Robotic Assembly by Slight Random Movements

Badano, Fernand; Bétemps, M.; Redarce, Tanneguy; Jutard, A.

doi:10.1017/s0263574700015538

Cited by 29 publications

(7 citation statements)

References 4 publications

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“…In Badano et al (1991), a different approach to robotic assembly is used. They show experiments for a planar peg-inthe-hole assembly in which the position error between the peg and the hole is compensated for by slight random movements.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Models as a Process Monitor in Robotic Assembly

Hovland

McCarragher

1998

The International Journal of Robotics Research

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A process monitor for robotic assembly based on hidden Markov models (HMMs) is presented. The HMM process monitor is based on the dynamic force/torque signals arising from interaction between the workpiece and the environment. The HMMs represent a stochastic, knowledge-based system in which the models are trained off-line with the Baum-Welch reestimation algorithm. The assembly task is modeled as a discrete event dynamic system in which a discrete event is defined as a change in contact state between the workpiece and the environment. Our method (1) allows for dynamic motions of the workpiece, (2) accounts for sensor noise andfriction, and (3) exploits the fact that the amount of force information is large when there is a sudden change of discrete state in robotic assembly. After the HMMs have been trained, the authors use them on-line in a 2D experimental setup to recognize discrete events as they occur. Successful event recognition with an accuracy as high as 97% was achieved in 0.5-0.6 s with a training set size of only 20 examples for each discrete event.

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Section: Introductionmentioning

confidence: 99%

Hidden Markov Models as a Process Monitor in Robotic Assembly

Hovland

McCarragher

1998

The International Journal of Robotics Research

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“…The uncertainty in the sensory feedback from the Zebra Zero is also significantly higher than that assumed in [l] or [3]. The positional uncertainty, in particular, is of the order of 10 to 100 times the clearance between the peg and the hole and is primarily due to gear backlash.…”

Section: Discussionmentioning

confidence: 90%

“…Although a passive remote-center-compliance (RCC) wrist is sufficient t o ensure successful peg-in-hole insertions when the hole or the peg is chamfered, highprecision mating of unchamfered parts is a topic of current research. In the latter case, some success has been achieved using special vibrating wrists [ll] or using slight random planar movements [3], and by using a learned assembly strategy [l]. In these cases, however, motion of the peg was restricted to reduce the number of controlled degrees of freedom, for example, by allowing only translations while keeping the orientation of the peg fixed.…”

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confidence: 99%

Learning admittance mappings for force-guided assembly

Gullapalli

Barto

Grupen

Proceedings of the 1994 IEEE International Conference on Robotics and Automation

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We present a practical method for autonomous synthesis of appropriate admittance behavior for robust high-precision robotic assembly. Because our approach relies on on-line learning of the appropriate admittance through repeated attempts at the assembly operation, we are able to circumvent the problems alternative approaches have in trying to model the interactions between the robot and its environment. Test results on the peg-in-hole insertion task show that the performance of our approach compares favorably with that of other methods recently proposed for highprecision chamferless peg-in-hole insertion.A primary concern in robotic assembly is to overcome part misalignment that can cause the assembly operation to fail. Precision assembly operations are particularly susceptible to misalignment. As the clearance between parts decreases, part misalignments become increasingly critical because the precision required approaches the level of uncertainty in positioning the parts. In such cases, conventional position control of the assembly operation has to be augmented with some method of overcoming possible misalignments. One approach is to use geometric path planning techniques to determine a motion sequence that has the highest likelihood of success given the part geometries and the margins of uncertainty in executing that sequence. Pre-imaging [12], backprojection [B], and other strategies (e.g., [5, 71) have been proposed for geometric path planning.Alternative approaches, based on reactive control, try to counter the effects of uncertainty with on-line modification of the assembly path based on sensory feedback. Compliant motion control, in which the motion path is modified by contact forces or tactile stimuli occurring during the motion, is often used. In this paper, we focus on force-guided assembly strategies that cause the manipulator to deviate from its nominal motion path depending on the sensed contact forces.Force control serves two purposes. First, it can be used to prevent excessive contact forces from damaging the parts or the manipulator during assembly. More importantly, force control can correct part misalignment by mapping contact forces arising from misalignment to motions that reduce the misalignment.A key component in implementing force-guided assembly therefore is to specify the manipulator's admittance, which determines how the manipulator responds to forces. The specified admittance behavior can be realized in several ways. Passive mechanical devices such as the remote center compliance (RCC)[18] can be used to implement fixed diagonalized linear admittance mappings. When different admittances are to be used for assembling different parts, more elaborate active compliance devices can be programmed to exhibit the desired admittance characteristics. The admittance also can be implemented in software, which has the advantage that complex, nonlinear admittances can be realized without resorting to special hardware.The difficulty, however, lies in determining the appropriate admittance to use when...

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“…Peg insertion task has a long history in the field of robotics and automation research [2,3,4,5,6,7,8,9,10,11,12,13,14]. It is one of the most commonly used goal tasks.…”

Section: Related Previous Workmentioning

confidence: 99%

“…This section reviews some of the previous work. Badano et al [6] introduced random motion to the work-table in order to absorb the positioning error between parts to be inserted. The clearance they use is about 15 µm.…”

Section: Related Previous Workmentioning

confidence: 99%