=T ) j�x(t)jdt = j�x (t )jdt ; t = t=Tfor all x 2M , i.e., this effort measure does not depend on how fast a movementis performed.


padding was used. Aliasing was not an issue as we used a sam-pling frequency of 1000 Hz and an external analog low-passfilter with cut-off frequency 500 Hz.

Directional -tests were used to investigate learning-relatedfeatures and to compare GM with FM, after Lilliefors tests con-firmed that the data were -distributed.

C. Results

Rotation effort during training was used to infer whether thereis adaptation when consecutive trials were performed, in freeor guided mode (Fig. 5). There was a significant adaptation,as with . In contrast, nolearning was required in GM.

We then investigated the force and torque exerted on theScooter in FM and GM. For movements in free and guidedmodes, we computed the effort measures of (8), which arethe total force and torque magnitude during the movement.The results show that the rotational effort is smaller inGM than in FM; however, the transverse effort doesnot vary in GM. The rotation effort over the two environmentsand all subjects is significantly larger in FM than in GM( ), while the transverse effort is not significantlydifferent ( ). Similar results as with the transverse effort

were found with the total force , defined similarlyas the integral of the force .

The subjects took less time to complete the same path in GMthan in FM ( ), as illustrated in Fig. 7(a). As the dis-tance is the integral of the speed, one may expect the mean speedto be significantly larger in GM, so that the area under the speedcurves would be similar in FM and GM. However, the meanspeed in GM is larger by only 0.04 m/s in mean over the sub-jects. The longer time taken in mean by FM may be due to moreback-and-forth corrective movements [Fig. 7(b)] in FM than inGM. A -test confirmed that there were more direction rever-sals in FM than in GM ( ).

Motions in GM showed smaller high-frequency content thanin FM [Fig. 7(c), (d)]. As the FFT for either FM or GM wassimilar in all environments, we used its mean over the two en-vironments in Fig. 6. The torque contained significantly morehigh frequencies in FM than in GM ( with

). The force generally contained more high frequen-cies in FM than in GM, but the difference was not significant( with ).

To investigate how the operators use motion guidance(Fig. 8), we computed the correlation between the force normalto the path traced on the plane and, also normal to thispath, the curvature vector defined by

(10)

where is a unit normal vector: , .A high correlation coefficient indicates that the operator is pro-viding a centripetal force to move the Scooter along a curve.Therefore, a high correlation is expected in FM. However, inGM, the operator does not have to provide centripetal force toturn, and the correlation coefficient becomes an indicator of themotion strategy used. A large (positive) correlation would in-dicate that the operator turns with the curve, while an oper-

Fig. 8. Comparison between normal force and curvature of one typical (x; y)path in free mode (grey, top panels) and in guided mode (black, lower panels)performed by the same subject, plotted along the actual trajectory. In free mode,the normal force, thus also the normal acceleration, is correlated with the cur-vature, as it is used to turn. In guided mode, the correlation coefficient betweenthe normal force and curvature is indicative of the motion strategy used by theoperator.

TABLE IIMPORTANT FACTORS FOR GOOD PATHS

ator just pushing straight will produce force anticorrelated withcurvature.

The correlation coefficient between the FM normal force andthe curvature was a mean 0.6, with a standard deviation 0.1 forall subjects and the two environments. In GM, normal force andcurvature were uncorrelated, with a mean coefficient of 0.0 anda standard deviation of 0.1.

After the experiment was completed, the subjects wererequired to list the important factors for good paths in FMand GM (Table I), they selected “little effort” and “smooth,”corresponding to the measures of effort and frequency contentin force and torque. The other important factors cited, namely“effective,” “comfort,” and “sufficient clearance,” are hard toevaluate mathematically. “Effective” covers subjects’ citerion


Fig. 9. Flow diagram of second experiment described in Section V-A.

of few reversals or a short path. “Little effort” was felt to bemuch less important in GM than in FM, probably because inGM the effort is small anyway.

D. Interpretation

The above results show that movements with the Scootercobot are fundamentally different in guided mode than in freemode.

• Movements in guided mode did not require training. Move-ments in guided mode required little effort and fewer onlinecorrections, and were smooth from the first trial. In con-trast, movements in free mode showed significant adapta-tion in consecutive movements. They required significantlymore effort and corrections than in guided mode, even afteradaptation.

• In particular, significantly more rotational effort was re-quired to maneuver the Scooter in free mode than in guidedmode. Translational effort was not significantly different infree mode and guided mode.

