MakeSense: Automated Sensor Design for Proprioceptive Soft Robots

Abstract

Soft robots have applications in safe human-robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this article, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided soft robotic designs, which is optimized to perform well under a set of user-specified deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces. We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones, which minimizes the error when sensing specified deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables. We demonstrate our technique on a bending bar and gripper example, illustrating more complex designs with a simulated tentacle.

Topological Study on the Design of Soft Strain Sensors for Simultaneous Multi-point Contact Localization

“…This work presents a design study on the use of soft strain sensors for simultaneous multipoint contact localization. Similar design studies have been performed for proprioception [5], [6] and robust single-point detection [7], however, not for multi-point contact detection.…”

Intelligent Soft Surgical Robots for Next‐Generation Minimally Invasive Surgery

“…To solve this problem, Tapia et al recently presented a computational method that can automatically compute a minimal strain sensor network for specific soft robotic structures, through which proprioception of soft robots with any shape and size is possible. [72] Although strain measurement can help reconstruct the instrument shape, the end-position error in the process is difficult to control. Considering such a defect, it is necessary to introduce end-tracking technology in some precision operations to obtain higher accuracy.…”

“…Nonlinear responses and viscoelastic properties of soft materials are always the main obstacles for fluidic‐driven soft robots to reach precise control, and hence the strategies using traditional model‐based control only achieve very limited progress. [ 71 ] Feedback control using sensors is an ideal way to jump over these barriers, e.g., the works by Tapia [ 72 ] and Marchese. [ 73 ] Nevertheless, increasing the integration of soft devices without affecting the size requires more powerful fluidic actuators.…”

“…To solve this problem, Tapia et al recently presented a computational method that can automatically compute a minimal strain sensor network for specific soft robotic structures, through which proprioception of soft robots with any shape and size is possible. [ 72 ]…”

Real-World Robot Evolution: Why Would it (not) Work?

“…Firstly, robots with sensors and closed loop controllers that can take sensory inputs into account should be used. This is not only an engineering challenge, but also an extension of the scientific and conceptual framework from evolving body plans and brains to evolving body plans, sensors, and brains Tapia et al (2020);Ferigo et al (2021). Secondly, robots should be evolved for more and more practical tasks.…”