Smell Driven Navigation for Soft Robotic Arms
Artificial Nose and Control
F. Piqué (Scuola Superiore Sant’Anna)
F. Stella (École Polytechnique Fédérale de Lausanne, TU Delft - Learning & Autonomous Control)
Josie Hughes (École Polytechnique Fédérale de Lausanne)
Egidio Falotico (Scuola Superiore Sant’Anna)
C. Della Santina (TU Delft - Learning & Autonomous Control, Deutsches Zentrum für Luft- und Raumfahrt (DLR))
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Abstract
Elephants and other animals heavily rely on the sense of smell to operate. Soft robots would also benefit from an artificial sense of smell, which could be helpful in typical soft robotic tasks such as search and rescue, pipe inspection, and all the tasks involving unstructured environments. This work proposes an artificial nose on a soft robotic arm that ensures separate smell concentration readings. We propose designing the nose to generate a one-to-one matching between the sensors' inputs and the actuators. This design choice allows us to implement a simple control strategy tailored to reach a dynamically varying smell in the environment, which we validate on a two-segment tendon-driven soft robotic arm equipped with the proposed artificial nose. We also propose and validate in simulation a control strategy for reaching tasks in the case of a stationary smell