Polymer sensors for underwater robot proprioception

The natural swimming ability of fish has inspired the development of stealthy and quiet fish-like robots for a range of applications, from defense to wildlife research, where it is desirable to observe animals in a non-threatening way. A realistic fish-robot will benefit from something resembling proprioception, enabling body and fin position to be sensed for the control of swimming actuators. To demonstrate cyber-proprioception, a stretchy neoprene sensing skin embedded with discrete dielectric elastomer stretch sensors could be used. As the fish-robot body bends, the sensor integrated skin stretches resulting in measurable deformation to the sensors. One of the key challenges however, is stretchy sensors that operate reliably and repeatably in a marine environment. In this study we present a sensor design suitable for underwater use, and compare it with the more commonly used structure. Both sensor designs have been simulated in air and fresh water environments, and have been subjected to strain, sensitivity and cross-talk experiments. We show that the new sensor design renders them insensitive to changes in the dielectric properties of the medium in which they are immersed and to cross-talk noise. As a demonstration we then integrate eight of the sensors, 4 on each side, on a submerged underactuated tensegrity fish-like robot based on the Wahoo (Acanthrocybium solandri), a fast pelagic swimmer. Carangiform movement of the robot’s body has been characterized using cameras and this data has been compared with a virtual model of the robot that uses sensor input for real-time model kinematic data. Extremes of shape change associated with escape and prey capture (C-start and S maneuvers) have also been compared. Four angles along the body defined the shape using camera and sensor data. A root mean square error between model and true camera angles of less than 2.62° was calculated for realistic cangiform motion at 0.16 Hz tail frequency. For the extremes of shape, the sensor-based predictions had an R2 -Score of 0.98 for C and 0.99 for S. When the algorithm was tested on new shapes, the R2 score dropped to 0.81 and 0.79 respectively suggesting some model overfitting. The results support the efficacy of the use of a sensing skin for a soft fish-swimming robot.