Abstract
Dense and distributed tactile sensors are critical for robots to achieve human-like manipulation skills. Soft robotic sensors are a potential technological solution to obtain the required high dimensional sensory information unobtrusively. However, the design of this new class of sensors is still based on human intuition or derived from traditional flex sensors. This work is a first step towards automated design of soft sensor morphologies based on optimization of information theory metrics and machine learning. Elementary simulation models are used to develop the optimized sensor morphologies that are more accurate and robust with the same number of sensors. Same configurations are replicated experimentally to validate the feasibility of such an approach for practical applications. Furthermore, we present a novel technique for drift compensation in soft strain sensors that allows us to obtain accurate contact localization. This work is an effort towards transferring the paradigm of morphological computation from soft actuator designing to soft sensor designing for high performance, resilient tactile sensory networks.
Highlights
S OFT strain sensors are becoming increasingly important for soft robotics and wearable electronic devices [1]
With the rise in novel materials, technologies and designs in the field of soft robotics, it is crucial that we look into other design methodologies for the development of soft sensors
The validation of the proposed methodology is done by experimentally comparing the performance of the square grid and the optimized sensor morphology with a N value of 5 (Fig. 10)
Summary
S OFT strain sensors are becoming increasingly important for soft robotics and wearable electronic devices [1]. They are vital to obtain intrinsic state information like contact, deformation, pressure and stress. Modeling these soft strain sensors is still a major challenge due to their high nonlinearity and time-variant properties [2]. The morphological design of these sensors have largely been overlooked, mainly because of the lack of analytical models. This work investigates an information theoretics-based approach to design better soft strain sensors. We look into the design of sensor morphologies that are more robust to damages/loss of data
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