Autonomous failure detection and multimodal sensor fusion in a modular arm model

When soft robots act in new or unforeseen situations, uncertainty becomes a major challenge. Sensors might fail and the forward kinematics might become faulty if limbs are deformed. To meet this challenge, we propose a model that uses additional sensors in different modalities and provides a highly modular body model which can be easily adapted. Longterm sensor-blackout is covered by usage of multiple sensors in different modalities, while short term sensor-blackout is covered by the maintenance of state representations over time and the inclusion of a full forward and inverse model suitable for prediction and simulation. Even more, as the state is represented redundantly in multiple modalities, the system compares the estimates to detect changes in sensor noise or even sensor offsets and discards failing sensors. Finally, the highly modularized architecture of our representation simplifies the adaptation of the body model.