We present an active acoustic sensor that turns soft pneumatic actuators into contact sensors. The whole surface of the actuator becomes a sensor, rendering the question of where best to place a contact sensor unnecessary. At the same time, the compliance of the soft actuator remains unaffected. A small, embedded speaker emits a frequency sweep which travels through the actuator before it is recorded with an embedded microphone. The specific contact state of the actuator affects how the sound is modulated while traversing the structure. We learn to recognize these changes in the sound and map them to the corresponding contact locations. We demonstrate the method on the PneuFlex actuator. The active acoustic sensor achieves a classification rate of 93% and mean regression error of 3.7mm. It is robust against background noises and different objects. Finally, we test it on a Panda robot arm and show that it is unaffected by motor noises and other active sensors.

We propose a novel sensing method for soft pneumatic actuators. The method uses a single microphone, embedded into the actuator's air chamber. Contact with the environment induces sound (vibration) in the actuator. The materials and the shape of the actuator reflect, refract, and attenuate the sound as it propagates inside the actuator. This produces a unique sound signature for different types of events, enabling the sensing of contact locations, contact force, and the type of contacted material. Sensing is insensitive to the inflation state of the actuator and to background noise. We demonstrate the robustness and versatility of the microphone-based sensor solution in experiments with a PneuFlex actuator. The proposed sensorization avoids the fundamental challenges of sensorizing soft pneumatic actuators, because the placement of a microphone does not negatively affect the compliance of the actuator and because a single microphone suffices for sensorization of the entire actuator, eliminating the need for an application-specific sensor layout.

The compliance of soft actuators makes manipulation safer and simplifies control. But their high flexibility also makes sensorization challenging. From the large space of possible deformations not all are equally important. We present a method for sensorization of soft actuators that, for a given application, finds an effective layout from a set of sensors. It starts from a redundant sensor layout and iteratively reduces the number of sensors. Applying the method to the PneuFlex actuators of the RBO Hand 2, we identify a layout of four liquid metal strain sensors and one pressure sensor to predict actuator deformation in three dimensions: flexional, lateral, and twist. Finally, the layout is used to build a sensorized RBO Hand 2. It can detect passive shape adaptation while grasping and reveals failure cases during manipulation, e.g. slipping fingers while opening a door.