Trenched microwave resonator integrated with porous PDMS for detection and classification of VOCs with enhanced performance.

Microwave resonators combined with polymer absorption layers are widely used in volatile organic compound (VOC) detection based on their variable resonant frequencies. However, the response time is limited due to the polymer's slow volumetric absorption of VOC molecules. By constructing a porous structure in Polydimethylsiloxane (PDMS), resulting in reduced the response time to as short as 71.1%. To mitigate the sensitivity decline caused by the porous PDMS, a trenched-substrate complementary split-ring resonator (CSRR) is proposed for enhancing the interaction between the electromagnetic fields (EMFs) and the porous PDMS with VOCs. The removal of the substrate beneath CSRR's sensing region enhances the effective EMF, increasing frequency and amplitude sensitivities up to 175.5% and 137.8%, respectively. Responses to four common VOCs by the sensor show a maximum sensitivity of 217 Hz/ppm and a minimum limit of detection of 295 ppm. Additionally, resonant parameters and extracted lumped parameters are utilized to establish two decision-tree-based VOC classification models, achieving high accuracies of 98.71% and 99.59%, respectively. And the latter one fully utilizing responses throughout the swept band, proves superior in identifying similar substances. This sensor technology helps promote the sensitive detection and accurate classification of diverse VOCs.

Sensitivity Enhancement of Soft Capacitive Pressure Sensors Using a Solvent Evaporation-Based Porosity Control Technique.

Soft pressure sensors play a significant role in developing "man-machine" tactile sensation in soft robotics and haptic interfaces. Specifically, capacitive sensors with micro-structured polymer matrices have been explored with considerable effort because of their high sensitivity, wide linearity range, and fast response time. However, the improvement of the sensing performance often relies on the structural design of the dielectric layer, which requires sophisticated microfabrication facilities. This article reports a simple and low-cost method to fabricate porous capacitive pressure sensors with improved sensitivity using the solvent evaporation-based method to tune the porosity. The sensor consists of a porous polydimethylsiloxane (PDMS) dielectric layer bonded with top and bottom electrodes made of elastic conductive polymer composites (ECPCs). The electrodes were prepared by scrape-coating carbon nanotubes (CNTs)-doped PDMS conductive slurry into mold-patterned PDMS films. To optimize the porosity of the dielectric layer for enhanced sensing performance, the PDMS solution was diluted with toluene of different mass fractions instead of filtering or grinding the sugar pore-forming agent (PFA) into different sizes. The evaporation of the toluene solvent allowed the fast fabrication of a porous dielectric layer with controllable porosities. It was confirmed that the sensitivity could be enhanced more two-fold when the toluene to PDMS ratio was increased from 1:8 to 1:1. The research proposed in this work enables a low-cost method of fabricating fully integrated bionic soft robotic grippers with soft sensory mechanoreceptors of tunable sensor parameters.

Adaptive Locomotion Control of a Hexapod Robot via Bio-Inspired Learning.

In this paper, an adaptive locomotion control approach for a hexapod robot is proposed. Inspired from biological neuro control systems, a 3D two-layer artificial center pattern generator (CPG) network is adopted to generate the locomotion of the robot. The first layer of the CPG is responsible for generating several basic locomotion patterns and the functional configuration of this layer is determined through kinematics analysis. The second layer of the CPG controls the limb behavior of the robot to adapt to environment change in a specific locomotion pattern. To enable the adaptability of the limb behavior controller, a reinforcement learning (RL)-based approach is employed to tune the CPG parameters. Owing to symmetrical structure of the robot, only two parameters need to be learned iteratively. Thus, the proposed approach can be used in practice. Finally, both simulations and experiments are conducted to verify the effectiveness of the proposed control approach.

Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot.

Soft robots have recently received much attention with their infinite degrees of freedoms and continuously deformable structures, which allow them to adapt well to the unstructured environment. A new type of soft actuator, namely, dielectric elastomer actuator (DEA) which has several excellent properties such as large deformation and high energy density is investigated in this study. Furthermore, a DEA-based soft robot is designed and developed. Due to the difficulty of accurate modeling caused by nonlinear electromechanical coupling and viscoelasticity, the iterative learning control (ILC) method is employed for the motion trajectory tracking with an uncertain model of the DEA. A D 2 type ILC algorithm is proposed for the task. Furthermore, a knowledge-based model framework with kinematic analysis is explored to prove the convergence of the proposed ILC. Finally, both simulations and experiments are conducted to demonstrate the effectiveness of the ILC, which results show that excellent tracking performance can be achieved by the soft crawling robot.