The precise control of manipulators with high joint-friction using base force/torque sensing☆
Introduction
Precise position and force control is required for many important applications, including the assembly of precision-component systems, micro-manipulation, and robotic surgery (Ku & Salcudean, 1996). These applications often require very small motions at low speeds. Under such conditions, the degrading effects of joint, actuator and transmission friction can dominate system behavior, making precise position and endpoint force control of robotic manipulators difficult to achieve.
The problem is compounded when the manipulator must move heavy objects at slow speeds with high precision. The important problem of nozzle dam placement in nuclear power plant maintenance is such an application (Zezza, 1985). In this application, an hydraulic or highly geared electrical manipulator is required to precisely place a large, heavy nozzle dam. In hydraulic manipulators, joint seal friction is often very high, and likewise in highly geared electrical manipulators, transmission friction is often high. Hence, obtaining precise motion and force control in these systems is very difficult.
Substantial research has been devoted to improving friction-degraded manipulator performance. Direct-drive electrical robots have been proposed and developed that have relatively low joint friction (Asada & Youcef-Toumi, 1987). However, these systems are not appropriate for applications that require the applications of large forces. Also, direct-drive actuators are heavy compared with geared actuators, and thus are not appropriate for applications that require lightweight manipulators, such as space applications (Schenker et al., 1997).
Control methods have also been proposed to reduce the effects of joint friction. Some methods incorporate a mathematical model of friction (Canudas de Wit, 1988). An estimate of frictional forces provided by the model is used either in feedforward or feedback compensation (Armstrong, 1991; Popovic, Shimoga & Goldenberg, 1994). These methods require a precise model. Unfortunately, friction is a highly complex, nonlinear phenomena that can depend upon numerous factors, including joint position, load, temperature, and wear (Armstrong, 1991). These factors can be challenging to estimate or measure, and thus model-based friction compensation remains difficult to implement in practical applications.
To overcome modeling difficulties, adaptive methods have been proposed (Canudas de Wit, Olsson, Astrom & Lischinsky, 1996). Dither has also been utilized to improve low-speed positioning performance (Ipri & Asada, 1995). A method that utilizes short-duration torque pulses computed from fuzzy-logic reasoning has also been studied (Popovic, Gorinvesky & Goldenberg, 1995). This approach can be effective in applications where only the final end-effector position is critical. The method cannot control the trajectory that a manipulator takes to reach this final position.
Finally, measurement-based friction compensation methods have been studied (Luh, Fisher & Paul, 1983; Pfeffer, Khatib & Hake, 1989; Vischer & Khatib, 1995). In these methods, the torque applied to a manipulator's links is measured and used as the feedback signal in a torque control loop. Friction is an output disturbance to this control system. With sufficient gain and bandwidth, the torque controller can reject frictional effects. These measurement-based methods have the advantage of being model-free, and have been shown to be effective in practice (Pfeffer et al., 1989; Vischer & Khatib, 1995). However, this approach requires torque sensors mounted at the joint transmission outputs. The use of “indirect sensing” at the actuator level (such as motor current measurements or differential pressure in hydraulic systems) is not appropriate for friction compensation, because the friction disturbance is not measured by these methods. Joint-torque sensors have the drawback of added cost, increased joint flexibility, and additional cabling and electronics. Their complexity can reduce system reliability. Finally, internal sensors must be included during the design of a manipulator, as it is difficult to add them to the existing systems.
In this paper, a new measurement-based joint friction compensation method is presented. The method, called base sensor control (BSC), uses a six-axis force/torque sensor placed under the base of the manipulator (Morel & Dubowsky, 1996; Iagnemma, 1997; Iagnemma, Morel & Dubowsky, 1997). From the measured forces and torques it is possible to calculate the net dynamic torque applied to the links of the manipulator. This measurement is uncorrupted by joint friction. These calculated torques are used in joint-torque controllers. This method eliminates the need for internal joint sensors with the practical problems described above.
The BSC method in its most general form requires dynamic and gravitational models of the system, but no friction model. Nonetheless, the dynamic and gravitational model calculations can be a burden. It is shown that a nearly model-free form of the method can be applied successfully in applications that require low-speed, small-amplitude motions. Experimental results are presented for low-speed, small-amplitude tasks that show that the reduced (i.e. model-free) method can achieve very fine performance. The reduced method requires only simple kinematic coordinate transformations to implement, and hence is easy to apply and does not require substantial computational resources. Results are presented for BSC control of a highly geared PUMA electric manipulator and a hydraulically powered Schilling Titan II manipulator. For both systems, low-speed small-motion performance is greatly improved, even while transporting heavy payloads.
