An earthworm-like modular soft robot for locomotion in multi-terrain environments.

Robotic locomotion in subterranean environments is still unsolved, and it requires innovative designs and strategies to overcome the challenges of burrowing and moving in unstructured conditions with high pressure and friction at depths of a few centimeters. Inspired by antagonistic muscle contractions and constant volume coelomic chambers observed in earthworms, we designed and developed a modular soft robot based on a peristaltic soft actuator (PSA). The PSA demonstrates two active configurations from a neutral state by switching the input source between positive and negative pressure. PSA generates a longitudinal force for axial penetration and a radial force for anchorage, through bidirectional deformation of the central bellows-like structure, which demonstrates its versatility and ease of control. The performance of PSA depends on the amount and type of fluid confined in an elastomer chamber, generating different forces and displacements. The assembled robot with five PSA modules enabled to perform peristaltic locomotion in different media. The role of friction was also investigated during experimental locomotion tests by attaching passive scales like earthworm setae to the ventral side of the robot. This study proposes a new method for developing a peristaltic earthworm-like soft robot and provides a better understanding of locomotion in different environments.

Iterative simulations to estimate the elastic properties from a series of MRI images followed by MRI-US validation.

The modeling of breast deformations is of interest in medical applications such as image-guided biopsy, or image registration for diagnostic purposes. In order to have such information, it is needed to extract the mechanical properties of the tissues. In this work, we propose an iterative technique based on finite element analysis that estimates the elastic modulus of realistic breast phantoms, starting from MRI images acquired in different positions (prone and supine), when deformed only by the gravity force. We validated the method using both a single-modality evaluation in which we simulated the effect of the gravity force to generate four different configurations (prone, supine, lateral, and vertical) and a multi-modality evaluation in which we simulated a series of changes in orientation (prone to supine). Validation is performed, respectively, on surface points and lesions using as ground-truth data from MRI images, and on target lesions inside the breast phantom compared with the actual target segmented from the US image. The use of pre-operative images is limited at the moment to diagnostic purposes. By using our method we can compute patient-specific mechanical properties that allow compensating deformations. Graphical Abstract Workflow of the proposed method and comparative results of the prone-to-supine simulation (red volumes) validated using MRI data (blue volumes).

Analytical derivation of elasticity in breast phantoms for deformation tracking.

PURPOSE: Patient-specific biomedical modeling of the breast is of interest for medical applications such as image registration, image guided procedures and the alignment for biopsy or surgery purposes. The computation of elastic properties is essential to simulate deformations in a realistic way. This study presents an innovative analytical method to compute the elastic modulus and evaluate the elasticity of a breast using magnetic resonance (MRI) images of breast phantoms. METHODS: An analytical method for elasticity computation was developed and subsequently validated on a series of geometric shapes, and on four physical breast phantoms that are supported by a planar frame. This method can compute the elasticity of a shape directly from a set of MRI scans. For comparison, elasticity values were also computed numerically using two different simulation software packages. RESULTS: Application of the different methods on the geometric shapes shows that the analytically derived elongation differs from simulated elongation by less than 9% for cylindrical shapes, and up to 18% for other shapes that are also substantially vertically supported by a planar base. For the four physical breast phantoms, the analytically derived elasticity differs from numeric elasticity by 18% on average, which is in accordance with the difference in elongation estimation for the geometric shapes. The analytic method has shown to be multiple orders of magnitude faster than the numerical methods. CONCLUSION: It can be concluded that the analytical elasticity computation method has good potential to supplement or replace numerical elasticity simulations in gravity-induced deformations, for shapes that are substantially supported by a planar base perpendicular to the gravitational field. The error is manageable, while the calculation procedure takes less than one second as opposed to multiple minutes with numerical methods. The results will be used in the MRI and Ultrasound Robotic Assisted Biopsy (MURAB) project.

Surgical robot simulation with BBZ console.

