Stroke is becoming a widely concerned social problem, and robot-assisted devices have made considerable contributions in the training and treatment of rehabilitation. Due to the compliance and continuous deformation capacity, rehabilitation devices driven by soft actuators are attached to widespread attention. Considering the large output force of pneumatic artificial muscle (PAM) and the biological musculoskeletal structure, an antagonistic PAM-driven rehabilitation robotic device is developed. To fulfill the need for control of the proposed device, a knowledge-guided data-driven modeling approach is used and an adaptive feedforward–feedback control approach is presented to ensure the motion accuracy under large deformation motion with high frequency. Finally, several simulations and experiments are carried out to evaluate the performance of the developed system, and the results show that the developed system with the proposed controller can achieve expected control performance under various operations.

Multimodal motion capability is an emerging topic in the robotics field, and this paper presents a hybrid robot system maneuvering in both terrestrial and aerial environments. Firstly, a micro quadruped–quadrotor robot with onboard sensing and computing is developed. This robot incorporates both the high mobility of unmanned aerial vehicles and the long endurance of mobile robots on the ground. A coordinated motion control scheme is then exploited for adaptive terrestrial–aerial motion transition. In this scheme, a bio-inspired terrestrial locomotion controller is proposed to generate various quadruped locomotions, and a model-based aerial locomotion controller is proposed to generate various quadrotor configurations. Then, an unified motion controller for the two subsystems which dynamically adjusts crawling and flying motion in a complicated environment is presented. Consequently, several practical trials are conducted to demonstrate the adaptability and the robustness of the proposed system.

High-sensitivity flow measurement technology is a prerequisite for precise dynamic control of microfluidics. Despite the advances in structure optimization, a more efficient approach to improve device sensitivity can be realized by leveraging materials with a higher temperature coefficient of resistance (TCR). This work presents the design and simulation of a vanadium dioxide (VO ₂ )-based microfluidic thermal flow sensor with record high sensitivity. Owing to the phase change property, VO ₂ demonstrates the maximum TCR of -0.703 K ^-1 and -0.63 K ^-1 in the major heating and cooling curves, respectively, which is more than two orders of magnitude higher than commonly used thermal-sensitive materials. To fully utilize the high thermal sensitivity of VO ₂ , a dual-heater configuration (DHC) with enhanced thermal differential effect is proposed, and its sensing performance is evaluated in the flow range below 10 μL∙min ^-1 . By individually operating the VO ₂ thermal sensors at critical transition temperatures in the major hysteresis loop, the sensitivity can reach as high as 2.79 V/μL∙min ^-1 , which is about 187.88 times and 277.89 times higher than the VO ₂ -based anemometer and the Pt-based DHC sensor, respectively. The research in the present work may enable a breakthrough in the improvement of high-performance microfluidic thermal flow sensors in the ultra-low flow region using non-standard metamaterials.

In this paper, we propose a biomimetic approach of adaptive locomotion generation for a hexapod robot. Inspired by biology, a Central Pattern Generator (CPG) model is employed to generate tripod locomotion in the six-legged robot and the parameters can be tuned adaptively. Both simulation studies and experiments verified that the proposed control scheme can help the hexapod robot improve maneuverability and environmental adaptability with different parameters in various terrains.