Abstract-Parallel continuum manipulators are the main research focus these days. Parallel manipulators have other advantages than serial manipulators in terms of acceleration and positioning. In this work, we proposed the use of two Compact Bionic Handling Arm (CBHA) manipulators, attaching them to a common platform, to constitute a parallel continuum manipulator. A relation is developed for optimal platform length as a function of the inter-distance between bases of two arms, by using optimization of the volume of the common work space realized from multiple CBHA. The work is extended to parallel continuum manipulator consisting of three CBHA manipulators. Same for this case, after identifying work space of this parallel continuum manipulator, a relation is developed between different design parameters of manipulator using optimization of volume of its work space. As the work space is the main limitation in case of parallel manipulators, this approach to generate relation is useful for rigid as well as continuum manipulators to design them for optimal work space as per required application.

Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.

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Fault detection is one of the key steps in Fault Detection and Isolation (FDI) and, therefore, critical for subsequent prognosis or implementation of Fault Tolerant Control (FTC). It is, therefore, advisable to utilize detection algorithms which are quick and can detect the smallest faults. Model-based detection methods satisfy both these criteria and should be preferred. However, a big limitation for model-based methods is that they require the accurate value of the component parameters, which is difficult to obtain in real situations. This limits the accuracy of model-based methods. This paper proposes a new method for fault detection using Energy Activity (EA) which can detect minute levels of fault in systems with high component uncertainty. Different forms of EA are developed for use as an FDI metric. The proposed forms are simulated using a two-tank system under various types of faults. The results are compared with each other and with the traditional model-based FDI method using Analytical Redundancy Relations (ARRs). The simulations are performed considering model uncertainties to check the inherent performance of the methods. From initial simulations, it is established that the integral form of EA is most suited for fault detection. The integral for if EA is then tested using a real two-tank system considering both the model and measurement uncertainties.