An End-to-End Dynamic Posture Perception Method for Soft Actuators Based on Distributed Thin Flexible Porous Piezoresistive Sensors

This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. To achieve this, the method employs an array of miniaturized sponge resistive materials along the soft actuator, which uses long short-term memory (LSTM) neural networks to solve the end-to-end 3D posture for the soft actuators. The method takes into account the hysteresis of the soft robot and non-linear sensing signals from the flexible bending sensors. The proposed approach uses a flexible bending sensor made from a thin layer of conductive sponge material designed for posture sensing. The LSTM network is used to model the posture of the soft actuator. The effectiveness of the method has been demonstrated on a finger-size 3 degree of freedom (DOF) pneumatic bellow-shaped actuator, with nine flexible sponge resistive sensors placed on the soft actuator's outer surface. The sensor-characterizing results show that the maximum bending torque of the sensor installed on the actuator is 4.7 Nm, which has an insignificant impact on the actuator motion based on the working space test of the actuator. Moreover, the sensors exhibit a relatively low error rate in predicting the actuator tip position, with error percentages of 0.37%, 2.38%, and 1.58% along the x-, y-, and z-axes, respectively. This work is expected to contribute to the advancement of soft robot dynamic posture perception by using thin sponge sensors and LSTM or other machine learning methods for control.

Fully rubbery Schottky diode and integrated devices

A fully rubbery stretchable diode, particularly entirely based on stretchy materials, is a crucial device for stretchable integrated electronics in a wide range of applications, ranging from energy to biomedical, to integrated circuits, and to robotics. However, its development has been very nascent. Here, we report a fully rubbery Schottky diode constructed all based on stretchable electronic materials, including a liquid metal cathode, a rubbery semiconductor, and a stretchable anode. The rubbery Schottky diode exhibited a forward current density of 6.99 × 10-3 A/cm2 at 5 V and a rectification ratio of 8.37 × 104 at ±5 V. Stretchy rectifiers and logic gates based on the rubbery Schottky diodes were developed and could retain their electrical performance even under 30% tensile stretching. With the rubbery diodes, fully rubbery integrated electronics, including an active matrix multiplexed tactile sensor and a triboelectric nanogenerator-based power management system, are further demonstrated.

Wide-Range Flexible Capacitive Pressure Sensors Based on Dielectrics with Various Porosity

Wide-range flexible pressure sensors are in difficulty in research while in demand in application. In this paper, a wide-range capacitive flexible pressure sensor is developed with the foaming agent ammonium bicarbonate (NH₄HCO₃). By controlling the concentration of NH₄HCO₃ doped in the polydimethylsiloxane (PDMS) and repeating the curing process, pressure-sensitive dielectrics with various porosity are fabricated to expand the detection range of the capacitive pressure sensor. The shape and the size of each dielectric is defined by the 3D printed mold. To improve the dielectric property of the dielectric, a 1% weight ratio of multi-walled carbon nanotubes (MWCNTs) are doped into PDMS liquid. Besides that, a 5% weight ratio of MWCNTs is dispersed into deionized water and then coated on the electrodes to improve the contact state between copper electrodes and the dielectric. The laminated dielectric layer and two electrodes are assembled and tested. In order to verify the effectiveness of this design, some reference devices are prepared, such as sensors based on the dielectric with uniform porosity and a sensor with common copper electrodes. According to the testing results of these sensors, it can be seen that the sensor based on the dielectric with various porosity has higher sensitivity and a wider pressure detection range, which can detect the pressure range from 0 kPa to 1200 kPa and is extended to 300 kPa compared with the dielectric with uniform porosity. Finally, the sensor is applied to the fingerprint, finger joint, and knee bending test. The results show that the sensor has the potential to be applied to human motion detection.

A Miniature Soft Sensor with Origami-Inspired Self-Folding Parallel Mechanism

Miniature soft sensors are crucial for the perception of soft robots. Although centimeter-scale sensors have been well developed, very few works addressed millimeter-scale, three-dimensional-shaped soft sensors capable of measuring multi-axis forces. In this work, we developed a millimeter-scale (overall size of 6 mm × 11 mm × 11 mm) soft sensor based on liquid metal printing technology and self-folding origami parallel mechanism. The origami design of the sensor enables the soft sensor to be manufactured within the plane and then fold into a three-dimensional shape. Furthermore, the parallel mechanism allows the sensor to rotate along two orthogonal axes. We showed that the soft sensor can be self-folded (took 17 s) using a shape-memory polymer and magnets. The results also showed that the sensor prototype can reach a deformation of up to 20 mm at the tip. The sensor can realize a measurement of external loads in six directions. We also showed that the soft sensor enables underwater sensing with a minimum sensitivity of 20 mm/s water flow. This work may provide a new manufacturing method and insight into future millimeter-scale soft sensors for bio-inspired robots.

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

In the past decade, the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare, Internet of Things, human-machine interfaces, artificial intelligence and soft robotics. Among them, flexible humidity sensors play a vital role in noncontact measurements relying on the unique property of rapid response to humidity change. This work presents an overview of recent advances in flexible humidity sensors using various active functional materials for contactless monitoring. Four categories of humidity sensors are highlighted based on resistive, capacitive, impedance-type and voltage-type working mechanisms. Furthermore, typical strategies including chemical doping, structural design and Joule heating are introduced to enhance the performance of humidity sensors. Drawing on the noncontact perception capability, human/plant healthcare management, human-machine interactions as well as integrated humidity sensor-based feedback systems are presented. The burgeoning innovations in this research field will benefit human society, especially during the COVID-19 epidemic, where cross-infection should be averted and contactless sensation is highly desired.

Smart deployment of IoT-TelosB service care StreamRobot using software-defined reliability optimisation design

Intelligent service care robots have increasingly been developed in mission-critical sectors such as healthcare systems, transportation, manufacturing, and environmental applications. The major drawbacks include the open-source Internet of Things (IoT) platform vulnerabilities, node failures, computational latency, and small memory capacity in IoT sensing nodes. This article provides reliable predictive analytics with the optimisation of data transmission characteristics in StreamRobot. Software-defined reliable optimisation design is applied in the system architecture. For the IoT implementation, the edge system model formulation is presented with a focus on edge cluster log-normality distribution, reliability, and equilibrium stability considerations. A real-world scenario for accurate data streams generation from in-built TelosB sensing nodes is converged at a sink-analytic dashboard. Two-phase configurations, namely off-taker and on-demand, link-state protocols are mapped for deterministic data stream offloading. An orphan reconnection trigger mechanism is used for reliable node-to-sink resilient data transmissions. Data collection is achieved, using component-based programming in the experimental testbed. Measurement parameters are derived with TelosB IoT nodes. Reliability validations on remote monitoring and prediction processes are studied considering neural constrained software-defined networking (SDN) intelligence. An OpenFlow-SDN construct is deployed to offload traffic from the edge to the fog layer. At the core, fog detection-to-cloud predictive machine learning (FD-CPML) is used to predict real-time data streams. Prediction accuracy is validated with decision tree, logistic regression, and the proposed FD-CPML. The data streams latency gave 40.00%, 33.33%, and 26.67%, respectively. Similarly, linear predictive scalability behaviour on the network plane gave 30.12%, 33.73%, and 36.15% respectively. The results show satisfactory responses in terms of reliable communication and intelligent monitoring of node failures.