Section snippets
Theoretical basis of BSC control
In this section, the equations for joint-torque estimation using a base force/torque sensor are developed.
Experimental BSC control of a Puma 550 manipulator
Fig. 3 shows a Puma 550 manipulator mounted on a base force/torque sensor. The manipulator is controlled by a single board 68020 VME computer supporting VxWorks, with a 300 Hz sampling frequency. The base sensor used in these experiments was a modified version of the AMTI OR6-1000 six-axis force/moment sensor (AMTI, 1995). The cost of sensors of this type is approximately 50% greater than the cost of a wrist force/torque sensor with equivalent performance, a small fraction of the cost of adding
Application of BSC control to a hydraulic manipulator
The Schilling Titan II is a six degree-of-freedom industrial hydraulic manipulator (see Fig. 10). It is used widely in undersea and nuclear applications because of its very high strength, low weight, and large workspace. However, it suffers from poor dynamic characteristics, largely due to high joint friction. Performance during small, slow motions is dominated by difficult-to-model nonlinear joint and actuator friction (Armstrong, 1991; Habibi, Richards & Goldenberg, 1994; Lischinsky, Canudas
Conclusions
In this paper a method for compensating for joint friction, using a six-axis force/torque sensor mounted under the manipulator is presented. A simplified form of this method is formulated, which depends only on feedback from the force/torque sensor and manipulator kinematic parameters. The simplified method is shown to greatly improve the positioning performance during fine-motion tasks of a both a highly geared electrical manipulator and an industrial hydraulic manipulator.
Acknowledgements
This work was partially supported by an NSF graduate fellowship, the Korean Electric Power Corporation, and Electricité de France.
Guillaume Morel is an assistant professor at the University Louis Pasteur — Strasbourg I, where he is involved with the CNRS/ULP Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection. He completed his Ph.D. at the Laboratoire de Robotique de Paris (University of Paris 6) and spent two years as a postdoctoral researcher at the Massachusetts Institute of Technology. His research interests include position and force control of robots, and visual servoing.
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Guillaume Morel is an assistant professor at the University Louis Pasteur — Strasbourg I, where he is involved with the CNRS/ULP Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection. He completed his Ph.D. at the Laboratoire de Robotique de Paris (University of Paris 6) and spent two years as a postdoctoral researcher at the Massachusetts Institute of Technology. His research interests include position and force control of robots, and visual servoing.
Karl Iagnemma received his B.S. in Mechanical Engineering summa cum laude from the University of Michigan in 1994, and his M.S. in Mechanical Engineering from the Massachusetts Institute of Technology in 1997, where he was a National Science Foundation Graduate Research Fellow. He is currently completing his Ph.D. in Mechanical Engineering at M.I.T. His research interests include mobile robot planning and control, mobile manipulation, and manipulator identification.
Steven Dubowsky received his Bachelor's degree from Rensselaer Polytechnic Institute of Troy, New York in 1963, and his M.S and Sc.D. degrees from Columbia University in 1964 and 1971. He is currently a Professor of Mechanical Engineering at M. I. T. He has been a Professor of Engineering and Applied Science at the University of California, Los Angeles, a Visiting Professor at Cambridge University, Cambridge, England, and Visiting Professor at the California Institute of Technology. During the period from 1963 to 1971, he was employed by the Perkin-Elmer Corporation, the General Dynamics Corporation, and the American Electric Power Service Corporation. Dr. Dubowsky's research has included the development of modeling techniques for manipulator flexibility and the development of optimal and self-learning adaptive control procedures for rigid and flexible robotic manipulators. He has authored or coauthored nearly 100 papers in the area of the dynamics, control and design of high performance mechanical and electromechanical systems. Professor Dubowsky is a registered Professional Engineer in the State of California and has served as an advisor to the National Science Foundation, the National Academy of Science/Engineering, the Department of Energy, and the US Army. He has been elected a fellow of the ASME and is a member of Sigma Xi, and Tau Beta Pi.
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The original version of this paper was presented at the 5H IFAC Symposium on Robot Control SYROCO’97, Nantes, France, 3–5 September 1997. This paper was recommended for publication in revised form by Associate Editor Y. Nakamura under the direction of Editor K. Furuta.
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Present address: The university of Strasbourg 1-ENSPS, Laboratoires des Sciences de l'lmage, de l'lnformatique et de la Télédetection, CNRS strasbourg, France.