| dc.description.abstract | In this article, we present a framework that automatically selects a modular manipulator structure based on a user-defined task involving 3D trajectory tracking, handling heavy payloads with precision, and navigating around obstacles. For such tasks, hybrid structures combining serial and parallel components may offer advantages, leveraging the large reachable space of serial elements and the rigidity of parallel components for enhanced accuracy. Given the challenges in developing a comprehensive method for kinematic design and dynamic analysis of hybrid robots, existing works often constrain structures to 2D space or optimize predefined initial designs. Our approach explores both serial and hybrid structures, incorporating modular components such as open and closed loops. By widening the solution space, we uncover designs for tasks previously considered unfeasible due to structural constraints.We formulate task-to-manipulator matching as a constrained optimization problem in kinematics and statics, yielding both design and control solutions. To validate our approach, we demonstrate its feasibility through the physical implementation of manipulators capable of performing complex tasks. | |
