Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot

Reinhart F, Steil JJ. Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot. In: Procedia Technology. Vol 26. 2016: 12-19

Constant curvature continuum kinematics as fast approximate model for the Bionic Handling Assistant

Rolf M, Steil JJ. Constant curvature continuum kinematics as fast approximate model for the Bionic Handling Assistant. In: IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS). Vilamoura, Portugal; 2012: 3440-3446

Robots Hiper-Redundantes: Clasificación, Estado del Arte y Problemática

Los robots hiper-redundantes son aquellos que tienen un número muy elevado de grados de libertad. En su uso cotidiano, la redundancia es referida para indicar una repetición o un uso excesivo de un concepto. En el campo de la robótica, la redundancia puede ofrecer numerosos beneficios frente a los robots convencionales. Los robots hiper-redundantes poseen una mayor habilidad para sortear obstáculos, son tolerantes a fallos en algunas de sus articulaciones y también pueden ofrecer ventajas cinemáticas. En este artículo se presentan los conceptos generales para entender este tipo de robots, así como una clasificación de los mismos, su potencial, su problemática y su evolución a lo largo de la historia

Study and development of stretchable sensors for flexible surgical instrumentation.

Recently, attention has been focused to minimize the invasiveness of existing minimally invasive surgery (MIS) approaches: one example is the development of continuum-like and soft robots that can bend, extend, contract at any point along their length. This provides them with capabilities well beyond those of their rigid-link counterparts, thus allowing to perform whole arm manipulation. One recent approach to soft and modular systems is represented by the on-going EU project STIFF-FLOP (www.stiff-flop.eu). The STIFF-FLOP arm is not fabricated by rigid structures, but soft ones showing advanced manipulation capabilities for surgical applications, with multiple degrees of freedom (DOFs), and ability of multi-bending. Ideally, the entire robotic structure should safely move with contact and bend detection and the embedded sensors should not interfere with the movements: the use of small sensors, both soft and stretchable, which remain functional when deformed, becomes necessary. For the aforementioned reasons, we introduce a small, low-cost, soft and stretchable sensor composed of a silicone rubber (EcoFlex0030, SmoothOn), integrating a conductive liquid channel filled with biocompatible Sodium Chloride (NaCl) solution. By stretching the sensor the cross-section of the channel deforms, thus leading to a change in electrical resistance. The functionality of the sensor has been proved through testing: changes in electrical resistance are measured as a function of the applied strain. The advantage of using silicone rubber is its mechanical durability and high flexibility, non-toxicity, chemical stability and low cost. Furthermore, liquid conductors eliminate the need for rigid electronics and preserve the natural elasticity of the sensor, and the NaCl solution fulfills the need for a biocompatible liquid. Differently from existing solutions that are not truly stretchable and biocompatible, the contribution of this work is an effort for improving the current soft sensors technologies through the demonstration that NaCl filled channel rubbers represent a valid solution for measuring deformations in flexible surgical instrumentation

Dynamic Environmental Monitoring using Intelligent Tendril Robots

Traditional robots are constructed from rigid links which facilitate both stiffness and accuracy. However, these systems operate best in open, highly structured spaces, and environments traversable by this technology are inherently restricted to scales and geometries which match the size and shape of the links. Conversely, continuous backbone continuum robots have enormous potential for adaptive exploration of unstructured environments. However, to date there has been very little research on algorithms for learning and adapting to changes in environmental conditions with continuum robots. In this research, we introduce new results in learning policies for novel long, thin, continuously bending continuum tendril robots aimed toward applications such as remote inspection and sensor mobility for improved sample acquisition. The results could also have potential applica tions in defense and security, search and rescue in hazardous environmental conditions, and as an innovative option for sensor placement in environmental monitoring. Using a prototype continuum tendril robot previously developed at Clemson University, we demonstrate the new learning policy for the tendrils adaptive sensor placement and remote inspection within an environment seeded with numerous disparate and slowly (over a matter of hours) time-varying sources, and discuss the potential for use of such robot tendrils in environmental monitoring applications. The learning algorithm implemented in real-time is shown to help the tendril to adapt its sensor placement to changing environmental sources

Embedded Sensing and Control for High-Speed Electrohydraulic Soft Robots

Soft robotics is a field of robotic system design characterized by materials and structures that exhibit large-scale deformation, high compliance, and rich multifunctionality. The incorporation of soft and deformable structures endows soft robotic systems with the compliance and resiliency that makes them well-adapted for unstructured and dynamic environments. While actuation mechanisms for soft robots vary widely, soft electrostatic transducers such as dielectric elastomer actuators (DEAs) and hydraulically amplified self-healing electrostatic (HASEL) actuators have demonstrated promise due to their muscle-like performance. Despite significant leaps in design and modeling of HASEL actuators thus far, the actuators in and of themselves are limited in terms of estimating their own states and individually lacks useful system-level functionalities. To address the mentioned shortcomings, this body of research has enabled embedded perception and intelligence to electromechanical systems driven by electro-hydraulic actuators, by (i) developing capacitive self-sensing and magnetic sensing techniques to estimate shape changes of electro-hydraulic actuators in real-time; (ii) designing and implementing hardware and software controllers for an array of electrohydraulic actuators to enable complex motions and their collective useful functionalities; and (iii) demonstrating the integration of embedded sensing and control to bring soft robots driven by these actuators closer to practical, real-world applications. While not exhaustive, the introduced system technologies provide the fundamental building blocks for more advanced functionalities and features that can potentially integrate HASEL actuators to augment and enrich our daily lives

Reliability of Extreme Learning Machines

Neumann K. Reliability of Extreme Learning Machines. Bielefeld: Bielefeld University Library; 2014.The reliable application of machine learning methods becomes increasingly important in challenging engineering domains. In particular, the application of extreme learning machines (ELM) seems promising because of their apparent simplicity and the capability of very efficient processing of large and high-dimensional data sets. However, the ELM paradigm is based on the concept of single hidden-layer neural networks with randomly initialized and fixed input weights and is thus inherently unreliable. This black-box character usually repels engineers from application in potentially safety critical tasks. The problem becomes even more severe since, in principle, only sparse and noisy data sets can be provided in such domains. The goal of this thesis is therefore to equip the ELM approach with the abilities to perform in a reliable manner. This goal is approached in three aspects by enhancing the robustness of ELMs to initializations, make ELMs able to handle slow changes in the environment (i.e. input drifts), and allow the incorporation of continuous constraints derived from prior knowledge. It is shown in several diverse scenarios that the novel ELM approach proposed in this thesis ensures a safe and reliable application while simultaneously sustaining the full modeling power of data-driven methods