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Active Sensorimotor Augmentation in Robotics-Assisted Surgical Systems
Published in Terry M. Peters, Cristian A. Linte, Ziv Yaniv, Jacqueline Williams, Mixed and Augmented Reality in Medicine, 2018
Seyed Farokh Atashzar, Michael Naish, Rajni V. Patel
The third category of computerized robotic technology used for surgical procedures is GCH-RAS systems. This technology is an interactive robotic device that has a grounded kinematic chain. The end-effector of the robot is connected to a surgical tool and provides a sensorized gripping mechanism for the surgeon. The mechanism measures force applied by the surgeon as an indication of the intended motion profile. As a result, the robot can detect the surgeon’s intention and can react to the intended motions by providing corrective forces to enhance the surgical outcomes. Consequently, the final motion of the tool will be affected by both the actuation provided by the surgeon and the one provided by the robotic system. The cooperative control architecture of this technology is usually a composite method. It includes (a) an admittance control technique to convert the measured forces generated by the surgeon to appropriate actions and (b) a corrective algorithm, such as a virtual fixture, to assist the surgeon in following a registered surgical trajectory. This technology realizes a form of intelligent cooperation between the robot and the surgeon and has been reported as a “very convenient and natural [11] form of control.”
Methodologies of Feature Representations
Published in Awais Ahmad Khan, Emad Abouel Nasr, Abdulrahman Al-Ahmari, Syed Hammad Mian, Integrated Process & Fixture Planning, 2018
Awais Ahmad Khan, Emad Abouel Nasr, Abdulrahman Al-Ahmari, Syed Hammad Mian
In the variant fixture planning approach, the part is represented in the form of a feature model, and these features are used to retrieve the previous fixture design cases and develop a new, improved fixture design. Zhou et al. [40] presented a variant fixture planning approach, but the generative approach in modular fixture design is more advantageous than the variant approach as it is capable of producing plans for components that do not belong to any existing part family and also without any human intervention [52]. By using the artificial intelligence technique in variant fixture planning, such as CBR methods [46–51], the planning quality is increased by indexing the fixture cases through similarity. Similar fixture cases are retrieved and adapted to a new, improved design. By using artificial intelligence in generative fixture planning, the KBR techniques [41,42,44] and blackboard framework [57] can successfully be implemented in the fixture design system. VR is now facilitating the fixture designers to accomplish the entire design process for modular fixtures within the Virtual Environment. Current trends in the fixture design process are based on the VR concept. VR-based solutions facilitate a better 3D viewing as compared to CAD-based software. The main advantage of a VR system is the capability of simulating the various physical behaviors for virtual fixture elements [58–60].
Enhanced teleoperation performance using hybrid control and virtual fixture
Published in International Journal of Systems Science, 2019
Jing Luo, Chenguang Yang, Ning Wang, Min Wang
It is difficult to provide sufficient real-time perception for a teleoperation system. Moreover, due to the unskilful operation and muscle physiological tremor of the human operator, the natural performance cannot guarantee secure operation (Li, Kang, Xiao, & Song, 2017; Liu, Mao, Luo, Zhang, & Chen, 2014; Liu et al., 2015; Zhao et al., 2017). Thus, it is important to enhance the interaction capability of the teleoperation system. Virtual fixture is an alternative method to improve teleoperation performance. Virtual fixture was first proposed to extract relevant information between the human operator and the remote environment for HRI (Rosenberg, 1993). In Fehlberg, Nisky, Doxon, and Provancher (2014), a virtual fixture control strategy was presented to improve the manipulation performance of the active handrest. Becker, MacLachlan, Lobes, Hager, and Riviere (2013) developed a derivation of virtual fixtures based on the motion of the instrument in real time for system control. In Hong and Rozenblit (2016), the authors proposed a forbidden region virtual fixture with robust fuzzy logic controller to improve the human manipulation performance during laparoscopic surgery. A virtual fixture method based on the position error was presented to add an augmentation force on the master device to improve the task quality (Maddahi & Zareinia, 2015). Selvaggio et al. (2016), proposed an online virtual fixture and task switching mechanism that utilises a stereo camera system to provide position information, thus improving teleoperation performance. In Quintero, Dehghan, Ramirez, Ang, and Jagersand (2017), a flexible virtual fixture method with force-vision-based scheme was developed to reduce cognitive load and improve the task performance.
Preliminary testing by adults of a haptics-assisted robot platform designed for children with physical impairments to access play
Published in Assistive Technology, 2018
Isao Sakamaki, Kim Adams, Maria Fernanda Gomez Medina, Javier Leonardo Castellanos Cruz, Nooshin Jafari, Mahdi Tavakoli, Heidi Janz
Haptic interfaces can create virtual fixtures (VFs): software-generated forces applied by the robotic interface (Abbott et al., 2007). Guidance VFs assist in guiding the robot along a desired area, while forbidden region VFs help to keep the robot inside (or outside) a defined region.