Unveiling sensor-induced behaviours
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Vision-based tactile sensor design using physically based rendering
High-resolution tactile sensors are very helpful to robots for fine-grained perception and manipulation tasks, but designing those sensors is challenging. This is because the designs are based on the compact integration of multiple optical elements, and it is difficult to understand the correlation between the element arrangements and the sensor accuracy by trial and error. In this work, we introduce the digital design of vision-based tactile sensors using a physically accurate light simulator. The framework modularizes the design process, parameterizes the sensor components, and contains an evaluation metric to quantify a sensor’s performance. We quantify the effects of sensor shape, illumination setting, and sensing surface material on tactile sensor performance using our evaluation metric. The proposed optical simulation framework can replicate the tactile image of the real vision-based tactile sensor prototype without any prior sensor-specific data. Using our approach we can substantially improve the design of a fingertip GelSight sensor. This improved design performs approximately 5 times better than previous state-of-the-art human-expert design at real-world robotic tactile embossed text detection. Our simulation approach can be used with any vision-based tactile sensor to produce a physically accurate tactile image. Overall, our approach enables the automatic design of sensorized soft robots and opens the door for closed-loop co-optimization of controllers and sensors for dexterous manipulation.
Ultrafast humidity sensor and transient humidity detections in high dynamic environments
Limited by the adsorption and diffusion rate of water molecules, traditional humidity sensors, such as those based on polymer electrolytes, porous ceramics, and metal oxides, typically have long response times, which hinder their application in monitoring transient humidity changes. Here we present an ultrafast humidity sensor with a millisecond-level response. The sensor is prepared by assembling monolayer graphene oxide quantum dots on silica microspheres using a simple electrostatic self-assembly technique. Benefiting from the joint action of the micro spheres and the ultrathin humidity-sensitive film, it displays the fastest response time (2.76 ms) and recovery time (12.4 ms) among electronic humidity sensors. With the ultrafast response of the sensor, we revealed the correlation between humidity changes in speech airflow and speech activities, demonstrated the noise immunity of humidity speech activity detection, confirmed the humidity shock caused by explosions, realized ultrahigh frequency respiratory monitoring, and verified the effect of humidity-triggering in the non-invasive ventilator. This ultrafast humidity sensor has broad application prospects in monitoring transient humidity changes.
A robust organic hydrogen sensor for distributed monitoring applications
Hydrogen is an abundant and clean energy source that could help to decarbonize difficult-to-electrify economic sectors. However, its safe deployment relies on the availability of cost-effective hydrogen detection technologies. We describe a hydrogen sensor that uses an organic semiconductor as the active layer. It can operate over a wide temperature and humidity range. Ambient oxygen p-dopes the organic semiconductor, which improves hole transport, and the presence of hydrogen reverses this doping process, leading to a drop in current and enabling reliable and rapid hydrogen detection. The sensor exhibits a high responsivity (more than 10,000), fast response time (less than 1 s), low limit of detection (around 192 ppb) and low power consumption (less than 2 μW). It can operate continuously for more than 646 days in ambient air at room temperature. We show that the sensor outperforms a commercial hydrogen detector in realistic sensing scenarios, illustrating its suitability for application in distributed sensor networks for early warning of hydrogen leaks and preventing explosions or fires.
Submersible touchless interactivity in conformable textiles enabled by highly selective overbraided magnetoresistive sensors
Miniature electronics positioned within textile braids leverages the persistent flexibility and comfort of textiles constructed from electronics with 1D form factors. Here, we developed touchless interactivity within textiles using 1D overbraided magnetic field sensors. Our integration strategy minimally impacts the performance of flexible giant magnetoresistive sensors, yielding machine-washable sensors that maintain conformability when integrated in traditional fabrics. These overbraided magnetoresistive sensors exhibit a detectivity down to 380 nT and a nearly isotropic magnetoresistance amplitude response, facilitating intuitive touchless interaction. The interactivity is possible even in humid environments, including underwater, opening reliable activation in day-to-day and specialized applications. To showcase capabilities of overbraided magnetoresistive sensors, we demonstrate a functional armband for navigation control in virtual reality environments and a self-monitoring safety helmet strap. This approach bridges the integration gap between on-skin and rigid magnetic interfaces, paving the way for highly reliable, comfortable, interactive textiles across entertainment, safety, and sportswear.
An intelligent humidity sensing system for human behavior recognition
An intelligent humidity sensing system has been developed for real-time monitoring of human behaviors through respiration detection. The key component of this system is a humidity sensor that integrates a thermistor and a micro-heater. This sensor employs porous nanoforests as its sensing material, achieving a sensitivity of 0.56 pF/%RH within a range of 60–90% RH, along with excellent long-term stability and superior gas selectivity. The micro-heater in the device provides a high operating temperature, enhancing sensitivity by 5.8 times. This significant improvement enables the capture of weak humidity variations in exhaled gases, while the thermistor continuously monitors the sensor’s temperature during use and provides crucial temperature information related to respiration. With the assistance of a machine learning algorithm, a behavior recognition system based on the humidity sensor has been constructed, enabling behavior states to be classified and identified with an accuracy of up to 96.2%. This simple yet intelligent method holds great potential for widespread applications in medical assistance analysis and daily health monitoring.
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