Journal of Bionic Engineering (2025) 22:739–754 https://doi.org/10.1007/s42235-025-00651-6

High-Performance Bionic Tactile Sensing Method for Temperature  and Pressure Based on Triboelectric Nanogenerator and Micro-Thermoelectric Generator 

Changxin Liu1  · Runhe Chen1  · Peihan Huang1  · Guangyi Xing1  · Zhijie Hao1  · Haoxuan Che1  · Dazhi Zhang1  ·  Rongxin Zhang2  · Mingyu Lu3

1 Marine Engineering College, Dalian Maritime University,  116026 Dalian, People’s Republic of China 

2 Department of Dermatology, The Second Hospital of Dalian  Medical University, 116027 Dalian, People’s Republic of  China 

3 College of Artificial Intelligence, Dalian Maritime  University, Dalian, People’s Republic of China

Abstract 

In intricate aquatic environments, enhancing the sensory performance of underwater actuators to ensure successful task  execution is a significant challenge. To address this, a biomimetic tactile multimodal sensing approach is introduced in  this study, based on TriboElectric NanoGenerator (TENG) and Micro-ThermoElectric Generator (MTEG). This method  enables actuators to identify the material properties of underwater target objects and to sense grasping states, such as  pressure and relative sliding. In this study, a multi-dimensional underwater bionic tactile perception theoretical model is  established, and a bionic sensing prototype with a sandwich-type structure is designed. To verify the performance of pressure feedback and material perception, pertinent experiments are conducted. The experimental results reveal that within a  pressure measurement range of 0–16 N, the detection error of the sensor is 1.81%, and the maximum pressure response  accuracy achieves 2.672 V/N. The sensing response time of the sensor is 0.981 s. The recovery time of the sensor is 0.97 s.  Furthermore, the exceptional fatigue resistance of the sensor is also demonstrated. Based on the frequency of the output  voltage from the prototype, the sliding state of the target object relative to the actuator can be sensed. In terms of material identification, the temperature response accuracy of the sensor is 0.072 V/°C. With the assistance of machine learning  methods, six characteristic materials are identified by the sensor under 7 N pressure, with a recognition accuracy of 92.4%.  In complex marine environments, this method has great application potential in the field of underwater tactile perception. 

Keywords Bionic multi-dimensional sensing · Triboelectric nanogenerator · Micro-thermoelectric generator ·  Identification of characteristic material · Grasping pressure perception