• In guided mode, the operator can rely on the constraintsto guide the movement and excessive normal forces arecounteracted by friction. Therefore, we might expect largernormal forces to be used in guided mode than in free mode.However, even in highly curved paths, the total normalforce was similar in guided mode to that in free mode, orsmaller.

• Movements in guided mode were significantly faster, andhad fewer back-and-forth corrective movements than infree mode.

• The high-frequency content of applied torque was lowerin guided mode than in free mode, indicating smoothermotions in guided mode.

These points demonstrate the advantages of the guided modeover the free mode and the strength of the cobot concept.

V. TESTING PATH GUIDANCE DESIGN TOOLS

The tools introduced in Section III, WTP and the dedicatedGUI, were implemented on the Scooter and tested in exper-iments (approved by the Institutional Ethics Committee) per-formed by seven informed subjects. These subjects had no priorexperience with cobots, except one of them who had already per-formed the first experiment. The experiments (Fig. 9) tested per-formance with these tools and were complemented by a ques-tionnaire showing the users’ preferences.

A. Methods

1) Day 1: Training: The subjects were first trained to drivethe Scooter cobot as explained in Section IV-A.1). They werethen instructed how to use the WTP and GUI, and used them todesign guide paths in the environment of Fig. 3.


Fig. 10. To test visual selection capabilities, the subject was presented inrandom order with six paths that he or she had traced in free mode, and requiredto select a good guide path. The obstacles were visible, as in these snapshots ofthe GUI presented to the subjects.

To learn use of the WTP, the subjects designed guide pathswith it and were instructed to experience the resulting guid-ance extensively. On average, each subject realized three paths(range: 2–5).

To learn use of the GUI, the subjects were told to note theregions of the guide path they were not content with, and toimprove the path locally until satisfied (Fig. 3). On average,each subject realized three improvements (range: 2–5), i.e., al-together four trials.

2) Day 2: Guide Path Design: The next day, the subjects de-fined guide paths in the two environments of Fig. 6. The subjectshad first to design six paths with the WTP, i.e., to trace six pathsin free mode. They had to grade each of these six tries, i.e., toestimate how well the resulting path would guide motion. Thefollowing scale was used: fail, bad, okay,good, and optimal.

The subjects then had to define a guide path with 16 controlpoints using the GUI. To test how well guide paths can be eval-uated visually, the six paths traced by the subjects with the WTPwere presented to them in random order (Fig. 10), to be gradedon the above scale.

Additionally, five (of the seven) subjects were instructed tomove in GM along the best path defined with the WTP and im-prove it using the GUI. On average, each of these subjects im-proved the path twice (range: 2–3), i.e., each subject did on av-erage three trials.

3) Day 3: Evaluation of Paths Designed Using WTPand GUI: Five guide paths were tested on the next day:1) the highest-graded defined in WTP (free mode); 2) thelowest-graded defined in WTP; 3) the guide path defined in theGUI; 4) the path selected visually, and 5) best path in WTPimproved using the GUI after driving it in GM (for the fivesubjects who did this).

The subjects were required to move the Scooter alongthese paths and grade the movements using the above scalein Section V-A.2. In each environment, they were requiredto make at least three series of movements along these five(for two subjects, four) guide paths, refine the grading after

each series, and continue to try the paths until they had stablegrading. The first series presented the paths in the above order.In subsequent series, the order could be modified at will, forexample, to compare two similar guide paths by examiningthem consecutively. Only final grades were analyzed, and morethan one of the guide paths could obtain the highest grade.On average, the subjects did four movement series (least four,most six). Wilcoxon rank-sum tests [29] were used to compareperformance with the various guide path design tools.

After the experiment, the subjects were required to answerthe following questions.

1) How easy is it to learn with the WTP, respectively, withthe GUI? Options were: very difficult ; difficult ;average ;, easy ; very easy .

2) How easy is it to use the WTP, respectively, the GUI, todesign guide paths? Options were: very difficult ; dif-ficult ; average ; easy ; very easy .

3) Is this method good to design guide paths? Options were:bad ; poor ; average ; good ; excellent .

4) Will you use this tool to design guide paths? The subjecthad to answer yes or no for each of the followingfour possibilities: WTP alone; GUI alone; WTP and GUI;and “none of these methods.”

A statistical analysis was performed on the answers.

