Study of operator behavior, learning, and performance ... - IEEE Xplore

Proceedingsof the 2004 IEEE

International Conferenceon Control Applications Taipei, Taiwan, September 2-4,2004

STUDY OF OPERATOR BEHAVIOR, LEARNING, AND PERFORMANCE USING AN INPUT-SHAPED BRIDGE CRANE

Attir Khalid, William Sanghose, John Huey, and Jason Lawrence Department of Mechanical Engineering Georgia Institute of Technology Atlanta, GA 30332-0405 bill.singhoseOme.gatech.edu

David Frakes 4-D Imaging, Inc. Atlanta. GA 30302 Abstract Input shaping is a simple way of reducing vibration in bridge cranes. Reduction of payload sway is particularly important if the crane must operate in a cluttered workspace or has to accurately position payloads. An input shaping controller has been implemented on a large bridge crane at the Georgia Inst.itute of Technology. It is used to study the response of operators and their learning patterns while driving the crane through obstacle courses both with and without input shaping. An image processing technique was implemented to track the movement of the crane payload. Data from these experiments show that operators performed tasks faster, safer, and more effectively when input shaping is used. Index Terms: Sway Control. Vibration Control, Crane Control, Operator Learning

INTRODUCTION ". . .the more advanced a control system is, the more crucial may be the contribution of the human operator.. . '' - Bainbridge (1983) [l] Human-operated positioning systems hold a prominent position in industry today. However, as these systems become more complex and powerful, it is crucial that the human operators' titsks be well defined, and that they are adequately trained. Otherwise, human f machine interaction problem could have a great negative influence on productivity and system reliability 121. Control schemes can be added to human-operated systems for the purpose of improving the operator's 1.

0-7803-8633-7/04/$20.00 02004 IEEE

Figure 1: Sketch of Bridge Crane. performance. For instance, even a skilled crane operator can have difficulty maneuvering a payload without inducing sway. So, a secondary control scheme may be added to achieve low vibration motions. However, these secondary control schemes operate by augmenting the operator's original commands. This has the potential of confusing or annoying an operator, potentially resulting in poorer overall results. There fore, it is necessary to analyze the cognitive process of operators, and their decision making process needs to be studied [3]. The system studied here is a human-operated bridge crane. When a human operator attempts to maneuver payloads using an overhead bridge crane like the one sketched in Figure 1, significant oscillations can be induced into the payload. The crane oscillations make it difficult to manipulate the payload quickly and with good positioning accuracy. Furthermore, when the workspace is cluttered with obstacles, the oscillations can create significant safety issues, especially when the payload or obstacles are of a h a z

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ardous or fragile nature. Input shaping has been shown to be effective for controlling the oscillation of several types of cranes [4-101. Input shaping is implemented in real time by convolving the human-generated command signal with an impulse sequence (an input shaper). This process is illustrated in Figure 2 with a pulse input and an input shaper containing two positive impulses. The key to input shaping is knowledge of the system’s natural frequencies and damping ratios. Also, it is important to choose a shaper that is suitably robust to system parameter variatiom [5, 111. Our emphasis is to study the tacit human skills and performance in controlling a bridge crane. Since input shaping augments the operator’s commands, its compatibility with a human operator must be investigated. The goal of this research is to show that input shaping makes crane operation safer, more efficient and more reliable. Also. the data from the exDeriments can be used to design improved controllers that take into account the control skill typically demonstrated by expert crane operators. This research is comprised of two different studies. The first study simply observed unskilled Operators driving a bridge crane both with and without input shaping control. The second study followed a set of operators for several weeks and observed their performance over this time frame. This study integrates the operators’ ability to learn and gain skill. A bridge crane a t the Georgia Institute Of Technology was equipped with an input shaping controller and crane operators were asked to run the crane through different obstacle courses both with and without input shaping. Data from these experiments was analyzed to determine how the crane OP erators reacted to input shaping. The experiments were performed with a diverse pool of crane operators. The volunteers had varying skill levels, but most were novice operators. For analysis, crane runs were videotaped and the crane trajectory was obtained ming a video tracking algorithm. The next section describes the input-shaped bridge crane. Section 3 discusses the results obtained from the unskilled crane operator experiments. Section 4 explains various operator learning mechanism and discusses the learning patterns observed for the crane operators. Conclusions are presented in the fi~

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INPUT SHAPING ON A BRIDGE CRANE

A sketch of the crane used in these experiments is shown in Figure 1. The major components of the crane are the bridge, the trolley, the payload, and the control pendant. The bridge is an I-beam that movw forward and backward. The trolley moves left and right on the bridge. The hoisting mechanism, hoisting cable, and payload hang from the trolley. The crane has a capacity of 10 tons. Its usable workspace is 20 feet high, 30 feet wide and 140 feet long. New hardware was added to the crane to implement input shaping. Figure 3 shows how the new hardware was installed and how it generates the input-shaped control signal. Button-signals generated by the human operator travel from the pendant to the hoist controls or the bridge-and-trolley control box. In the bridge-and-trolley control box, a programmable logic controller (PLC) performs the input shaping algorithm. These commands are then sent to the trolley and/or bridge motor drives. These drives use the incoming command from the PLC as velocity set points for the motors. To insure accurate execution of the PLC commands, the drives are AC-AC inverters. This type of drive uses a pulse width modulated signal to accurately control the motors. In order to be compatible with the drives, the motors are inverter duty capable. Figure 4 compares the unshaped and shaped responses of the crane payload for a typical maneuver. This figure shows that the payload oscillates while the crane is commanded to move and after it is commanded to stop. Also, it shows that input s h a p

