Walking on the water surface is a dream of humans, but it is exactly the way of life for some aquatic insects. In this study, a bionic aquatic microrobot capable of walking on the water surface like a water strider was reported. The novel water strider-like robot consisted of ten superhydrophobic supporting legs, two miniature dc motors, and two actuating legs. The microrobot could not only stand effortlessly but also walk and turn freely on the water surface, exhibiting an interesting motion characteristic. A numerical model describing the interface between the partially submerged leg and the air-water surface was established to fully understand the mechanism for the large supporting force of the leg. It was revealed that the radius and water contact angle of the legs significantly affect the supporting force. Because of its high speed, agility, low cost, and easy fabrication, this microrobot might have a potential application in water quality surveillance, water pollution monitoring, and so on.

Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.

This study reported for the first time a novel microrobot that could continuously jump on the water surface without sinking, imitating the excellent aquatic locomotive behaviors of a water strider. The robot consisted of three supporting legs and two actuating legs made from superhydrophobic nickel foam and a driving system that included a miniature direct-current motor and a reduction gear unit. In spite of weighing 11 g, the microrobot jumped 14 cm high and 35 cm long at each leap. In order to better understand the jumping mechanism on the water surface, the variation of forces exerted on the supporting legs was carefully analyzed and calculated based on numerical models and computational simulations. Results demonstrated that superhydrophobicity was crucial for increasing the upward force of the supporting legs and reducing the energy consumption in the process of jumping. Although bionic microrobots mimicking the horizontal skating motions of aquatic insects have been fabricated in the past years, few studies reported a miniature robot capable of continuously jumping on the water surface as agile as a real water strider. Therefore, the present finding not only offers a possibility for vividly imitating and better understanding the amazing water-jumping capability of aquatic insects but also extends the application of porous and superhydrophobic materials to advanced robotic systems.

In this paper, the task-space cooperative tracking control problem of networked robotic manipulators without task-space velocity measurements is addressed. To overcome the problem without task-space velocity measurements, a novel task-space position observer is designed to update the estimated task-space position and to simultaneously provide the estimated task-space velocity, based on which an adaptive cooperative tracking controller without task-space velocity measurements is presented by introducing new estimated task-space reference velocity and acceleration. Furthermore, adaptive laws are provided to cope with uncertain kinematics and dynamics and rigorous stability analysis is given to show asymptotical convergence of the task-space tracking and synchronization errors in the presence of communication delays under strongly connected directed graphs. Simulation results are given to demonstrate the performance of the proposed approach.

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A variable magnification ratio transmission structure powered by the electric actuators is proposed to improve the flexibility and portability of the exoskeleton under heavy load carrying condition. The parameters of connecting rod size and hanging position are optimized to ensure that the output torque of active joints can fully envelope the demand load area. The control strategy based on intrinsic sensing is designed to realize the automatic human motion intention prediction and flexible trajectory tracking. The newly developed split embedded connecting rod can accurately measure the human-robot interaction (HRI) force applied to the exoskeleton and extract the human motion intention without being affected by the differences in wearing status. The force tracking control based on the zero-force following is modified by feedforward compensation with extreme learning machine (ELM), which enhances the response speed to human motion intention and reduces the HRI force by 70.6%. Based on multi-sensor information, stacked autoencoder deep neural networks (DNNs) are utilized to realize the automatic locomotion transition and the corresponding control parameters' switching. After optimization by a hybrid algorithm of genetic algorithm and particle swarm optimization (GA_PSO), the identification accuracy is enhanced from 96.2% to 99.7%. The adaptive neural-fuzzy inference system (ANFIS) is used to analyze the plantar pressure to achieve flexible switching between the swing phase and the stance phase. The experiments under various gait motion trajectories assisted by novel weight-bearing exoskeleton are carried out for evaluation, and the performance of the proposed control strategy based on motion intention prediction, locomotion mode identification, and gait phase switching is effectively verified.

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