Background: In the process of fracture reduction, there are some errors between the actual trajectory and the ideal trajectory due to mechanism errors, which would affect the smooth operation of fracture reduction. To this end, based on self-developed parallel mechanism fracture reduction robot (FRR), a novel method to reduce the pose errors of FRR is proposed. Methods: Firstly, this paper analyzed the pose errors, and built the model of the robot pose errors. Secondly, mechanism errors of FRR were converted into drive bar parameter’s errors, and the influence of each drive bar parameter on the robot pose error were analyzed. Thirdly, combining with Cauchy opposition-based learning and differential evolution algorithm (DE), an improved whale optimization algorithm (CRLWOA-DE) is proposed to compensate the end-effector’s pose errors, which could improve the speed and accuracy of fracture reduction, respectively. Results: The iterative accuracy of CRLWOA-DE is improved by 50.74%, and the optimization speed is improved by 22.62% compared with the whale optimization algorithm (WOA). Meanwhile, compared with particle swarm optimization (PSO) and ant colony optimization (ACO), CRLWOA-DE is proved to be more accurate. Furthermore, SimMechanics in the software of MATLAB was used to reconstruct the fracture reduction robot, and it was verified that the actual motion trajectory of the CRLWOA-DE optimized kinematic stage showed a significant reduction in error in both the x-axis and z-axis directions compared to the desired motion trajectory. Conclusions: This study revealed that the error compensation in FRR reset process had been realized, and the CRLWOA-DE method could be used for reducing the pose error of the fracture reduction robot, which has some significance for the bone fracture and deformity correction.

The main joints of lower limb are hip, knee, ankle, and toe. Because of accidents or diseases, some people have to amputate. Wearing prosthesis can help them return to normal life. In the past, Active Knee joint and Active Knee-Ankle transfemoral prosthesis are studied. While in the stance phase, toe joint play an important role. In this article, the dynamic model and control method of Knee-Ankle-Toe Active Transfemoral Prosthesis are studied. The main contents are as follows: Firstly, according to the ground reaction force, the gait cycle is divided into three phases, Early and Middle Stance Phase (E&MSP), Late Stance Phase (LSP) and Swing Phase (SP), and the mechanical model of Knee-Ankle-Toe Active Transfemoral Prosthesis (KATATP) is established. Secondly, consider the cooperative movement of amputee and prosthesis, the kinematics model of prosthesis is established by Lie Groups and Lie Algebras, and the dynamic model of prosthesis is established by Lagrange equation. Thirdly, the whole gait cycle is a complete system and each phase is a different subsystem, switching rules and controllers of different subsystems are designed. Finally, experiments show that KATAP with the switching control enables amputee to have a biomechanically appropriate walking gait and achieve smooth transition between different gait phases.

ArUco is an efficient and convenient method to estimate pose of the robot or the object by detecting corner points. However, ArUco cannot be used in some cases due to its lack of detection stability and insufficient accuracy. In this paper, an improved monocular 6D pose estimation method based on the spatial icosahedron marked by ArUco is proposed. By identifying the marked points on the surface of the icosahedron in the image, matching the 3D points in the database and solving with the Perspective-n-points(PnP) method, the accurate 6D pose estimation of the robot can be obtained. Compared with the original ArUco, the method proposed in this paper can cope with larger pose changes, provide higher accuracy, and improve the detection stability, while the time complexity is not significantly increased. In most cases, the method proposed in this paper can achieve translation accuracy of sub-millimeter level and rotation accuracy of less than 1°. Besides, it realized the ±180° large-scale rotation detection of three axes, and the real-time indicators is also satisfied.