Soft bionic gripper with tactile sensing and slip detection for damage-free grasping of fragile fruits and vegetables

The robot gripper, as an interface for physical-information interaction between agricultural robots and the operating environment, has been widely used in agricultural production. The potential slipping risk during the grasping process is an important factor affecting safe gripping. Therefore, detecting the initial slipping during the gripping process and optimizing the force applied during gripping are key technologies for preventing slippage and achieving safe gripping. This paper proposes a slip detection method based on tactile sensing and time–frequency analysis, it can attain slip signal detection and grip force optimization during fragile fruits gripping. We have constructed a compliant robotic hand grasping system and developed a tactile information acquisition platform based on contact force sensors and bending sensors. This platform is capable of real-time monitoring and recording tactile sensing data during grasping experiments. To deeply analyze the tactile temporal information during the grasping process, we employed the Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT) algorithms, which precisely captured the variations in the contact force signal in the time–frequency domain during the sliding event. With the application of machine learning techniques, we not only achieved effective slip detection but also optimized the control of the grasping force, providing valuable references and guidance for non-destructive grasping technology in the field of agricultural robotics.