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Virtual fixtures

JHU project w/NeuroMATE Co-operative control Johns Hopkins University SkuU base drilling with force based co-operative control with virtual Fixtures... [Pg.478]

Rosenberg, L.B. Virtual fixtures Perceptual tools for telerobotic manipulation. In Proceedings of the IEEE Virtual Reality Annual International Symposium, pp. 76-82 (1993)... [Pg.485]

Abbott, J.J., Marayong, P., Okamura, A.M. Haptic virtual fixtures for robot-assisted manipulation. Springer Tracts in Advanced Robotics 28,49-64 (2007)... [Pg.485]

Li, M., Ishii, M., Taylor, R.H. Spatial motion constraints using virtual fixtures generated by anatomy. IEEE T. Robot. 23(1), 4-19 (2007)... [Pg.485]

Li, M., Okamura, A.M. Recognition of operator motions for real-time assistance using virtual fixtures. In Proceedings of the 11th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 125-31(2003)... [Pg.485]

Another human-machine interaction class is the active constraint, also called virtual fixture, virtual wall, and electronic tunnel. The concept of active constraints was first introduced by Rosemberg [17] as an overlay of abstract sensory information on a work space in order to improve human performance in direct and remotely manipulated surgical tasks in order to increase the system safety. Recently, Bowyer et al. [18] presented a classification of different types of constraints in regional/guidance, attractive/repulsive, and unilateral/ bilateral constraints, hi case of regional and repulsive constraints, the system control prevents the robot to overcome predefined space regions either if the robot is manually driven (i.e., the controller nullifies the motion that violates a constraint applying a force to the user) or if the robot is remotely operated (only motion components which do not violate a constraint are allowed by the controller). [Pg.11]

Bowyer SA, Davies BL, and Rodriguez y Baena F, Active constraints/virtual fixtures A survey, IEEE Transactions on Robotics, vol. 30 1, pp. 138-157, 2013. [Pg.29]

Gibo TL, Verner LN, Yuh DD, and Okamura AM, Design considerations and human-machine performance of moving virtual fixtures, IEEE International Conference on Robotics and Automation, pp. 671-676,2009. [Pg.29]

A. Kapoor, M. Li, and R. H. Taylor, "Spatial motion constraints for robot assisted suturing using virtual fixtures," Med. Image Comput. Comput. Assist. Interv., 8(part 2) 89-96,2005. [Pg.86]

RIO , developed by MAKO Corp. (Figure 5.15) and approved by the FDA, is a hands-on robotic tool mainly for medial and lateral uni-condylar knee components and pateUofemoral arthroplasty [45]. RIO is designed to have a low friction and be easily back-drivable. By means of virtual fixtures, the robot assists the surgeon to stay on the preplanned paths during the milling process. [Pg.104]

Bettini A, Marayong P, Lang S, Okamura AM, Hager GD. Vision-assisted control for manipnlation using virtual fixtures. IEEE Trans Robotics 2004 20(6) 953-66. [Pg.111]

Park S, Howe RD, Torchiana DF. Virtual fixtures for robotic cardiac surgery. In Medical image computing and computer-assisted intervention—MICCAI 2001. Springer 2001. p. 1419-20. [Pg.111]

When processing plastics some type of tooling is usually required. Tools include molds, dies, mandrels, jigs, fixtures, punch dies, perforated forms, etc. The terms for tools are virtually synonymous in the sense that they have some type of female and/or negative cavity into or through which a molten plastic moves usually under heat and pressure or they are used in secondary operations such as cutting dies, stamping sheet dies, etc. These tools fabricate or shape products. In this chapter injection molds and extrusion dies are primarily reviewed because they represent over 95% of all tools made for the plastic industry. This chapter also includes information applicable to other molds and dies used in the other processes some of the other chapters too provide information applicable to their tools. [Pg.512]

A few boards are tested, and, if results appear reasonable, the program is considered valid. The key drawback of the method is that self-learned test programs determine that all the boards are the same, not that they are good. Moreover, for economic fabrication of the test fixture, it is necessary to process product data anyway thus we may as well have output a data-driven test program. With virtually 100 percent of board designs being CAD-driven, there is little motivation to use self-learning today. [Pg.864]

To enable grid test points to be translated to exactly the correct target location on the product, it is important that each translator pin fall into the correct sequence of holes while the fixture is being assembled. If sufficient intermediate guide plates are employed, then the geometry of drilled holes can be so arranged that a virtual tunnel is created for each... [Pg.875]

Tool When processing plastics some type of tooling is required. Tools include molds, dies, mandrels, jigs, fixtures, punch dies, perforated forms, etc. These tools febricate or shape products. They fit into the overall flow chart in fabricating plastic products. The terms for tools are virtually... [Pg.540]


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See also in sourсe #XX -- [ Pg.93 ]




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