Youyu Liu

To enable a polishing tool to automatically adjust its posture and adapt to the surface of the workpiece, an end-effector based on a parallel mechanism with 3-PSS + PS′ (prismatic–spherical–spherical + prismatic–sphere–pin) was designed by using the reverse adaptive working principle. Based on the spiral theory, the degrees of freedom (DOF) of the mobile platform in the parallel mechanism are analyzed, and its DOF are verified according to the modified Grübler–Kutzbach formula. According to the kinematic analysis, the relationship between the plunger movement distance and the grinding wheel spatial pose is determined. The polishing force function between the grinding wheel and workpiece is obtained according to the analysis of the spatial pose of the grinding wheel. The main structural dimensions of the model were determined, and the physical model was given. The simulation analysis of the designed structure is carried out; the results show that the end-effector can realize 3-DOF motion. The maximum rotation angle of the grinding wheel around the x-axis and y-axis is 7.68° and 6.68°, respectively, which meets the design requirements. Within the working range, without controlling the end of a manipulator, the grinding wheel can adaptively compensate the trajectory error, and for different materials 45 steel, HT-200, 6061, the polishing force fluctuation is within 2.56% after stable polishing, which is basically constant.

Laser point clouds are commonly affected by Gaussian and Laplace noise, resulting in decreased accuracy in subsequent surface reconstruction and visualization processes. However, existing point cloud denoising algorithms often overlook the local consistency and density of the point cloud normal vector. A feature map learning algorithm which integrates point normal constraints, Dirichlet energy, and coupled orthogonality bias terms is proposed. Specifically, the Dirichlet energy is employed to penalize the difference between neighboring normal vectors and combined with a coupled orthogonality bias term to enhance the orthogonality between the normal vectors and the subsurface, thereby enhancing the accuracy and robustness of the learned denoising of the feature maps. Additionally, to mitigate the effect of mixing noise, a point cloud density function is introduced to rapidly capture local feature correlations. In experimental findings on the anchor public dataset, the proposed method reduces the average mean square error (MSE) by 0.005 and 0.054 compared to the MRPCA and NLD algorithms, respectively. Moreover, it improves the average signal-to-noise ratio (SNR) by 0.13 DB and 2.14 DB compared to MRPCA and AWLOP, respectively. The proposed algorithm enhances computational efficiency by 27% compared to the RSLDM method. It not only removes mixed noise but also preserves the local geometric features of the point cloud, further improving computational efficiency.

Within the domain of mobile robot path planning, an Enhanced Grey Wolf Optimal (IGWO) algorithm emerges to address the drawbacks present in the conventional Grey Wolf Optimal approach, such as sluggish convergence and limited global search capabilities. By introducing a nonlinear convergence factor and incorporating an inertia weight showcasing disturbance characteristics, enhancements bolster the algorithm's convergence speed and stability. Integration of the search mechanism from the Whale Optimization Algorithm and the introduction of a new Spiral Disturbance Search Strategy work together to amplify the algorithm's global search prowess. Eight sets of test functions show that IGWO has the advantages of strong global search ability and high optimization accuracy. The improved algorithm is implemented for mobile robot path planning in a 20×20 grid model with a static environment using Matlab. Algorithm programs based on GWO, GA and IGWO were employed to simulate robot obstacle avoidance and compute the optimal path. Advantages and disadvantages are compared through 10 independent tests. The testing results indicated that compared with GWO and GA, success rate of planning optimal path by IGWO is increased by 50% and 40%, respectively. It validates the improved Grey Wolf algorithm's superiority and feasibility in addressing the path planning challenge for mobile robots.

Manipulators applied to polishing can avoid the harm of dust and noise to workers’ health in manual polishing, and can significantly increase productivity. The degrees of freedom (DOF) of the existing end-effector for manipulator polishing is insufficient, and then it is difficult to adapt to complex surfaces. A passive compliant end-effector (PCEE) with 4-DOF employing pneumatic floating was designed to solve that problem in this article. According to the included angle between the floating spindle and the plunger, the supporting force functions of floating amount were constructed. Considering the characteristics of the supporting force and the operating modes comprehensively, an appropriate number of plungers was selected. Based on the equilibrium principle of spatial force system, a PCEE was designed by using the floating scheme at both ends, and then a physical model and a simplified model were provided. Eventually, the performances of 4-DOF PCEE are compared with those of the existing 2-DOF compliant end-effector (CEE). The results show that The PCEE can float with 4-DOF; when the number of plungers is 9, all performances meet the design requirements; the polishing force can adapt to the local bulges and overcome the error reflection; the 4-DOF PCEE can adaptively fit the surface to reduce its geometric error.

The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator.