In Takagi-Sugeno (T-S) fuzzy systems, the design of dynamic output feedback controller (DOFC) plays a crucial role in ensuring system performance. However, traditional DOFC designs often rely on instantaneous samples, which may lead to suboptimal stability and fail to leverage historical system information effectively. To address the above limitation, we propose a novel weighted sum-based dynamic event-triggered mechanism (WSDETM) for T-S fussy system that incorporates weighted historical measurement samples and internal dynamic variables to enhance the triggering condition. By considering the relative importance of past samples, the design of controller can achieve faster convergence to the equilibrium point, resulting in ensuring finite-time stability. In contrast to traditional DOFC designs focusing on asymptotic Lyapunov stability, our approach prioritizes finitetime performance, which is crucial for practical applications. Additionally, deception attacks are modelled in the system as a Markov random process, providing a more general and robust framework compared to the traditional Bernoulli process. The design of DOFC and WSDETM parameters is achieved using the Cone Complementarity Linearization (CCL) algorithm, and extensive experimental results demonstrate the superior performance of WSDETM in terms of stability, finite-time convergence, and communication efficiency. Eventually, the main results demonstrate the superiority of WSDETM in two cases.

Redundant manipulators utilize their redundant solutions to achieve the position and orientation control of the end-effector in a given variety of complex tasks, which is an essential issue in the field of industrial robots. Moreover, for manipulators with unknown models, traditional control methods generate large control errors during the execution of the task or even lead to the failure of the task. To address this problem, this paper proposes a Discrete Data-Driven Position and Orientation Control (D³POC) scheme. The scheme consists of a Discrete Jacobian Matrix Learning (DJML) algorithm, a Discrete Gradient Neural Dynamics (DGND) solver, and a Kalman filter. Then, theoretical analyses are provided to demonstrate the convergence of the D³POC scheme. Subsequently, simulations, comparisons, and experiments based on this scheme are carried out on redundant manipulators. The obtained results indicate the validity, superiority, and practicability of the proposed control scheme.

Grasp detection plays a critical role for robot manipulation. Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency. However, they usually transmit the high-level feature in the encoder to the decoder, and low-level features are neglected. It is noted that low-level features contain abundant detail information, and how to fully exploit low-level features remains unsolved. Meanwhile, the channel information in high-level feature is also not well mined. Inevitably, the performance of grasp detection is degraded. To solve these problems, we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle residual. Both low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module, and the fused information is then propagated to corresponding layers of the decoder for in-depth feature fusion. Such a hierarchical fusion guarantees the quality of grasp prediction. Furthermore, an inverted shuffle residual module is created, where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective branches. By such differentiation processing, more high-dimensional channel information is kept, which enhances the representation ability of the network. Besides, an information enhancement module is added before the encoder to reinforce input information. The proposed method attains 98.9% and 97.8% in image-wise and object-wise accuracy on the Cornell grasping dataset, respectively, and the experimental results verify the effectiveness of the method.

Minimally invasive surgery (MIS) robots, such as single-arm stapling robots, are key to oral and maxillofacial surgery because they overcome space constraints in the oral cavity and deep throat. However, biodegradable suture staples should be developed for the single-arm stapling robots to avoid a secondary operation. For this aim, a new type of Mg-3Zn-0.2Ca-2Ag biodegradable alloy wire was developed in this study applied as suture staples. Its tensile strength, yield strength, and elongation are 326.1 MPa, 314.5 MPa, and 19.6%, respectively. Especially, the alloy wire attains the highest yield strength value reported among all the biodegradable Mg wires, which is mainly attributed to fine grain strengthening and second phase strengthening such as Mg₂Zn₁₁ nano phase strengthening. Moreover, the corrosion rate of this alloy wire in simulated body fluid (SBF) reaches 26.8 mm/y, the highest value among all the biodegradable Mg alloy wires reported so far, which is mainly from the intensified galvanic corrosion between the Ag₁₇Mg₅₄ phase and the Mg matrix. In vitro studies demonstrate that the alloy wire exhibits good blood compatibility and low cytotoxicity. The cone beam computed tomography (CBCT) data shows that the suture staple made of the Mg alloy wire provides better mechanical support in the early postoperative period. From the single arm robot tests, it confirms that suture staples can close the wound tightly and remain stable over time. This research provides a good material selection for the automated suturing in oral and throat surgery robots.