B. Results

1) Analysis of User Satisfaction: First, what is the relation-ship between the user satisfaction after moving in an environ-ment and the mathematics measures of Section IV-B? In FM,the measures of effort ( ) and smoothness ( ),as well as the number of corrective movements ( ), de-creased with increasing grading of the user satisfaction. Thisshows that these measures, which were used in Section IV toevaluate movements, correspond to user satisfaction. For GM,these motion measures are very small, and do not vary with thesatisfaction grade ( ), thus the satisfaction must encom-pass other factors as well.

Fig. 11 shows how the subjects graded the five different pathsalong which they performed guided motion. We first observethat the guide path corresponding to the highest-graded pathtraced in FM was almost always preferred to the lowest-graded( ). This indicates that the subjects are able to inferwhich free-mode path will result in good guide paths, and showsthe efficacy of the WTP.

Guidance with the path selected visually from the six pathstraced in FM was no better ( ) than the worst path. Onemay expect this when considering that in Fig. 10, the six pathsare hard to differentiate; however, as we will see from the an-swers to the questionnaire, the subjects thought that they wereable to select good paths visually.

The other modality based on vision only, consisting of de-signing guide paths by placing B-spline control points on theGUI, did not seem to work better: the guide path correspondingto the highest graded path traced in FM was preferred to thatdesigned with the GUI ( ).

The best path designed with the WTP and improved in theGUI was better than the original path ( ). The subjectsfound this path design method “optimal,” i.e., not significantly


Fig. 11. Mean satisfaction of all subjects with different path design methodsdescribed in Section III and significance analysis. The ratios in brackets indicatehow many times a path designed with the corresponding method was selectedas best guide path when moving along it.

different from optimal with . So the GUI appearsto be a good complement to the WTP.

2) Questionnaire About WTP and GUI: The questionnaireabout WTP and GUI filled out by subjects at the end of the testscomplement the analysis of user satisfaction. They reveal thefollowing.

• The subjects found it “very easy” to learn the WTP andonly “easy” to “average” for the GUI. The difference ofapproval between the two tools is significant ( ).

• Similarly, they found it “very easy” to use the WTP to de-sign a guide path, only “average” with the GUI, significantat .

• They found “WTP and GUI” (WTP followed by visual se-lection: Fig. 10) an “excellent” way to design guide paths,and preferred it to both the WTP ( ) and the GUIalone ( ), which they found “good.” They ratedsimilarly the GUI and WTP ( ).

• All subjects would use “WTP and GUI” ( ), butnot the GUI ( ) or the WTP alone ( ).

At first sight, these results seem to contradict the results ofsatisfaction after each movement reported in previous section.Although visual selection of paths traced with the WTP doesnot work well (see Fig. 11), in the questionnaire, the subjectspreferred this possibility to both WTP or GUI alone. Moreover,while they would all like to use this poor design method, theywould not use the WTP alone.

This surprising result suggests that, although the subjects areactually able to design a good guide path with the WTP alone,they need a visual representation of it to feel comfortable. Thesubjects cannot trust their haptic senses and need to check withtheir visual senses.

VI. DISCUSSION

A. Efficiency of Motion Guidance

This study first showed that path guidance does assist ma-neuvering of the Scooter cobot. Motion guidance enables oper-ators to significantly reduce effort, in particular, rotational ef-

fort. Ergonomics studies on pushing/pulling suggest that the re-duced effort will prevent back injuries [16]. The reduced ef-fort will lead to reduced energy burn by the operator, whichmay reduce fatigue. The fact that motions were faster, smoother,and need less correction in GM suggests that subjects performmore comfortably in guided than in free mode. Leaning into acobot without concern over correcting errant motion may en-able the operators to remove the muscle forces needed to keepthe cobot under control and handle jerks and other high-stressactions. These advantages of mechanical guidance demonstratethe strength of the cobot concept.

These results were obtained with a particular cobot, theScooter, and planar motion. The prototype used in the experi-ments had friction, which may have affected the results, thoughreal trolleys also have friction. Further, can we expect similarresults for general motion guidance involving six DOF and loadsupport against gravity? The Scooter reduces a 3-DOF motionguidance task to a 1-D one. Another interface reducing a 6-DOFtask to a 1-D task may yield similar or even larger differences.In particular, similar to our finding about minimization ofrotation effort with the planar Scooter cobot, motion guidancein 6 DOF may enable to minimize more complex torsion withpotential advantages in ergonomics.

