The rapidly exploring random tree and its variants (RRTs) have been widely adopted as the motion planning algorithms for mobile robots. However, the trap space problem, such as mazes and S-shaped corridors, hinders their planning efficiency. In this article, we present a generalized Voronoi diagram (GVD)-based heuristic path planning algorithm to generate a heuristic path, guide the sampling process of RRTs, and further improve the motion planning efficiency of RRTs. Different from other heuristic algorithms that only work in certain environments or depend on specified parameter setting, the proposed algorithm can automatically identify the environment feature and provide a reasonable heuristic path. First, the given environment is initialized with a lightweight feature extraction from the GVD, which guarantees that any state in the free space can be connected to the feature graph without any collision. Second, to remove the redundancy of feature nodes, a feature matrix is proposed to represent connections among feature nodes and a corresponding feature node fusion technique is utilized to delete the redundant nodes. Third, based on the GVD feature matrix, a heuristic path planning algorithm is presented. This heuristic path is then used to guide the sampling process of RRTs and achieve real-time motion planning. The proposed GVD feature matrix can be also utilized to improve the efficiency of the replanning. Through a series of simulation studies and real-world implementations, it is confirmed that the proposed algorithm achieves better performance in heuristic path planning, feature extraction of free space, and real-time motion planning.

As an ideal method to manipulate biological particles, the dielectrophoresis (DEP) technique has been widely used in clinical diagnosis, disease treatment, drug development, immunoassays, cell sorting, etc. This review summarizes the research in the field of bioparticle manipulation based on DEP techniques. Firstly, the basic principle of DEP and its classical theories are introduced in brief; Secondly, a detailed introduction on the DEP technique used for bioparticle manipulation is presented, in which the applications are classified into five fields: capturing bioparticles to specific regions, focusing bioparticles in the sample, characterizing biomolecular interaction and detecting microorganism, pairing cells for electrofusion and separating different kinds of bioparticles; Thirdly, the effect of DEP on bioparticle viability is analyzed; Finally, the DEP techniques are summarized and future trends in bioparticle manipulation are suggested.

Throat cancer treatment involves surgical removal of the tumor, leaving patients with facial disfigurement as well as temporary or permanent loss of voice. Surface electromyography (sEMG) generated from the jaw contains lots of voice information. However, it is difficult to record because of not only the weakness of the signals but also the steep skin curvature. This paper demonstrates the design of an imperceptible, flexible epidermal sEMG tattoo-like patch with the thickness of less than 10 μm and peeling strength of larger than 1 N cm⁻¹ that exhibits large adhesiveness to complex biological surfaces and is thus capable of sEMG recording for silent speech recognition. When a tester speaks silently, the patch shows excellent performance in recording the sEMG signals from three muscle channels and recognizing those frequently used instructions with high accuracy by using the wavelet decomposition and pattern recognization. The average accuracy of action instructions can reach up to 89.04%, and the average accuracy of emotion instructions is as high as 92.33%. To demonstrate the functionality of tattoo-like patches as a new human–machine interface (HMI) for patients with loss of voice, the intelligent silent speech recognition, voice synthesis, and virtual interaction have been implemented, which are of great importance in helping these patients communicate with people and make life more enjoyable.

Despite the death rate of COVID-19 is less than 3%, the fatality rate of severe/critical cases is high, according to World Health Organization (WHO). Thus, screening the severe/critical cases before symptom occurs effectively saves medical resources. In this study, all 336 cases of patients infected COVID-19 in Shanghai to March 12th, were retrospectively enrolled, and divided in to training and test datasets. In addition, 220 clinical and laboratory observations/records were also collected. Clinical indicators were associated with severe/critical symptoms were identified and a model for severe/critical symptom prediction was developed. Totally, 36 clinical indicators significantly associated with severe/critical symptom were identified. The clinical indicators are mainly thyroxine, immune related cells and products. Support Vector Machine (SVM) and optimized combination of age, GSH, CD3 ratio and total protein has a good performance in discriminating the mild and severe/critical cases. The area under receiving operating curve (AUROC) reached 0.9996 and 0.9757 in the training and testing dataset, respectively. When the using cut-off value as 0.0667, the recall rate was 93.33 % and 100 % in the training and testing datasets, separately. Cox multivariate regression and survival analyses revealed that the model significantly discriminated the severe/critical cases and used the information of the selected clinical indicators. The model was robust and effective in predicting the severe/critical COVID cases.

Magnetic miniature soft robots have shown great potential for facilitating biomedical applications by minimizing invasiveness and possible physical damage. However, researchers have mainly focused on fixed-size robots, with their active locomotion accessible only when the cross-sectional dimension of these confined spaces is comparable to that of the robot. Here, we realize the scale-reconfigurable miniature ferrofluidic robots (SMFRs) based on ferrofluid droplets and propose a series of control strategies for reconfiguring SMFR’s scale and deformation to achieve trans-scale motion control by designing a multiscale magnetic miniature robot actuation (M ³ RA) system. The results showed that SMFRs, varying from centimeters to a few micrometers, leveraged diverse capabilities, such as locomotion in structured environments, deformation to squeeze through gaps, and even reversible scale reconfiguration for navigating sharply variable spaces. A miniature robot system with these capabilities combined is promising to be applied in future wireless medical robots inside confined regions of the human body.

Flexible electronics devices with tactile perception can sense the mechanical property data of the environment and the human body, and they present a huge potential in the human health system. In particular, the introduction of ultra-flexible and self-powered characteristics to tactile sensors can effectively reduce the problems caused by rigid batteries. Herein, we report a triboelectric nanogenerator (TENG), mainly consisting of an ultra-flexible polydimethylsiloxane (PDMS) film with micro-pyramid-structure and sputtered aluminum electrodes, which achieves highly conformal contact with skin and the self-powered detection of human body motions. The flexible polyethylene terephthalate (PET) film was selected as spacer layer, which made the sensor work in the contact-separation mode and endowed the perfect coupling of triboelectrification and electrostatic induction. Moreover, the controllable and uniform micro-structure PDMS film was fabricated by using the micro-electro-mechanical system (MEMS) manufacturing process, bringing a good sensitivity and high output performance to the device. The developed TENG can directly convert mechanical energy into electric energy and light up 110 green Light-Emitting Diodes (LEDs). Furthermore, the TENG-based sensor displays good sensitivity (2.54 V/kPa), excellent linearity (R² = 0.99522) and good stability (over 30,000 cycles). By virtue of the compact size, great electrical properties, and great mechanical properties, the developed sensor can be conformally attached to human skin to monitor joint movements, presenting a promising application in wearable tactile devices. We believe that the ultra-flexible and self-powered tactile TENG-based sensor could have tremendous application in wearable electrons.