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ing virtually eliminates these oscillat~ions. To c a p ture this motion a camera was mounted overhead. The crane runs were videotaped to capture the entire run through the obstacles. An image processing algorithm in MATLAB was then used to obtain the payload trajectory. 3. NOVICE CRANE OPERATORS A crane operator must be extremely careful in avoiding collisions between the payload and obstacles. Any such mishap could result in an injury or a damaged payload. On the other hand, a crane operator must work with a high level of speed and efficiency. Therefore, a real crane operator must make tough decisions on how fast to go and what paths to take in a cluttered work environment. Given that input shaping changes the dynamic nature of the crane it is important to investigate how operator performance and behavior change with input shaping. Experiments were conducted to examine how input shaping changed an operator’s performance. Volunteers ran the bridge crane through the obstacle courses shown in Figures 5 and 6. Each operator ran the crane both with and without input shaping. The goal was to move the crane from a start region to a goal region quickly without running into any o b stacles. In order to observe changes in operator behavior, each course had an option of two paths for reaching the target. These long and short paths are shown schematically in Figures 5 and 6. The shorter paths have more narrow bends and turns as compared to the longer paths. Therefore they require comparatively more accurate positioning.

Figure 6: Possible Routes in Course 2 A total of 23 volunteers drove the crane through the courses. Ten volunteers were tested on Course 1 and 13 on course 2.

3.1

Run Times

The run time is the amount of time taken to drive the crane from the start region to the end region. A run is considered complete when the crane payload reaches the end region and does not swing outside this region. Figures 7 and 8 show the run times for Courses 1 and 2, respectively. The dark bars represent the unshaped runs whereas the light bars represent the shaped runs. The average times to complete the courses with shaping were 31 seconds and 51 seconds for Course 1 and Course 2, respectively. On the other hand, the average times for the unshaped runs were 88 seconds and 135 seconds, respectively. The bar graphs clearly indicate that the crane operators took much less time t o perform their task when input shaping was enabled on the crane.

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Choice Between Long or Short R o u t e

Input shaping reduces the dynamic complexity of the crane. Thus, operators tended to be more aggressive and took the shorter, more efficient route when input shaping was available. Hence, the choice of a shorter path and an improvement in crane control combined to yield the much shorter run times. Figures 9 and 10 compare the number of people that took the short or long routes for both courses. Both of the courses resulted in the same trend overall. Crane o p erators preferred taking the shorter, more complex path when the input shaping was turned on. For Course 1, all 10 operators chose the short path for their shaped run, whereas for Course 2, 11 out of the 13 crane operators chose the shorter route for their shaped run. This indicates that the crane operators felt more confident with the input shaper and chose to take the shorter route despite its complexity. 4.

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OPERATOR LEARNING

There is a trend in using advanced control strategies based on learning techniques. The learning patterm and schemes for operat,ors can be quite sophis ticated. If the control task is repetitive, some knowledge can be extracted and patterns can be observed

for the task [12]. Experiments have been done to study the human capability to perform the tasks by learning iteratively. Results from these experiments show the ability of the human operator to perform the tracking of a desired trajectory for some unknown non-linear system with quite reasonable accuracy during the iteration process 1131. Once the task has been assigned to the human operators, it is assumed they perform the new task starting with some initial guess, then in each subsequent trial they recall the previous control pattern and modify it simultaneously according to the task requirements.

It is a common observation that learning to perform a new task which has been encountered previously, becomes easier after each encounter [13]. In order to test this phenomenon for bridge crane operators, volunteers were asked to drive the crane through the same obstacle course several times over a period of several weeks. A total of 12 people ran the crane between 3 and 5 different times. The performance of a crane operation is greatly dependant on the amount of swing present in the crane. Although the input shaper cancels the swing out completely, manual swing control techniques also exist that enable the operator to cancel out the swing

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OBSTACLES

Trial Number Figure 1%:Average Run Times

Fignre 11: Crane Course for Learning Study. in the crane even in the absence of input shaping. Most of the amateur crane operators rely on a “passive” approach for swing cancellation. They limit themselves to slow movements in the horizontal direction, thus avoiding large oscillations. They mainly wait for the swing to dampen out due to air drag b o fore changing the direction of their maneuver. This tactic works, but results in long task completion times. To achieve shorter times, it is necessary to move at maximum velocity most of the time, which causes a large swing. It turns out that large swings can be damped “actively” by appropriately nianipulating the horizontal force 1141. Unfortunately, a badly timed active control can instead make the swing worse. Acquiring such an “active” swing control thus requires a decent. amount of practice and expertise. An auxiliary goal of this research was also to examine the time required by an amateur crane operator to become familiar with such a control technique. Volunteers drove the bridge crane through the obstacle course shown in Figure 11 . Each operator was allowed to run the crane through t.he same course at least once a week. Each run consisted of a complete unshaped run and a complete shaped run. The task was to learn a crane-driving strategy through which they would maneuver quickly through the obstacle course with no collisions. In order to observe changes in the operator behavior, the obstacle course had an option of two paths for reaching the target.