B. How Do Operators Use Motion Guidance?

The results on movements also revealed how the operators be-have when using the Scooter in guided mode. To move a rigidobject in a plane, one needs force to drive it along antrajectory, and torque to rotate it along this trajectory. Further,the normal force is used to curve the trajectory while theparallel force accelerates it along this trajectory. This was il-lustrated by the free-mode case, in which the rotational effort(i.e., the torque) is large, and the normal force correlates withthe path curvature.

How do operators use motion guidance? The subjects use thepath constraints in configuration space first to minimizerotational effort, i.e., torque. They could also avoid using normalforce to direct the Scooter along curved paths. However,the results show that they need as much total force in guidedas in free mode. If they were turning the cobot by themselves,their normal force would be correlated with curvature.However, the correlation coefficient between the normal forceand curvature was not positive, showing that the operatorsused the guide paths to direct the Scooter.

It would not be surprising that the operators used the con-straints to facilitate motion. We may expect that the price to payfor moving easily along a guide path is higher normal force. Ifthe operator were to push the cobot straight, without knowledgeof the path, it may be expected that the normal force would belarge and negatively correlated with curvature. However, thenormal force was relatively small and not (anti)correlated withcurvature, indicating that the operators did not just push thecobot straight. Perhaps the haptic information received throughnormal force was integrated during the movement to determinefurther movement and helped the operator to keep pushingmainly in the correct direction.


C. Efficiency of the Guide Paths Design Tools

The novel approach proposed in this paper to design er-gonomic guide paths consists of letting the operator define asuitable path for the task. The dedicated GUI uses a B-splinerepresentation for offline definition and modification of guidepaths. It is also possible to define a path online by WTP, i.e.,by least-squares fitting a traced-out path in configuration space.Both the psychophysical experiments and the questionnairedemonstrated the efficacy of these approaches in designingergonomic guide paths for the (Scooter) cobot.

Using the WTP and GUI, all seven subjects could designguide paths that they found optimal, using only a few trials.When they trace a path the operators know whether this willresult in satisfying GM. In the questionnaire filled after comple-tion of the experiments, the subjects confirmed that it was easyto learn and use these tools to edit ergonomic guide paths, andthat they were satisfied with these tools and would use them.

D. Necessity and Complementarities of WTP and GUI

As the subjects were satisfied with guide paths designed witheither the WTP or the GUI alone (Fig. 11), could one of thesetools be discarded? Paths designed with WTP, i.e., using hapticfeedback, are highly appreciated and preferred to paths designedwith the GUI, i.e., using vision, which do not result in goodguide paths. This suggests that only WTP is necessary to de-sign ergonomic guide paths. However (according to the ques-tionnaire), the subjects find the GUI indispensable and think itis more important than the WTP.

An obvious solution to this apparent paradox, that the subjectswant to see the guide paths although they cannot select goodpaths visually and can design good guide paths without visualdisplay of the path, consists of giving the cobot users both WTPand GUI. This enables them to design good guide paths withthe WTP and also fulfill their desire to see the guide path withthe GUI, which may be the reason why the subjects rated thispossibility as “optimal” (Fig. 11).

E. Collaborative Learning

Our vision is that the human operator and the cobot, usingthe WTP or the GUI, gradually improve a guide path, eventuallyachieving ergonomic motion guidance. To realize this collabo-rative learning, the operator can optimize the guide path onlineusing WTP locally or offline on the GUI. In our experiments, allseven subjects were able to improve or modify guide paths as inFig. 3, using only one or two trials. This resulted in smooth andsatisfying guide paths.

An elastic path controller has recently been designed, inwhich the path can be deformed online by applying normalforce in the desired direction (Fig. 12) [30]. After the trial iscompleted, the modified path is proposed to the subject onthe GUI, who can accept the modification if he or she thinksthat the environment change is permanent, or that the newpath is more appropriate. Fig. 12 shows how such a modifiedpath controller can be used to improve a path when there is anobstacle on the guideway.

Fig. 12. Collaborative learning using the elastic path controller.

ACKNOWLEDGMENT

The experiments were performed at LIMS, NorthwesternUniversity. The authors thank (in alphabetical order)E. Faulring, S. Kim, M. Peshkin, T. Poston, and M. Saladafor their suggestions and contributions, and the reviewers forinsightful comments.

