As a friendly tool, the soft gripper can be used directly for grasping vulnerable objects in underwater environments. These tasks are generally performed by teleoperation based on the visual feedback, rather than the soft actuators’ sensing information. However, vision sensors’ function may be restricted in some complex underwater environments with poor visibility and narrow spaces. This will greatly reduce the efficiency of the underwater operations. Therefore, soft actuators strongly require an organism‐like perception system to sense environmental stimuli and can be applied to complex underwater environments. To address the problem, a multidirectional external perception soft actuator based on two flexible optical waveguide sensors is developed and machine learning methods are utilized to build its perceptual model herein. The experimental results indicate that the soft actuator can recognize 12 contact positions based on the sensing model, and the identifying accuracy is up to 99.82%. Additionally, according to the contact location feedback, teleoperation can be more efficiently completed in unknown underwater environments.
Full Text Finder