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Many disabled people are not suitable for the control of the current mainstream manual joystick-controlled wheelchair, so the study of new control methods can provide convenience for these people, including bioelectrical signal control, voice control, and visual control, but these control methods have peripheral equipment complex or susceptible to environmental noise interference, in contrast. These problems can be solved effectively by using a wearable flexible sensor to recognize human movement signal. To collect and identify the strain signal characteristics of the moving neck muscles, based on the principle of flexible piezoresistive sensor, we designed and produced a K-carrageenan at polydimethylsiloxane (PDMS) flexible sensor, designed a series of experiments to explore the conductive properties, mechanical properties, and air permeability of the sensor, and also designed the peripheral acquisition circuit and signal processing algorithm. Finally, the empirical mode decomposition (EMD)-long short-term memory (LSTM) algorithm is used to classify the signal and explore the feasibility of fitting the signal to the human neck to control the wheelchair. The results show that the maximum tensile rate of the sensor designed by us is as high as 180%, and the air permeability is also improved slightly. After 1000 cycles of stretching, the sensor still has good performance and can effectively collect human neck action signals. Through the field test, the accuracy of this system is up to 95%, which provides a new idea for wheelchair control.

Strong winds can cause severe lateral vibrations in the cables of large-span cable-stayed bridges, leading to damage to the bridge. Real-time and effective acquisition of vibration behavior parameters in service is essential to assess the safety performance of structures. Existing sensing elements for detection include stress gauges, fiber optic gratings, and piezoelectric ceramics, but most of them have the disadvantages of high cost, poor durability, and low sensitivity. In this article, a flexible piezoresistive sensor based on carbon black (CB)/carbon nanotubes (CNTS) at PDMS has been developed with good tensile properties (tensile fracture rate up to 230%) and electrical properties (GF up to 12.1). It adheres closely to the surface of the cable and detects with an accuracy of 97.231%. This article analyzes the force distribution on the sensor surface and static loading by ANSYS, considering the fluid-structure coupling effect, and combined with wind tunnel tests to simulate the vibration response of the sensor when the cable of the cable-stayed bridge is affected by wind. An intelligent monitoring system for cable-stayed bridges was designed using visual studio. The system can upload and display wind pressure field data in real time, enabling the prediction and assessment of the bridge’s health status. Finally, the sensor performs feature extraction and state recognition of the collected data, and a machine learning algorithm based on approximate entropy and BP neural networks was used to achieve classification and recognition of the wind pressure field data (97.231%). This study provides new ideas on the aerodynamic characteristics of cable-stayed bridges and structural health inspection.

Implementation of bipedal stable walking has attracted a lot of interest. Passive dynamic walking (PDW) is one promising manner to generate natural bipedal walking with high energy efficiency. However, how to improve stability and versatility of PDW-based robot against disturbance and time varying environments is still a big challenge in robotics. Chaos and bifurcations are intrinsic features of biped dynamic walking, which are important reasons for the failure of PDW. Thus a hybrid chaotic controller to stabilize chaos and bifurcations of PDW was proposed in this paper. At first, the dynamics model of compliant biped robot was set up, and routine to bipedal chaotic motions was found through parameter study. Then one hybrid chaotic controller integrating hip stiffness modulation and hip impulse torque control was developed, where hip impulse torque control based on reinforcement learning was trained to get state variables close to fixed point of PDW, and then hip stiffness modulation was conducted based on Ott-Grebogi-Yorke method to stabilize unstable motions of fixed point. Simulation results showed that period-1 stable walking could be gained for biped robots from chaotic motions, against original value disturbance, force disturbance and in time varying environments. The proposed hybrid chaotic controller could be used to stabilize bipedal chaotic motions and make the passivity-based robot become robust and versatile to disturbed and time changing environments.

Owing to the advantages of safety and reproducibility, remote center of motion (RCM) mechanisms are widely adopted in lumbar puncture (LP) procedures to guide the insertion angle and depth of the end effector. However, the proximal-actuated pattern in existing RCM mechanisms occupies a large space near the end effector, which obstructs the visual field and increases the system inertia. In this work, a base-actuated three-rhombus configured RCM mechanism for LP operation is first proposed, where the symmetric three-rhombus scheme is designed for motion transmission. As a result, the rotational and translational motions of the needle are respectively realized through the homodromous and heterodromous actuation of the two base-mounted motors. Kinematic models are established to analyze the manipulability, singularity, and workspace of the RCM mechanism theoretically. The parameter optimization procedure is provided to minimize the footprint of the RCM mechanism. Experimental results show that the mechanism reaches an insertion angle from −29.2 deg to 29.2 deg, a maximum insertion depth of 60.02 mm, and a footprint of 4.98 × 104 mm2. The relative error of the RCM point is 1.1 mm.

Passive dynamic walking exhibits human-like and energy-efficient gait. Biologically inspired compliance introduced to flexible passivity-based robot would be helpful to generate stable locomotion. However, designing adaptive controller for flexible biped on compliant ground still remains a challenge. This paper aims to design an adaptive and model-free stiffness controller for passivity-based flexible biped on compliant ground, where the hip stiffness is modulated by double deep Q network. One benefit of the double deep Q network is that adaptive stiffness control policy could be directly learned from inputs. At first, passive dynamic walking gait is utilized as a reference trajectory during double deep Q network training. Then the trained double deep Q network is used as adaptive stiffness controller for biped on compliant ground. Simulation results show that the passivity-based biped robot could walk in such walking cases as disturbed initial condition, level compliant ground, downslope slippery compliant surface, and varying compliance environments. The adaptive stiffness controller would be used to make the passivity-based biped robot adapt to the environmental changes.