Abstract Imaging techniques such as X-ray, computerized tomography scan and magnetic resonance imaging are useful in the correct diagnosis of a disease or deformity in the organ. Two-dimensional imaging techniques such as X-ray give a clear picture of simple bone deformity but fail in visualizing multiple fractures in a bone. Moreover, these lack in providing a multi-angle view of a bone. Three-dimensional techniques such as computerized tomography scan and magnetic resonance imaging present a correct orientation of fracture geometry. Computerized tomography scan is a collection of multiple slices of an image. These slices provide a fair idea about a fracture but fail in the measurement of correct dimensions of a fractured fragment and to observe its geometry. It also exposes a patient with carcinogenic radiations. Magnetic resonance imaging induces a strong magnetic field. So, it becomes ineffective for organs containing metallic implants. The high cost of three-dimensional imaging techniques makes them inaccessible for economic weaker section of society. The limitations of two- and three-dimensional imaging techniques motivate researchers to propose an innovative machine learning model ‘CT slices to $3$-D convertor’ that accepts multiple slices of an image and yields a multi-dimensional view at all possible angles from 0 degree to 360 degree for an input image.

Constraint force minimization is essential to improve the dynamic performance of an industrial manipulator. An optimization method is proposed to minimize the constraint forces using the concept of dynamically equivalent system of point-masses. It is shown that for the six-DOF PUMA robot the constraint forces are substantially reduced.

A systematic methodology for the constraint forces analysis for designated joints is developed in a serial industrial manipulator. In the method, the system equations of motion are derived using the recursive Newton-Euler equations while constraint forces are determined non-recursively for designated joints. To validate the method, the six-DOF Stanford and PUMA robots are analysed. Accuracy of the results are compared using RIDIM (recursive inverse dynamics industrial manipulator), an in-house software. The same results are obtained from the proposed method and from the RIDIM

A matrix formulation of the constraint forces and moments, together referred here as the wrenches, for serial manipulators is presented in this paper. The methodology is based on the Newton-Euler dynamic equations of motions of the manipulator at hand. The formulation provides joint forces and moments that are necessary for designing the robot links, as well those required by the joint actuators. The latter information is required in inverse dynamics. The formulation is based on writing the kinematic constraint equations using the Decoupled Natural Orthogonal complement (DeNOC) matrices, introduced elsewhere. The kinematic information in the form of the DeNOC matrices of a serial manipulator is used for the determination of the joint torques and forces, which lead to the development of a uniform analytical tool for control and design.