15 May 2023
Soft underwater animals such as octopuses leverage their softness when interacting with water to achieve complex manipulation actions or to maximize thrust generation. Proprioceptive sensing – or ‘self-sensing’ of its own body – is particularly key for these animals, as their limbs and structure are inherently underactuated. To be able to perform sensory-motor control of their limbs and adapt to changing environmental conditions such as currents, they must be able to understand the configuration of the body within a fluid.
This is also true for soft underwater bio-inspired robots which leverage soft underactuated or passive structures to locomote. Understanding the body deformation of the soft robot can allow for the optimization of actuation and, for that, proprioceptive sensing methods which do not hinder their softness are needed.
In this work, EMERGE partners from Delft University of Technology and collaborators propose a new sensing approach for soft underwater slender structures based on embedded pressure sensors and use a learning-based pipeline to link the sensor readings to the shape of the soft structure.
Using two different modeling techniques, the authors compare the pose reconstruction accuracy and identify the optimal approach. Using the proprioceptive sensing capabilities, they show how this information can be used to assess the swimming performance over metrics such as swimming thrust, tip deflection, and the traveling wave index. They conclude by demonstrating the robustness of the embedded sensor on a free swimming soft robotic squid swimming at a maximum velocity of 9.5 cm/s, with the absolute tip deflection being predicted within an error less than 9% without the aid of external sensors.
Read the paper: https://doi.org/10.1109/RoboSoft55895.2023.10121999
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