Enhancing the performance of a resonance-based sensor network for soft robots using binary excitation

Embedded, flexible, multi-sensor sensing networks have shown the potential to provide soft robots with reliable feedback while navigating unstructured environments. Time delay associated with extracting information from these sensing networks and the complexity of constructing them are significant obstacles to their development. This paper presents a novel enhancement to an existing class of embedded sensor network with the potential to overcome these challenges. In its original version, this sensor network extracts information from multiple reactive sensors on a two-wire electrical circuit simultaneously. This paper proposes to change the excitation signal applied to this sensor network to a binary signal. This change offers two key advantages: it provides the ability to employ small, inexpensive microcontrollers and results in a faster data extraction process. The potential of this enhanced system is demonstrated here with a proof of concept implementation. The self-inductance of all inductance-based sensors within this proof of concept sensor network can be measured at a rate of over 5000 times per second with an average measurement error of less than 2%.