The efficient integration of visual and tactile information is essential for slip detection and evaluation of grasping stability. However, existing research generally combines visual or tactile modalities as prior information, without fully exploring mechanisms for fusing complementary modalities. In this article, we propose a visual–tactile fusion Transformer (VTF-Trans) for slip detection, designed to handle unaligned data in different formats and facilitate cross-modal information exchange. The main advantages of the proposed method are summarized as follows: first, VTF-Trans employs an improved dual-stream transformer for feature extraction. In addition, we introduce a gated modal attention module to further refine cross-modal fusion. Compared with the existing methods, VTF-Trans effectively integrates useful information from different modalities across multiple scales. Second, to extract deep multimodal information, we propose a cross-modal attention (CMA) mechanism. By defining cross-affinity based on single-modality affinities (token metrics), CMA naturally alleviates the gap between different modalities and domains. Third, we evaluate VTF-trans on three datasets and conduct unknown object grasping experiments. Compared with the state-of-the-art methods, VTF-Trans achieves the highest accuracy for robotic grasping and slip detection, highlighting its superior performance and practical applicability.
This paper presents a deployable boom which combines a rigid telescopic frame and a flexible tape spring. The front end of the spring is fixed on the rear end of the innermost segment of the frame. The spring spreads and rolls up inside the frame to drive the segments to move one by one to realize the boom deployment and retraction. The driving forces needed to deploy and retract the frame are modeled and simulated. The feasibility of the frame driving by only one spring is also studied. A 1.6 kg prototype system with 2.1 m total deployment length is implemented. Experimental results show the maximum driving forces for deployment and retraction of the frame are about 9.1 N and 6.8 N respectively. The boom is able to deploy in 76 s with energy consumption of 315 J. The boom can resist at least 15 N force axially and 31.5 N·m bending moment when the forces are acted on its front end. Advantages of this kind of boom enable it to be applied for instruments deployment, walking and sampling assists, and robotic arms design in space exploration.
This paper addresses the problem of motion prediction and tracking control for cloud robotic systems with time-varying delays in measurements. A novel method using an observer-based structure for position and velocity prediction is developed to estimate the real-time information of robot manipulator. The prediction error can converge to zero even if model uncertainties exist in the robot manipulator. Based on the predicted positions and velocities, some sufficient conditions are derived to design suitable tracking controllers such that semi-globally uniformly ultimately bounded tracking performance of the predictor–controller couple can be guaranteed. Finally, the effectiveness and robustness to model uncertainties of the proposed method are verified by a two degree-of-freedom (DOF) robot system.
1 Robot Sensor and Control Lab, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 2Department of Mechanical Engineering, University of Victoria, P.O. Box 3055 STN CSC, Victoria, British Columbia, Canada V8W 2Y2 3 School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW 2522, Australia 4 Institute of Intelligent Machine, Chinese Academy of Sciences, Hefei 230031, China
In this Paper, a new freezing proof method is put forward. Its dynamic characteristic has been studied by theoretical analysis and esperimental method. The measures of enetgy conservation in operation have been discussed according to the research tesult.The result can provide reference for practical engineering.
Manual calibration of nuclear medicine scanners currently relies on handling phantoms containing radioactive sources, exposing personnel to high radiation doses and elevating cancer risk. We designed an automated detection framework for robotic inspection on the YOLOv8n foundation. It pairs a lightweight backbone with a shape-aware geometric attention module and an anchor-free head. Facing a small training set, we produced extra images with a GAN and then fine-tuned a pretrained network on these augmented data. Evaluations on a custom dataset consisting of PET/CT gantry and table images showed that the SAM-YOLOv8n model achieved a precision of 93.6% and a recall of 92.8%. These results demonstrate fast, accurate, real-time detection, offering a safer and more efficient alternative to manual calibration of nuclear medicine equipment.
With respect to the problem of big volume, large weight and high power consumption of lunar sampler nowadays, the paper firstly described a novel flexible mini lunar regolith sampler. Then the vibration model of it is established while drilling. The drilling efficiency can be improved more effectively by controlling the lunar regolith sampler always in the resonance state. But the dynamical modeling of the sampler-regolith system is difficult to obtain and time varies when the sampler is in different depth in the lunar regolith. So we present a method of the vibration frequency fuzzy adaptive control based on the dynamic prediction by using the Levenberg-Marquardt Back Propagation (LMBP) neural networks. The LMBP with a FIR filter in series is used to predict the resonant frequency dynamically. And the fuzzy adaptive control is used to calculate the sweeping frequency bandwidth with the input of the amplitude and variation. The simul