REFERENCES

[1] M. A. Peshkin, J. E. Colgate, W. Wannasuphoprasit, C. A. Moore, R.B. Gillespie, and P. Akella, “Cobot architecture,” IEEE Trans. Robot.Autom., vol. 17, no. 4, pp. 377–390, Aug. 2001.

[2] R. B. Gillespie, J. E. Colgate, and M. A. Peshkin, “A general frame-work for cobot control,” IEEE Trans. Robot. Autom., vol. 17, no. 4, pp.391–401, Aug. 2001.

[3] W. Wannasuphoprasit, R. B. Gillespie, J. E. Colgate, and M. A.Peshkin, “Cobot control,” in Proc. IEEE Int. Conf. Robot. Autom.,1997, vol. 4, pp. 3571–3576.

[4] J. Troccaz, M. A. Peshkin, and B. L. Davies, “Guiding systems for com-puter assisted surgery: Introducing synergistic devices,” Med. ImageAnal., vol. 2, no. 2, pp. 101–119, 1998.

[5] A. Bettini, S. Lang, A. Okamura, and G. Hager, “Vision-assisted con-trol for manipulation using virtual fixtures,” IEEE Trans. Robot., vol.20, no. 6, pp. 953–66, Dec. 2004.

[6] D. J. Reinkensmeyer, J. L. Emken, and S. C. Cramer, “Robotics, motorlearning, and neurologic recovery,” Annu. Rev. Biomed. Eng., vol. 6,pp. 497–525, 2004.

[7] A. H. Dodson, G. V. Moon, T. Moore, and D. Jones, “Guiding blindpedestrians with a personal navigation system,” J. Navig., vol. 52, no.3, pp. 330–41, 1999.

[8] T. Harada, Y. Kaneko, Y. Hirahara, K. Yanashima, and K. Magatani,“Development of the navigation system for visually impaired,” in Proc.Int. Conf. IEEE Eng. Med. Biol. Soc., 2004, vol. 7, pp. 4900–4903.

[9] J. D. Yoder, E. T. Baumgartner, and S. B. Skaar, “Initial results in thedevelopment of a guidance system for a powered wheelchair,” IEEETrans. Rehab. Eng., vol. 4, no. 3, pp. 143–151, Sep. 1996.

[10] T. Röefer and A. Lankenau, “Architecture and applications of theBremen autonomous wheelchair,” Inf. Sci., vol. 126, no. 1, pp. 1–20,2000.

[11] E. S. Boy, C. L. Teo, and E. Burdet, “Collaborative wheelchair assis-tant,” in Proc. IEEE/RSJ Int. Conf. Robot. Intell. Syst., 2002, vol. 2, pp.1511–1516.

[12] A. Civit-Balcells, F. D. Del Rio, G. Jimenez, J. L. Sevillano, C. Amaya,and S. Vicente, “SIRIUS: Improving the maneuverability of poweredwheelchairs,” in Proc. IEEE Int. Conf. Control Appl., 2002, vol. 2, pp.790–795.

[13] S. Ishida, J. E. Gayko, and J. E. Source, “Development, evaluation andintroduction of a lane keeping assistance system,” in Proc. IEEE Intell.Veh. Symp., 2004, pp. 943–944.

[14] J. Wang, S. Schroedl, K. Mezger, R. Ortloff, A. Joos, and T. Passegger,“Lane keeping based on location technology,” IEEE Trans. Intell.Transport. Syst., vol. 6, pp. 351–356, 2005.


[15] S. Fujiwara, H. Kitano, H. Yamashita, H. Maeda, H. Fukunaga, and H. ,“Technical Report,” (in Japanese) Matsushita Elect. Works, 78: 28-33,2002.

[16] M. H. Pope, K. L. Goh, and M. L. Magnusson, “Spine ergonomics,”Annu. Rev. Biomed. Eng., vol. 4, pp. 49–68, 2002.

[17] A. Magora, “Investigation of the relation between low back pain andoccupation: IV) Physical requirements: Bending, rotation, reachingand sudden maximal effort,” Scand. J. Rehab. Med., vol. 5, no. 4, pp.186–190, 1973.

[18] D. K. Damkot, M. H. Pope, J. Lord, and J. W. Frymoyer, “The rela-tionship between work history, work environment and low-back painin males,” Spine, vol. 9, pp. 395–399, 1984.

[19] M. M. Ayoub and J. W. McDaniel, “Effects of operator stance onpushing and pulling tasks,” Amer. Inst. Ind. Eng. Trans., vol. 6, pp.185–195, 1974.