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Trial Number Fignre 13: Number of Collisions trend, the data does not indicate significant learning. Perhaps a larger number of trials would show more improvement based upon the volunteers gaining operational skills. Still, the data suggests that, although increasing the skill and practice of an operator can improve performance, the presence of vibration control (i.e. input shaping) is a much bigger factor in the overall speed with which tasks can be completed. 4.2

Collisions

Figure 13 shows the number of collisions from the shaped and unshaped runs. Again, while gaining skills over a period of time does seem to generally d o crease the number of collisions, the presence of input shaping virtually eliminates crashes from the beginning of the learning process.

Figure 12 shows the average timetecompletion for the unshaped and shaped runs over the 5 trials. 4.3 Choice Between Long or Short Route Similar to the results shown in Section 3.2, FigClearly, the volunteers where able to consistently perform their task much quicker with input shaping than ure 14 compares the number of volunteers that chose with no vibration control. However, although both the long or the short route. For the shaped runs, control techniques show a slight, overall downward more people chose to take the shorter route. This

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[4] M. Agostini, G. C. Parker, K. Groom, H. Schaub, and R. D. Robinett, ”Command shaping and closedloop control interactions for a ship crane,” 2002 American Control Conference, vol. 3,2002, pp. 22982304.

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N. C. Singer and W. P. Seering, “Preshaping command inputs to reduce system vibration,” J . of Dynamic Sys., Measurement, and Contml, vol. 112, no. March, pp. 76-82, 1990.

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[6] N.Singer, W. Singhose, and E. Kriikku, “An input shaping controller enabling cranes to move without sway,” ANS 7th Topical Meeting on Robotics and Remote S y s t e m , Augusta, GA, 1997.

Unshaped Runs

Figure 14: Choice between Long or Short Path.

171 W. Singhose, L. J. Porter, and W. Seering, “Input shaped control of a planar gantry crane with hoisting,” American Control Conference, Albuquerque, NM, 1997, pp. 97-100.

trend was reversed for the unshaped runs when more people chose to go the long way instead. For the shaped run 46% of the total runs were made through the long route whereas for the unshaped run 77% of the total rum were made through the long route. 5.

[8] D. Lewis, G. G. Parker, B. Driessen, and R. D. Robinett, “Command shaping control of an operator-inthe-loop boom crane,” Amencan Control Confemce, Philadelphia, Pennsylvania, 1998, pp. 2643-2647.

CONCLUSIONS

The actions of numerous bridge crane operators [9] R. Robinett, 6. Parker, J. Feddema, C. Dohrmann, were videotaped and the change in their performance and B. Petterson, ”Sway control method and system and behavior while using input shaping was recorded. for rotary cranes,” Patent 5,908,122, 1999. The results indicate that operators can move through obstacle fields much quicker when input shaping is [lo] G. P. Starr, “Swing-freetransport of suspended objects with a path-controlled robot manipulator,” J. utilized. Furthermore, the operators have greater of Dynamic Systems, Measurement and Control, vol. confidence in their maneuvering capabilities. It was 107, pp. 97-100, 1985. observed that crane operators took more complex and efficient maneuvering paths when the input shaper [11] W. E. Singhose, W. P. Seering, and N. C. Singer, “Input shaping for vihration reduction with specified was enabled on the crane. Also, although learning insensitivity to modeling errors,” Japan- USA Sym. helped, the initial data indicates that input shaping on Flexible Automation, Boston, MA, 1996. played a bigger role in improving performance. [12] P. Albertos and A. Sala, “On-line learning control

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ACKNOWLEDGEMENTS

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This project would not have been possible without the generous support of Siemens Energy and Automa- [13] M. Arif and H. Inooka, “Iterative manual control tion and specifically Eddie Prince. They provided the model of human operator,” Bioloyical Cybernectics, technical support and physical hardware necessary t o vol. 81, pp. 445455, 1999. implement input shaping on the bridge crane. [14] I. Bratko and T. Urbancic, “Transfer of control skill by machine-learning,” Engineering Application ntell, References vol. 10, no. 1, pp. 63-71, 1997. 111 L. BainBridge, “Ironiesof automation,” Automatica, vol. 19, no. 6, 1983. [2] J. Stahre, “Evaluating humanlmachine interaction problems in advanced manufacturing: CIM Syst e m , vol. 8, no. 2, pp. 143-150, 1995. [3] S. Yoshimura and H. Takayanagi, “Study on modelling of operator’s leaning mechanism,” IEEE International Conference on S y s t e m , Man: and Cybernetics, vol. 3, 1999, pp. 721-726.

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