dimension optimization method for manipulator design considering load direction

Robots are often used to perform arduous tasks in fields such as nursing care, construction, and space exploration. These applications necessitate the use of manipulators with high load-carrying capacities. However, conventional manipulator designs generally fail to consider the directional aspects of kinematic and dynamic performance indices. Thus, the load-mass ratios of existing manipulators are limited in various engineering applications. This paper proposes a dimension optimization via global visualization (DOGV) method considering the direction of load-carrying capacity. Through a redundantly actuated parallel manipulator (RAPM) used in the construction robot system, the influence of the load weight on the dimension design of the manipulator during material handling is studied. Due to the redundant actuation characteristics of the manipulator and the existence of a constrained limb, it becomes challenging to directly establish the performance evaluation index of the manipulator. Based on the screw theory, this paper constructs the complete generalized Jacobian matrix and the dimensionless Jacobian matrix of the RAPM by independently modeling the active limb in parallel and the passive limb in series. Aiming at the low-speed and heavy-load working situations, the load-carrying capacity performance index of the manipulator is investigated considering the direction of load weight. The proposed DOGV method identifies the optimal dimension parameters that best fit practical applications from two different dimensions. To validate the effectiveness of the proposed DOGV method, the performance indices such as workspace, dexterity, load-carrying capacity, and stiffness are comprehensively compared and analyzed. Numerical results demonstrate the substantial impact of the DOGV method on enhancing load-carrying capacity. This study lays the foundation for the development of novel high-performance manipulators for material handling.