Design optimization of haptic device - A systematic literature review Article Swipe
Performance requirements for high-performing haptic devices are usually multi-criteria. Sometimes the requirements are interacting, and several of them are conflicting. Optimization is one of the main approaches to scrutinize the design space and to search for a design that satisfies all requirements. Many researchers have used and published optimization approaches to search for an optimal haptic device design. However, predicting the performance of a high-performing haptic device usually involves computationally intensive simulations and analyses with complex and heterogeneous models. In order to study what are the common design and performance requirements of haptic devices and what optimization approaches have been used to improve optimization effectiveness and efficiency, a literature review on the present state-of-the-art in these areas has been performed. The most commonly used performance requirements presented in the literature are the number of degrees-of-freedom, dynamic inertia, kinematic isotropy, stiffness, peak and continuous force, position/force resolution, and bandwidth. Furthermore, parallel and hybrid kinematic structures are more commonly used than serial structures. Multi-objective optimization (MOO) is a commonly used approach to simultaneously optimize all performance criteria. The most common optimization targets, as presented in published literature, are to maximize workspace, kinematic isotropy, as well as the peak force/torque provided by the device, and to minimize the dynamic inertia. Commonly used indices to constrain the design space are a minimum workspace, avoidance of singularities and motion limits of active and passive joints. The number of design variables varies from 2 to 9, and the most commonly used design variables are a set of mechanical parameters, such as the lengths and diameters of the mechanical components. To increase the efficiency of complex and multi-criteria optimization tasks, the Pareto-front approach combined with multidisciplinary design optimization (MDO) and metamodel techniques are recommended.
