With the emerging need for human–machine interactions, multi-modal sensory interaction is gradually pursued rather than satisfying common perception forms (visual or auditory), so developing flexible, adaptive, and stiffness-variable force-sensing devices is the key to further promoting human–machine fusion. However, current sensor sensitivity is fixed and nonadjustable after fabrication, limiting further development. To solve this problem, we propose an origami-inspired structure to achieve multiple degrees of freedom (DoFs) motions with variable stiffness for force-sensing, which combines the ductility and flexibility of origami structures. In combination with the pneumatic actuation, the structure can achieve and adapt the compression, pitch, roll, diagonal, and array motions (five motion modes), which significantly increase the force adaptability and sensing diversity. To achieve closed-loop control and avoid excessive gas injection, the ultra-flexible microfiber sensor is designed and seamlessly embedded with an approximately linear sensitivity of ∼0.35 Ω/kPa at a relative pressure of 0–100 kPa, and an exponential sensitivity at a relative pressure of 100–350 kPa, which can render this device capable of working under various conditions. The final calibration experiment demonstrates that the pre-pressure value can affect the sensor’s sensitivity. With the increasing pre-pressure of 65–95 kPa, the average sensitivity curve shifts rightwards around 9 N intervals, which highly increases the force-sensing capability towards the range of 0–2 N. When the pre-pressure is at the relatively extreme air pressure of 100 kPa, the force sensitivity value is around 11.6 Ω/N. Therefore, our proposed design (which has a low fabrication cost, high integration level, and a suitable sensing range) shows great potential for applications in flexible force-sensing development.
Intraoperative tumor detection and shape identification through manual palpation are routinely performed in traditional open surgeries to ensure complete tumor resection. However, most existing robot-assisted minimally invasive surgery (RMIS) systems lack tactile feedback and rely on vision heavily. Traditional tactile sensing methods require the sensor to be placed normal to the tissue surface. But this requirement cannot always be met due to the limited degrees of freedom and the complexity of the environment in confined spaces. This paper proposes a miniaturized piezoelectric tactile sensor for tissue hardness detection by measuring its electrical impedance spectrum. It has two unique detection modes in two orthogonal directions—transverse and longitudinal, and can detect hardness even when the contact angle is large. It is verified by simulations and experiments that both detection modes can be used to detect hardness in the normal contact condition. However, in the case of hardness detection at a large contact angle, the sensitivity of the sensor in the transverse detection mode is significantly higher than that in the longitudinal mode, implying that this mode is more suitable for the large-angle detection. The sensor is then tested on silicone phantoms with hard inclusions and also on an ex vivo porcine liver. In addition, a tactile imaging algorithm based on Gaussian process regression is used to generate the complete hardness distribution of the test sample, which is further processed to extract the shape and boundary of the hard inclusion. The results show that the accuracy of shape detection is high (recall ⩾ 95%, specificity ⩾ 97%), and the smallest feature size it could detect is 1.3 mm. This proves that the proposed tactile sensor has the potential to perform high-accuracy tumor detection in RMIS.
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