IET Cybersystems and Robotics最新文献

Dexterous robotic hands are essential for various tasks in dynamic environments, but challenges such as slip detection and grasp stability affect real-time performance. Traditional grasping methods often fail to detect subtle slip events, leading to unstable grasps. This paper proposes a real-time slip detection and force compensation system using a hybrid convolutional neural networks and long short-term memory (CNN-LSTM) architecture to detect slip to enhance grasp stability. The system combines tactile sensing with deep learning to detect slips and dynamically adjust individual finger grasping forces, ensuring precise and stable object grasping. The proposed system leverages a hybrid CNN-LSTM architecture to effectively capture both spatial and temporal features of slip dynamics, enabling robust slip detection and grasp stabilisation. By employing data augmentation techniques, the system generates a comprehensive dataset from limited experimental data, enhancing training efficiency and model generalisation. The approach extends slip detection to individual fingers, allowing real-time monitoring and targeted force compensation when a slip is detected on a specific finger. This ensures adaptive and stable grasping, even in dynamic environments. Experimental results demonstrate significant improvements, with the CNN-LSTM model achieving an 82% grasp success rate, outperforming traditional CNN (70%), LSTM (72%), and only traditional proportional–integral–derivative PID (54%) methods. The system's real-time force adjustment capability prevents object drops and enhances overall grasp stability, making it highly scalable for applications in industrial automation, healthcare, and service robots. Despite the CNN-LSTM architecture being a well-established approach, it demonstrates exceptional performance in this task, achieving high accuracy and robustness in slip detection and grasp stabilisation.

In robotic bimanual teleoperation, multimodal sensory feedback plays a crucial role, providing operators with a more immersive operating experience, reducing cognitive burden and improving operating efficiency. In this study, we develop an immersive bilateral isomorphic bimanual telerobotic system, which comprises dual arms and dual dexterous hands, with visual and haptic force feedback. To assess the performance of this system, we carried out a series of experiments and investigated the user's teleoperation experience. The results demonstrate that haptic force feedback enhances physical perception capabilities and complex task operating abilities. In addition, it compensates for visual perception deficiencies and reduces the operator's work burden. Consequently, our proposed system achieves more intuitive, realistic and immersive teleoperation, improves operating efficiency and expands the complexity of tasks that robots can perform through teleoperation.

Gesture recognition is a key task in the field of human–computer interaction (HCI). To solve the problems of low accuracy and poor real-time performance in the recognition process, this paper designs a HCI system based on gesture recognition. This paper utilises the Ultraleap 3Di to collect the dynamic gesture dataset for the defined interaction gestures, and the high-precision device guarantees data collection. This paper constructs a framework incorporating the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTM) using noncontact gesture interaction as the medium of human–computer collaboration. The framework utilises CNN to perform feature extraction on the input frame information. Then, the extracted feature sequences are fed into LSTM to process the timing information, which is very effective in classifying and recognising the defined dynamic gestures. Finally, a HCI system based on gesture recognition is designed. Based on the Unity3D platform, the UR5 robotic arm was modelled and the cyclic coordinate descent (CCD) algorithm was applied to solve the inverse kinematics, successfully realising the semantic control of gestures on the UR5 robotic arm. The experiment verifies that the CNN–LSTM network can ensure the real-time performance of the whole system and the effectiveness and reliability of the gesture interaction system based on Ultraleap 3Di.

Industrial anomaly detection is crucial for preventing equipment failures, yet challenges persist due to limited labelled data and complex fault patterns. This paper introduces the condition-adaptive refinement (CARe) framework, a self-supervised approach to anomaly detection that synthesises realistic training data through condition-guided diffusion and adaptive feature refinement. The framework features three innovations: a condition-controllable diffusion (CCD) model generates pseudo-anomalous samples using spatial constraints, enhancing synthetic data. An adaptive feature refinement (AFR) module improves detection accuracy by reconstructing multi-scale features. The method identifies anomalies by analysing reconstruction residuals without labelled data. Experiments validate the method's effectiveness, demonstrating substantial improvements in detection accuracy and generalisability. CARe offers a robust solution for industrial anomaly detection under data scarcity.

In recent years, significant advancements have been made in enabling intelligent unmanned agents to achieve autonomous navigation and positioning within large-scale indoor or underground environments. Central to these achievements is simultaneous localization and mapping (SLAM) technology. Concurrently, the rapid evolution of LiDAR technologies has revolutionised SLAM, enhancing localisation and mapping capabilities in extreme environments characterised by high dynamics, sparse features or GPS-denied environment. Although much research has concentrated on camera-based SLAM or GPS-fused SLAM, this paper provides a comprehensive review of the development of LiDAR-based multi-sensor fusion SLAM with a particular emphasis on GPS-denied environments and filter-based sensor fusion techniques. The paper is structured as follows: The first section introduces the relevant hardware and datasets. The second section delves into the localisation methodologies employed. The third section discusses the mapping processes involved. The fourth section addresses open problems and suggests future research directions. Overall, this review aims to offer a thorough analysis of the development trends in SLAM with a focus on LiDAR-based methods, covering both hardware and software aspects, providing readers with a clear reference on workflow for engineering deliverable technologies that can be adapted to various application scenarios.