[20] K. M. Lynch, C. Liu, A. Sorensen, M. A. Peshkin, J. E. Colgate, T.Tickel, D. Hannon, and K. Shiels, “Motion guides for assisted manip-ulation,” Int. J. Robot. Res., vol. 21, no. 1, pp. 27–43, 2002.

[21] V. S. Chib, J. L. Patton, K. M. Lynch, and F. A. Mussa-Ivaldi, “Hapticidentification of surfaces as fields of force,” J. Neurophysiol., vol. 95,pp. 1068–1077, 2006.

[22] H. Gomi, “Anisotropic stiffness reduction during constrained multi-joint arm movement,” in Proc. Conf. IEEE Eng. Med. Biol. Soc., 1998,vol. 20, pp. 2336–2337.

[23] H. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. E.Kavraki, and S. Thrun, Principles of Robot Motion. Cambridge, MA:MIT Press, 2005.

[24] T. Fong, C. Thorpe, and C. Baur, “Advanced interfaces for vehicle tele-operation: Collaborative control, sensor fusion displays, and remotedriving tools,” Auton. Robots, vol. 11, no. 1, pp. 77–85, 2001.

[25] W. Wannasuphoprasit, “COBOTS: Collaborative robots,” Ph.D. disser-tation, Northwestern Univ., Evanston, IL, 1996.

[26] A. L. Mills, “An analysis of position and velocity sensor systems for a3-degree-of-freedom planar collaborative robot,” M.Eng. thesis, North-western University, Evanston, IL, 1998.

[27] E. S. Boy, “The learning cobot,” Masters thesis, Dept. Mech. Eng., Nat.Univ. Singapore, , Singapore, 2003.

[28] Q. Zeng, C. L. Teo, B. Rebsamen, and E. Burdet, “Design of a collab-orative wheelchair with path guidance assistance,” in Proc. IEEE Int.Conf. Robot. Autom., 2006, pp. 877–882.

[29] J. S. Milton and J. C. Arnold, Introduction to Probability and Statistics,3rd ed. New York: McGraw-Hill, 1995.

[30] B. Long, B. Rebsamen, E. Burdet, H. Yu, and C. L. Teo, “Elastic pathcontroller for assistive devices,” in Proc. IEEE Int. Conf. Biomed. Eng.,2005, pp. 6239–6242.

Eng Seng Boy received the B.Eng. degree (first classhonors) in 2001, and the M.Eng. degree in 2002,both from the National University of Singapore,Singapore.

He is currently a Physics tutor and the Head of Stu-dent Development with Victoria Junior College, Sin-gapore.

Etienne Burdet (S’92–M’96) received the M.S.degree in mathematics in 1990, the M.S. degree inphysics in 1991, and the Ph.D. degree in robotics in1996, all from ETH-Zurich, Zurich, Switzerland.

He currently is a Senior Lecturer with Impe-rial College London, London, U.K. He is alsoperfoming research at the interface of roboticsand bioengineering; his main research interest ishuman–machine interaction. He has made contribu-tions in various fields from human motor control toVR-based training systems, assistive devices, and

robotics for life sciences.

Chee Leong Teo recieved the B.Eng. degree (firstclass honors) in 1980 from the University of Singa-pore, Singapore, and the Ph.D. degree in mechanicalengineering from the University of California,Berkeley, in 1988.

He is currently an Associate Professor with the De-partment of Mechanical Engineering, National Uni-versity of Singapore (NUS), Singapore, and the Di-rector of NUS Overseas Colleges since 2002. His re-search areas are in the controls of mechanical systemsand human–machine interface.

James Edward Colgate (M’88) received the Ph.D.degree in mechanical engineering in 1988 from theMassachusetts Institute of Technology, Cambridge.

He subsequently joined Northwestern University,Evanston, IL, where he is currently a Professor inMechanical Engineering. His principal research in-terest is human–robot interaction. He has worked ex-tensively in the areas of haptic interface and teleop-eration. He is a founder of Stanley Cobotics and ofChicago PT, and is the director or IDEA, the Institutefor Design Engineering and Applications of North-

western University.Dr. Colgate has served as an Associate Editor of the Journal of Dynamic

Systems, Measurement and Control and the IEEE TRANSACTIONS ON ROBOTICS

AND AUTOMATION.

Documents

Investigation of Motion Guidance With Scooter Cobot and Collaborative Learning