This paper presents a lean approach to training in robot assisted surgery. Minimally Invasive Surgical procedures can be decomposed in a sequence of tasks, each surgical task can be further decomposed in basic gestures. Each surgical gesture seems similar to perform rather in laparoscopic than in robot assisted technique, but surgeon posture, tools dexterity, force and vision feedback are different. As a consequence, performing a robot-assisted procedure needs specific training. Currently, the most used robot in in abdominal and pelvic surgery is the da Vinci Surgical System and a different set of skills is needed to master the human-machine interface of this device. The training with the real robot is very expensive due to the high initial cost of purchasing and maintaining the robotic surgical system, and the ethic involved in vivo practice. For these reasons, different training systems based on virtual reality were developed. The simulation physics realism and the objective metrics collected during the task execution are the main features for the effectiveness of a virtual reality based training device. Availability of training systems is another issue. To help surgeons to train in virtual reality, BBZ presents a compact, lightweight and portable console, suitable also for "home" training.

A Deformable Smart Skin for Continuous Sensing Based on Electrical Impedance Tomography.

In this paper, we present a low-cost, adaptable, and flexible pressure sensor that can be applied as a smart skin over both stiff and deformable media. The sensor can be easily adapted for use in applications related to the fields of robotics, rehabilitation, or costumer electronic devices. In order to remove most of the stiff components that block the flexibility of the sensor, we based the sensing capability on the use of a tomographic technique known as Electrical Impedance Tomography. The technique allows the internal structure of the domain under study to be inferred by reconstructing its conductivity map. By applying the technique to a material that changes its resistivity according to applied forces, it is possible to identify these changes and then localise the area where the force was applied. We tested the system when applied to flat and curved surfaces. For all configurations, we evaluate the artificial skin capabilities to detect forces applied over a single point, over multiple points, and changes in the underlying geometry. The results are all promising, and open the way for the application of such sensors in different robotic contexts where deformability is the key point.

Hydrodynamic pressure sensing with an artificial lateral line in steady and unsteady flows.

With the overall goal being a better understanding of the sensing environment from the local perspective of a situated agent, we studied uniform flows and Kármán vortex streets in a frame of reference relevant to a fish or swimming robot. We visualized each flow regime with digital particle image velocimetry and then took local measurements using a rigid body with laterally distributed parallel pressure sensor arrays. Time and frequency domain methods were used to characterize hydrodynamically relevant scenarios in steady and unsteady flows for control applications. Here we report that a distributed pressure sensing mechanism has the capability to discriminate Kármán vortex streets from uniform flows, and determine the orientation and position of the platform with respect to the incoming flow and the centre axis of the Kármán vortex street. It also enables the computation of hydrodynamic features which may be relevant for a robot while interacting with the flow, such as vortex shedding frequency, vortex travelling speed and downstream distance between vortices. A Kármán vortex street was distinguished in this study from uniform flows by analysing the magnitude of fluctuations present in the sensor measurements and the number of sensors detecting the same dominant frequency. In the Kármán vortex street the turbulence intensity was 30% higher than that in the uniform flow and the sensors collectively sensed the vortex shedding frequency as the dominant frequency. The position and orientation of the sensor platform were determined via a comparative analysis between laterally distributed sensor arrays; the vortex travelling speed was estimated via a cross-correlation analysis among the sensors.

What information do Kármán streets offer to flow sensing?

In this work, we focus on biomimetic lateral line sensing in Kármán vortex streets. After generating a Kármán street in a controlled environment, we examine the hydrodynamic images obtained with digital particle image velocimetry (DPIV). On the grounds that positioning in the flow and interaction with the vortices govern bio-inspired underwater locomotion, we inspect the fluid in the swimming robot frame of reference. We spatially subsample the flow field obtained using DPIV to emulate the local flow around the body. In particular, we look at various sensor configurations in order to reliably identify the vortex shedding frequency, wake wavelength and downstream flow speed. Moreover, we propose methods that differentiate between being in and out of the Kármán street with >70% accuracy, distinguish right from left with respect to Kármán vortex street centreline (>80%) and highlight when the sensor system enters the vortex formation zone (>75%). Finally, we present a method that estimates the relative position of a sensor array with respect to the vortex formation point within 15% error margin.