Efficient and situated design of haptic devices

It is a challenging task to develop and optimize a high-performing haptic device for, e.g., medical training, because of the multi-objective performance requirements, and the complex relations between the design variables and the performance objectives. Consequently, the solution space is scrutinized with efficient optimization techniques, and the cause-effect relations are preferably represented in a way that enables further reasoning in a multi-discipline setting, e.g., as Pareto-front curves and surfaces. Furthermore, if we have found a Pareto-optimal solution, how can we benefit from the knowledge and optimization results obtained in that process if we are facing a trailing challenge (a new design situation) to re-design the initial solution to another application and/or other requirements, i.e. how can we address this new situation by efficiently re-using as much as possible of the initial solutions? In this paper, we first present multi-objective optimizations of a 6-degree-of-freedom TAU haptic device. To investigate how we efficiently can address new design situations, by making use of the initial solutions, we have defined, solved and analyzed six re-design cases. For the different cases, it is shown what can be reused and in what way that can be done. The six studied cases are then analyzed and a generic process for situated design optimization, based on available knowledge and Pareto-optimal solutions, is proposed.