In human–robot collaboration, ensuring both safety and efficiency in obstacle avoidance remains a critical challenge. This paper proposes a sampling-based danger-aware artificial potential field (SDAPF) method for obstacle avoidance during human–robot collaboration and interaction. Existing methods often struggle with dynamic obstacles and varying environmental complexities, which can hinder their performance. To address these challenges, SDAPF integrates three key components: position sampling for local minimum avoidance, a novel hazard index that quantifies risk based on the distance and relative velocity between the robot and dynamic obstacles and a dynamic obstacle motion prediction module leveraging depth image data. These features enable intelligent path selection, adaptive step size adjustments based on obstacle dynamics and proactive decision-making for collision-free navigation. The hazard index allows the robot to dynamically assess the urgency of avoiding an obstacle, whereas the motion prediction module anticipates future positions of moving obstacles, enabling the robot to plan paths in advance. The effectiveness of SDAPF is demonstrated through both simulations and real-world experiments, highlighting its potential to significantly enhance safety and operational efficiency in complex human–robot interaction scenarios.

Multi-robot source seeking in unknown environments is challenging due to the difficulties in coordinating multi-robot sensing, information fusion and path planning. Existing approaches often struggle with computational scalability and search efficiency, particularly when dealing with multiple sources. In this paper, we develop a distributed multi-robot multi-source seeking strategy that enables robots to discover multiple sources using local sensing and neighbourhood communication. Our approach consists of three key components. First, we design a distributed mapping technique that leverages Gaussian processes for probabilistic inference across the entire environment and adapts it for a decentralised setup. Second, we formulate the source-seeking problem as an informative path planning problem and design a new information-theoretic objective function that combines predicted source locations with environmental uncertainty to prevent robots from being trapped at discovered sources. Third, we develop a tree search algorithm for planning the actions of robots over a fixed-horizon cycle. The algorithm generates a sequence of points leading to the most informative location. Based on the sequence, the robot is guided to the target location by taking a fixed-step movement inspired by the principles of model predictive control. Simulations validate our approach across different scenarios with varying numbers of sources and robots. In particular, the proposed information-theoretic heuristic outperforms the broadly used uncertainty-first and mean-gradient-first approaches, reducing search steps by up to 36.7%. Furthermore, our approach achieves an improvement of up to 63.8% in search efficiency compared to state-of-the-art coverage-based methods for multi-robot multi-source seeking problems. The average computational time of the proposed method is below 90 ms, supporting its feasibility for real-time applications.

Accurate identification of fungal species is essential for effective diagnosis and treatment. Traditional microscopy-based methods are often subjective and time-consuming. Deep learning has emerged as a promising tool in this domain. However, existing deep learning models often struggle to generalise in the presence of class imbalance and subtle morphological differences, which are common in fungal image datasets. This study proposes MASA-Net, a deep learning framework that combines a fine-tuned DenseNet201 backbone with a multi-aspect channel–spatial attention (MASA) module. The attention mechanism refines spatial and channel-wise features by capturing multi-scale spatial patterns and adaptively emphasising informative channels. This enhances the network's ability to focus on diagnostically relevant fungal structures while suppressing irrelevant features. The MASA-Net is evaluated on the DeFungi dataset and demonstrates superior performance in terms of accuracy, precision, recall and F1-score. It also outperforms established attention mechanisms such as squeeze-and-excitation networks (SE) and convolutional block attention module (CBAM) under identical conditions. These results highlight MASA-Net's robustness and effectiveness in addressing class imbalance and structural variability, offering a reliable solution for automated fungal species identification.

Autonomous systems have demonstrated high performance in several applications. One of the most important is localisation systems, which are necessary for the safe navigation of autonomous cars or mobile robots. However, despite significant advances in this field, there are still areas open for research and improvement. Two of the most important challenges include the precise traversal of a bounded route and emergencies arising from the breakdown or failure of one or more sensors, which can lead to malfunction or system localisation failure. To address these issues, auxiliary assistance systems are necessary, enabling localisation for a safe return to the starting point, completing the trajectory, or facilitating an emergency stop in a designated area for such situations. Motivated by the exploration of applying artificial intelligence to pose estimation in a navigation system, this article introduces a monocular visual odometry method that, through teach and repeat, learns and autonomously replicates trajectories. Our proposal can serve as either a primary localisation system or an auxiliary assistance system. In the first case, our approach is applicable in scenarios where the traversing route remains unchanged. In the second case, the goal is to achieve a safe return to the starting point or to reach the end point of the trajectory. We initially utilised a publicly available dataset to showcase the learning capability and robustness under different visibility conditions to validate our proposal. Subsequently, we compared our approach with other well-known methods to assess performance metrics. Finally, we evaluated real-time trajectory replication on a ground robot, both simulated and real, across multiple trajectories of increasing complexity.

The tool path trajectory serves as a cornerstone of three-dimensional (3D) printing robot technology, and path optimisation algorithms are instrumental in enabling faster, more precise and higher-quality prints. This work proposes a clustering path optimisation-based 2-opt rapid wax-drawing trajectory planning method for 3D drawing robots. Firstly, the input wax-drawing image is preprocessed to extract contour information, which is then simplified into polygons. Next, the spiral and filling trajectory algorithms are used to convert the polygons into corresponding spiral and filling paths, which are modelled as nodes in the travelling salesman problem (TSP). An improved k-means++ clustering algorithm is then designed to adaptively divide the nodes into multiple clusters. Each cluster is subsequently planned using the improved ant colony optimisation (ACO) algorithm to find the shortest path. Afterwards, the nearest-neighbour algorithm is employed to connect the shortest paths of each cluster, forming an initial tool path. Finally, the 2-opt optimisation algorithm is incorporated to optimise the preliminary path, resulting in the optimal motion trajectory for the wax-drawing tool. The verification tests show that the proposed method achieves an average reduction in path length of 30.75% compared with the parallel scanning method, traditional ant colony optimisation, Christofides with 2-opt algorithm. Meanwhile, the 3D robot wax-drawing experiments demonstrate a 17.9% reduction in drawing time, significantly improving the efficiency of large-scale production and highlighting the practical value of 3D drawing robots.