Background: We aimed to establish a novel method, using the weighted Procrustes analysis (WPA) algorithm, which assigns weight to facial anatomical landmarks, to construct a three-dimensional facial symmetry reference plane (SRP) for mandibular deviation patients. Methods: Three-dimensional facial SRPs were independently extracted from 15 mandibular deviation patients using both our WPA algorithm and the standard PA algorithm. A reference plane was defined to serve as the ground truth. To determine whether the WPA SRP or the PA SRP was closer to the ground truth, we measured the position error of mirrored landmarks, the facial asymmetry index (FAI) error, and the angle error for the global face and each facial third partition. Results: The average angle error between the WPA SRP and the ground truth was 1.66 ± 0.81°, which was smaller than that between the PA SRP and the ground truth. The position error of the mirrored landmarks constructed using the WPA algorithm in the global face (3.64 ± 1.53 mm) and each facial partition was lower than that constructed using the PA algorithm. The average FAI error of the WPA SRP was − 7.77 ± 17.02 mm, which was smaller than that of the PA SRP. Conclusions: This novel automatic algorithm, based on weighted anatomic landmarks, can provide a more adaptable SRP than the standard PA algorithm when applied to severe mandibular deviation patients and can better simulate the diagnosis strategies of clinical experts.

Accurate pose estimation of textureless objects from RGB images, without relying on depth information, remains a significant challenge in computer vision. This paper introduces an RGB-based 6D pose estimation method for 6-Degree-of-Freedom robotic grasping. A novel network, named the Regionbased Keypoint Detection Transformer, is proposed, utilizing the concept of set prediction. This network outputs numerous set points, establishing a fuzzy representation of keypoints. Set points are interpreted as keypoints through a voting mechanism for PnP-based 6D pose solving and refinement, guiding robotic grasping actions. Specialized losses, including a set-based coordinates loss and a soft index probability loss for multi-label classification, are designed to facilitate the training of set points. Evaluated on the LineMOD dataset, our method achieved an ADD(-S) accuracy of 92.92 % and a 2D projection accuracy of 98.72 %. Additionally, practical robotic grasping experiments were conducted in real-world environments, encompassing static and dynamic grasping, as well as bin-picking tasks. The picking success rate and completion rate reached 93.02 % and 100.00 % respectively, with the former exceeding the optimal baseline method by 2.11 %. These results validate the accuracy and robustness of our method in cluttered environments.

Traditional integer-order admittance control is widely used in industrial scenarios requiring force control, but integer-order models often struggle to accurately depict fractional-order-controlled objects, leading to precision bottlenecks in the field of precision machining. For robotic precision polishing scenarios, to enhance the stability of the control process, we propose a more physically accurate five-parameter fractional-order admittance control model. To reduce contact impact, we introduce a method combining the rear fastest tracking differential with fractional-order admittance control. The optimal parameter identification for the fractional-order system is completed through Pareto optimality and a time–frequency domain fusion analysis of the control system. We completed the optimal parameter identification in a simulation, which is applied to the robotic precision polishing scenario. This method significantly enhanced the force control precision, reducing the error margin from 15% to 5%.