Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
Inspired by the characteristics of living organisms with soft bodies and flexibility, continuum robots, which bend their robotic bodies and adapt to different shapes, have been widely introduced. Such robots can be used as manipulators to handle objects by wrapping themselves around them, and they are expected to have high grasping performance. However, their infinite degrees of freedom and soft structure make modeling and controlling difficult. In this study, we develop a tendon-driven continuum robot system with color-based posture sensing. The robot is driven by dividing the continuum body into two parts, enabling it to grasp objects by flexible motions. For posture sensing, each joint is painted in a different color, and the 3D coordinates of each joint are detected by a stereo camera for estimating the 3D shape of the robotic body. By taking a video of the robot in actuation and using image processing to detect joint positions, we succeeded in obtaining the posture of the entire robot in experiments. We also robustly demonstrate the grasping manipulation of an object using the redundant structure of the continuum body....
Deployment of CubeSat swarms is proposed for various missions necessitating cooperative interactions among satellites. Commonly, the cube swarm requires formation flight and even rendezvous and docking, which are very challenging tasks since they require more energy and the use of advanced guidance, navigation, and control techniques. In this paper, we propose the use of an extensible hook system and its corresponding GNC architecture to mitigate these drawbacks, i.e., it allows for saving fuel and reduces system complexity by including techniques that have been previously demonstrated on Earth. This system is based on a scissor boom structure, which could reach up to five meters for a 4U CubeSat dimension, including three degrees of freedom to place the end effector at any pose within the system workspace. We simulated the dynamic behavior of a CubeSat with the proposed system, demonstrating that the required power for a 16U CubeSat equipped with one extensible hook system is considered acceptable according to the current state-of-the-art actuators....
The accurate identification of kinematic parameters is crucial for improving the positioning accuracy of industrial robots, particularly in advanced manufacturing and automation. However, limited measurement space in practical applications often leads to concentrated data, causing overfitting and unreliable parameter estimation when using traditional identification methods. To address these challenges, this study proposes an L2-regularization-based method to improve parameter identification accuracy by penalizing deviations from the nominal kinematic parameters. The regularization factor is determined using a k-fold cross-validation strategy, ensuring a balance between generalization and accuracy. The proposed method was validated on a six-axis industrial robot, with calibration performed in a constrained measurement space and verification conducted in an expanded workspace. Compared to traditional least-squares methods, which suffer from significant parameter deviations and overfitting, the proposed L2-regularized method effectively improves parameter estimation accuracy. Specifically, this method reduces the mean error from 3.461 mm to 0.399 mm, achieving an approximate 88% improvement compared to the error before calibration. These findings demonstrate the effectiveness of the proposed method in improving parameter identification and positioning accuracy under constrained measurement space....
Surface classification is critical for ground robots operating in diverse environments, as it improves mobility, stability, and adaptability. This study introduces IMU-based deep learning models for surface classification as a low-cost alternative to computer vision systems. Two feature fusion models were introduced to classify the surface type using time-series data from an IMU sensor mounted on a ground robot. The first model, a cascaded fusion model, employs a 1-D Convolutional Neural Network (CNN) followed by a Long Short-Term Memory (LSTM) network and then a multi-head attention mechanism. The second model is a parallel fusion model, which processes sensor data through both a CNN and an LSTM simultaneously before concatenating the resulting feature vectors and then passing them to a multi-head attention mechanism. Both models utilize a multi-head attention mechanism to enhance focus on relevant segments of the time-sequence data. The models were trained on a normalized Internal Measurement Unit (IMU) dataset, with hyperparameter tuning achieved via grid search for optimal performance. Results showed that the cascaded model achieved higher accuracy metrics, including a mean Average Precision (mAP) of 0.721 compared to 0.693 for the parallel model. However, the cascaded model incurred a 44.37% increase in processing time, which makes the parallel fusion model more suitable for real-time applications. The multi-head attention mechanism contributed significantly to accuracy improvements, particularly in the cascaded model....
Miniature robots in small-diameter pipelines require efficient and reliable environmental perception for autonomous navigation. In this paper, a tiny machine learning (TinyML)-based resource-efficient pipe feature recognition method is proposed for miniature robots to identify key pipeline features such as elbows, joints, and turns. The method leverages a custom five-layer convolutional neural network (CNN) optimized for deployment on a robot with limited computational and memory resources. Trained on a custom dataset of 4629 images collected under diverse conditions, the model achieved an accuracy of 97.1%. With a peak RAM usage of 195.1 kB, flash usage of 427.9 kB, and an inference time of 1693 ms, the method demonstrates high computational efficiency while ensuring stable performance under challenging conditions through a sliding window smoothing strategy. These results highlight the feasibility of deploying advanced machine learning models on resource-constrained devices, providing a cost-effective solution for autonomous in-pipe exploration and inspection....
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