Robust Estimation of Contact Force and Location for Magnetic-Field-Based Soft Tactile Sensor Considering Magnetic Source Inconsistency

Flexible magnetic-field-based tactile sensors (FMFTS) have numerous advantages including low cost, ease of manufacture, simple wiring, high sensitivity, and so on. Flexible magnetic-field-based tactile sensors need to be calibrated before use to build accurate mapping between contact force and magnetic field intensity measured by magnetic sensors; however, when considering remanence inconsistency of magnetic source, each FMFTS needs to be calibrated independently to enhance accuracy, and the complex preparation prevents FMFTS from being used conveniently. A robust estimation method of contact force and location that can tolerate remanence inconsistency of magnetic source in FMFTS is proposed. Firstly, the position and orientation of magnetic source were tracked using the Levenberg-Marquart algorithm, and the tracking results were insensitive to the remanence of magnetic source with appropriate cost function. Secondly, the mapping between magnitude and location of contact force and position and orientation of magnetic source was built with calibration of one sensor; the mapping only depends on the structural response of flexible substrate, and thus can be extended to estimate external force and location for other sensors with the same structure. The proposed method was evaluated in both simulations and experiments, and the results confirm that the estimation of magnitude and location of external force for FMFTS with the same structure and different remanence could reach acceptable accuracy, depending on single calibration. The proposed method can be used to simplify the calibration procedure and remove the barrier for large-scale application of FMFTS and replacement of damaged FMFTS.

Controlling subterranean forces enables a fast, steerable, burrowing soft robot

Robotic navigation on land, through air, and in water is well researched; numerous robots have successfully demonstrated motion in these environments. However, one frontier for robotic locomotion remains largely unexplored-below ground. Subterranean navigation is simply hard to do, in part because the interaction forces of underground motion are higher than in air or water by orders of magnitude and because we lack for these interactions a robust fundamental physics understanding. We present and test three hypotheses, derived from biological observation and the physics of granular intrusion, and use the results to inform the design of our burrowing robot. These results reveal that (i) tip extension reduces total drag by an amount equal to the skin drag of the body, (ii) granular aeration via tip-based airflow reduces drag with a nonlinear dependence on depth and flow angle, and (iii) variation of the angle of the tip-based flow has a nonmonotonic effect on lift in granular media. Informed by these results, we realize a steerable, root-like soft robot that controls subterranean lift and drag forces to burrow faster than previous approaches by over an order of magnitude and does so through real sand. We also demonstrate that the robot can modulate its pullout force by an order of magnitude and control its direction of motion in both the horizontal and vertical planes to navigate around subterranean obstacles. Our results advance the understanding and capabilities of robotic subterranean locomotion.

Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

Design, Modeling, Control, and Application of Everting Vine Robots

In nature, tip-localized growth allows navigation in tightly confined environments and creation of structures. Recently, this form of movement has been artificially realized through pressure-driven eversion of flexible, thin-walled tubes. Here we review recent work on robots that "grow" via pressure-driven eversion, referred to as "everting vine robots," due to a movement pattern that is similar to that of natural vines. We break this work into four categories. First, we examine the design of everting vine robots, highlighting tradeoffs in material selection, actuation methods, and placement of sensors and tools. These tradeoffs have led to application-specific implementations. Second, we describe the state of and need for modeling everting vine robots. Quasi-static models of growth and retraction and kinematic and force-balance models of steering and environment interaction have been developed that use simplifying assumptions and limit the involved degrees of freedom. Third, we report on everting vine robot control and planning techniques that have been developed to move the robot tip to a target, using a variety of modalities to provide reference inputs to the robot. Fourth, we highlight the benefits and challenges of using this paradigm of movement for various applications. Everting vine robot applications to date include deploying and reconfiguring structures, navigating confined spaces, and applying forces on the environment. We conclude by identifying gaps in the state of the art and discussing opportunities for future research to advance everting vine robots and their usefulness in the field.