This paper presents a novel truss-shaped variable-stiffness deployable robotic grasper to grasp large unknown objects, the grasper comprises a series of basic modules and has the advantages of a large workspace, adjustable stiffness, and a high deploy/fold ratio. First, detailed mechanism designs of the grasper and variable-stiffness joint are introduced, and a mobility analysis and variable stiffness analysis are conducted. Second, the structural analysis of the basic module is carried out, by which several major indices, including deploy/fold ratio, grasping angle, deployment angle, grasping torque, and deployment torque, can be calculated. Third, kinematic analysis is presented to provide the workspace and kinematic simulation, and then the joint trajectory planning based on fifth-order polynomial is also conducted. Fourth, the condition of stable grasp is analyzed and a mathematical model of grasping motion is established. Adam optimization algorithm is then applied to optimization of the mathematical model and a grasping simulation is performed with objects of various sizes and for various working states of the variable-stiffness joint. Finally, a simple physical prototype is fabricated, and variable-stiffness experiments and grasping experiments confirm that the proposed grasper shows excellent grasping performance.

In this paper, a particle swarm optimizer that integrates self-organizing maps and k-means clustering (SK-PSO) is proposed. This optimizer generates an asymmetric Cartesian space from random joint configurations when addressing the inverse kinematics of manipulators, followed by K-means clustering applied to the Cartesian space. The resulting clusters are used to reduce the dimensionality of the corresponding joint space using Self-Organizing Maps (SOM). During the solving process, the target point’s clustering region is determined first, and then the joint space point closest to the target point is selected as the initial population for the particle swarm algorithm. The simulation results demonstrate the effectiveness of the SK-PSO algorithm. Given the inherent asymmetry among different algorithms in handling the problem, SK-PSO achieves an average fitness value that is 0.02–0.62 times better than five other algorithms, with an asymmetric solving time that is only 0.03–0.34 times that of the other algorithms. Therefore, compared to the other algorithms, the SK-PSO algorithm offers high accuracy, speed, and precision.

In order to grasp large-scale unknown objects, a novel deployable robotic grasper is presented in this paper, which is composed of a train of basic metamorphic mechanism modules. Firstly, a detailed mechanism design is introduced, in which a scissor-shaped mechanism is used to design basic module so that it has single deployment mobility and a particular metamorphic mechanism is applied to change its mobility from deployment motion to grasping motion. Then, metamorphic process, mobility analysis, and singularity analysis of the basic module are conducted and the assembling of the grasper is illustrated. Secondary, the deploy/fold ratio and its influencing factors are discussed. Thirdly, the workspace of the grasper is demonstrated, which shows that the grasper has large reachable workspace. In addition, kinematic equation and dexterity of the proposed grasper are also presented. Finally, the shape adaptability and grasping simulation are conducted, which shows that the grasper has good grasping performances and can be used to grasp large-scale unknown objects.