Self-aligning mechanisms are essential components in facilitating adaptability in wearable robots, but their synthesis from scratch is very challenging. To overcome this hurdle, we propose a so-far-unprecedented autonomous method to synthesize self-aligning knee joint mechanisms, requiring neither a baseline design nor human intervention during synthesis. Our method transforms the synthesis problem into an optimization problem amenable to an efficient gradient-based algorithm using a discretized ground mechanism model. The main challenge in the conversion lies in how to define the objective and constraint functions in order to ensure the fundamental self-aligning capability and also to impose a desired force transmittance profile. Several design cases were considered to show the effectiveness of the newly proposed functions for the optimization-based synthesis formulation, notably in addressing degree-of-freedom requirements. Although this study focuses primarily on knee joint mechanisms assisting gait motion and aligning with the flexion axis, the developed method can be applied to other self-aligning robot mechanisms.

The transformable wheel mechanism of a tri-footed mobile service robot determines its driving performance. Currently, two independent actuators adjust the wheel's three feet adaptively, but we aim to develop novel single-actuator-operable 1-DOF transformable wheel mechanisms. Due to the unexplored nature of 1-DOF mechanisms, the trial-and-error approach may fail to find such mechanisms. Herein, we propose a fully automated gradient-based design approach that simultaneously determines the mechanism's topology, dimension, and joint type without a baseline mechanism. We take both fundamental kinematic and functional (such as torque-force transmission ratio) requirements into account. Our formulation employing a unified ground synthesis model is newly established to consider the trajectory and orientation of the mechanism end-effector, control the number of synthesized prismatic joints, and satisfy the torque-force transmission ratio. Under various design constraints, we successfully synthesized novel mechanisms, and their functionalities were validated via simulations and the motions of their prototypes.

This paper presents a novel two-step method for synthesizing spatial linkage mechanisms. Compared with planar mechanisms, the main challenge in synthesizing spatial mechanisms is that the generating motion varies depending on its mechanism topologies. Therefore, we propose a big data approach to determine the topology of spatial mechanisms. We adopt a three-dimensional (3D) spring-connected rigid block model to represent the topology of the spatial mechanism and project 3D motion onto three orthogonal planes to determine the mechanism topology with big data. In addition, a gradient-based dimension synthesis procedure was carried out to determine a detailed dimension using already determined mechanism topology by mechanism big data. Also, several successful case studies by the proposed approach are presented to support the effectiveness of the proposed synthesis method.

Hyper redundancy, high reliability, and high task repeatability are the main advantages of binary manipulators over conventional manipulators with continuous joints, especially when manipulators are operated under tough and complex work conditions. The precise and complex movement of a binary manipulator necessitates many modules. In this case, numerically efficient inverse kinematics algorithms for binary manipulators usually require impractically large memory size for the real-time calculation of the binary states of all joints. To overcome this limitation by developing a new inverse kinematics algorithm is the objective of this research. The key idea of the proposed method is to formulate the inverse kinematics problem of a binary manipulator as an optimization problem with real design variables, in which the real variables are forced to approach the permissible binary values corresponding to two discrete joint displacements. Using the proposed optimization method, the inverse kinematics of 3-D binary manipulators with many modules can be solved almost in real time (say, less than a second for up to 16 modules) without requiring a large memory size. Furthermore, some manipulation considerations, such as operation power minimization, can be easily incorporated into the proposed formulation. The effectiveness of the proposed method is verified through several numerical problems, including 3-D inverse kinematics problems.

The precise positioning of the end-effector of a binary manipulator requires many modules. In this case, most of the existing algorithms require impractically large memory size for the real-time calculation of the binary positions of all joints. To overcome this limitation, we propose a new inverse kinematics algorithm using an optimization method. The key idea of the present method is to formulate the integer-variable inverse kinematics problem as a real-variable optimization problem in which the real-valued design variables are pushed towards the permissible binary values. Since the proposed algorithm is very efficient, the inverse kinematics of three-dimensional manipulators can be solved in real time. Furthermore, some manipulation considerations such as operation power minimization can be easily incorporated into the present new formulation.