This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot.

SUMMARY: In typical calibration methods (kinematic or non-kinematic) for serial industrial robot, though measurement instruments with high resolutions are adopted, measurement configurations are optimized, and redundant parameters are eliminated from identification model, calibration accuracy is still limited under measurement noise. This might be because huge gaps still exist among the singular values of typical identification Jacobians, thereby causing the identification models ill conditioned. This paper addresses such problem by using new identification models established in two steps. First, the typical models are divided into the submodels with truncated singular values. In this way, the unknown parameters corresponding to the abnormal singular values are removed, thereby reducing the condition numbers of the new submodels. However, these models might still be ill conditioned. Therefore, the second step is to further centralize the singular values of each submodel by using a matrix balance method. Afterward, all submodels are well conditioned and obtain much higher observability indices compared with those of typical models. Simulation results indicate that significant improvements in the stability of identification results and the identifiability of unknown parameters are acquired by using the new identification submodels. Experimental results indicate that the proposed calibration method increases the identification accuracy without incurring additional hardware setup costs to the typical calibration method.

To address the complex challenge of identifying the contact state between a shaft and a hole and to improve the efficiency of robotic shaft-hole assembly tasks, a robotic shaft-hole assembly method based on variable admittance control is proposed. In this method, admittance control serves as the foundational force controller for shaft-hole assembly. On this basis, the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm from deep reinforcement learning is utilized to optimize the parameters of the admittance controller. Additionally, a nonlinear reward function is designed, which not only prevents the assembly strategy from converging to local optima but also further accelerates the training speed of the assembly task. Experiments conducted with a collaborative robotic arm performing 15° inclined hole assembly demonstrated that the assembly efficiency of the variable admittance method was 9.6% higher than that of the fixed admittance parameter method, validating the feasibility and effectiveness of the proposed variable admittance shaft-hole assembly method.

This study is motivated by the clinical need for surgical navigation in minimally invasive arthroscopic surgery. An example of minimally invasive arthroscopic surgery is osteochondral autograft transplantation (OAT),¹ which is an option for repairing focal cartilage defects in the knee ioints. OAT is a useful treatment for small osteochondritis dissecans ($<2{\mathrm{cm}}^2$)^2,3 and works by replacing a focal cartilage defect area with one or more osteochondral autografts generally harvested from the non-weight-bearing area of the patient’s healthy cartilage and bone. OAT makes small incisions and thus is usually performed arthroscopically and is minimally invasive. In contrast, mosaicplasty is a surgical technique that involves inserting three or more small plugs of healthy cartilage and bone from a non-weight-bearing site to fill larger areas of cartilage defects, forming a mosaic appearance.^4,5 Mosaicplasty is usually performed through open incisions, which is more invasive and requires a longer recovery time. OAT can be conducted by open or arthroscopic procedures. Although an open procedure has better visibility of the surgical field and it enables surgeons to get direct access to almost all articular lesions, open OAT has not shown superior clinical outcomes. In a cadaveric study,⁶ open and arthroscopic procedures for plug placement were comparatively conducted, and results showed no significant difference regarding accuracy and precision between the two techniques. Hence, arthroscopic OAT is the most commonly employed method.