Inchworm-like robots have shown promise in achieving multimodal movements, yet the integration of untethered locomotion and manipulation capabilities remains a persistent challenge in soft robotics. This paper presents a novel soft robot that addresses this challenge through an anisotropic magnetization distribution, achieved via 3D printing, enabling precise control over its three-dimensional structure and magnetic properties. This unique design allows for magnetic field-controlled locomotion with asymmetric time-varying postures, achieving a maximum stride length of 9.2 mm under an 80 mT field. The robot employs active friction manipulation between its feet, enabling an inchworm-like gait with alternating forefoot and rearfoot fixation. Additionally, the specific magnetization design permits independent control of grippers under the same magnetic field, facilitating complex manipulation tasks. The robot's potential in biomedical applications is demonstrated through a multi-region targeted drug delivery experiment. This integrated locomotion and manipulation platform shows promise for applications in confined spaces and represents a step forward in expanding the capabilities of soft robots for potential medical and industrial tasks.
This paper presents the analysis and integration of two forms of fusion for multi-robot multi-sensor target estimation, namely observation fusion and belief fusion. While the observation fusion is a traditional sensor fusion where the observation likelihood is fused within the loop of recursive Bayesian estimation (RBE), the belief fusion fuses independently updated beliefs outside the RBE loop. The comparative analysis of the two fusions show that observation fusion is suited for multimodal sensors within a single robot whereas multiple robots can each update their belief more effectively through belief fusion. Numerical results validate the strength of each fusion approach and show that the effectiveness of the optimally integrated system.
Gesture recognition plays an increasingly important role in fields such as human-computer inter-action and rehabilitation robots and therapy. However, most current gesture recognition systems are based on a single type of sensor, which limits the feature sources for gesture recognition. Therefore, this paper proposes a time-frequency and visual feature fusion multimodal recognition framework (TFVF -CNN). It combines the deep features extracted from a small amount of image data with the time-frequency fea-tures of surface electromyogram (sEMG) signals to improve the accuracy of gesture recognition based on sEMG. Additionally, this paper introduces a multi-modal dataset (MEVD) containing raw sEMG signal data, sEMG signal time-frequency data, and image data, providing a reliable data source for experiments. Finally, the experimental results show that gesture recognition accuracy with multimodal fusion features and time-frequency features based on sEMG signals is 93.88 % and 95.9 %, respectively. Furthermore, we verified the performance of gesture recognition under partial image occlusion and found that TFVF -CNN still exhibits superior performance.
