Bioinspired Ciliary Force Sensor for Robotic Platforms

© 2017 IEEE. The detection of small forces is of great interest in any robotic application that involves interaction with the environment (e.g., objects manipulation, physical human-robot interaction, minimally invasive surgery), since it allows the robot to detect the contacts early on and to act accordingly. In this letter, we present a sensor design inspired by the ciliary structure frequently found in nature, consisting of an array of permanently magnetized cylinders (cilia) patterned over a giant magnetoresistance sensor (GMR). When these cylinders are deformed in shape due to applied forces, the stray magnetic field variation will change the GMR sensor resistivity, thus enabling the electrical measurement of the applied force. In this letter, we present two 3 mm × 3 mm prototypes composed of an array of five cilia with 1 mm of height and 120 and 200 μm of diameter for each prototype. A minimum force of 333 μ N was measured. A simulation model for determining the magnetized cylinders average stray magnetic field is also presented

A parallel modular biomimetic cilia sorting platform

The aquatic unicellular organism Paramecium caudatum uses cilia to swim around its environment and to graze on food particles and bacteria. Paramecia use waves of ciliary beating for locomotion, intake of food particles and sensing. There is some evidence that Paramecia pre-sort food particles by discarding larger particles, but intake the particles matching their mouth cavity.Most prior attempts to mimic cilia-based manipulation merely mimicked the overall action rather than the beating of cilia. The majority of massive-parallel actuators are controlled by a central computer; however, a distributed control would be far more true-to-life. We propose and test a distributed parallel cilia platform where each actuating unit is autonomous, yet exchanging information with its closest neighboring units. The units are arranged in a hexagonal array. Each unit is a tileable circuit board, with a microprocessor, color-based object sensor and servo-actuated biomimetic cilia actuator.Localized synchronous communication between cilia allowed for the emergence of coordinated action, moving different colored objects together. The coordinated beating action was capable of moving objects up to 4 cm/s at its highest beating frequency; however, objects were moved at a speed proportional to the beat frequency. Using the local communication, we were able to detect the shape of objects and rotating an object using edge detection was performed; however, lateral manipulation using shape information was unsuccessful

Design, Actuation, and Functionalization of Untethered Soft Magnetic Robots with Life-Like Motions: A Review

Soft robots have demonstrated superior flexibility and functionality than conventional rigid robots. These versatile devices can respond to a wide range of external stimuli (including light, magnetic field, heat, electric field, etc.), and can perform sophisticated tasks. Notably, soft magnetic robots exhibit unparalleled advantages among numerous soft robots (such as untethered control, rapid response, and high safety), and have made remarkable progress in small-scale manipulation tasks and biomedical applications. Despite the promising potential, soft magnetic robots are still in their infancy and require significant advancements in terms of fabrication, design principles, and functional development to be viable for real-world applications. Recent progress shows that bionics can serve as an effective tool for developing soft robots. In light of this, the review is presented with two main goals: (i) exploring how innovative bioinspired strategies can revolutionize the design and actuation of soft magnetic robots to realize various life-like motions; (ii) examining how these bionic systems could benefit practical applications in small-scale solid/liquid manipulation and therapeutic/diagnostic-related biomedical fields

Automatic Fracture Characterization Using Tactile and Proximity Optical Sensing

This paper demonstrates how tactile and proximity sensing can be used to perform automatic mechanical fractures detection (surface cracks). For this purpose, a custom-designed integrated tactile and proximity sensor has been implemented. With the help of fiber optics, the sensor measures the deformation of its body, when interacting with the physical environment, and the distance to the environment's objects. This sensor slides across different surfaces and records data which are then analyzed to detect and classify fractures and other mechanical features. The proposed method implements machine learning techniques (handcrafted features, and state of the art classification algorithms). An average crack detection accuracy of ~94% and width classification accuracy of ~80% is achieved. Kruskal-Wallis results (p < 0.001) indicate statistically significant differences among results obtained when analysing only integrated deformation measurements, only proximity measurements and both deformation and proximity data. A real-time classification method has been implemented for online classification of explored surfaces. In contrast to previous techniques, which mainly rely on visual modality, the proposed approach based on optical fibers might be more suitable for operation in extreme environments (such as nuclear facilities) where radiation may damage electronic components of commonly employed sensing devices, such as standard force sensors based on strain gauges and video cameras

Soft Robotics: An Exploration of Inspired Technologies by Biological Organisms for Medical Applications.

La robótica blanda y en especial la robótica blanda bioinspirada es un campo que tiene una gran capacidad para aplicaciones médicas, debido a que se centra en el desarrollo de dispositivos más amigables con el ser humano y que se adapten más a entornos biológicos. Para la búsqueda de la información que se trata en el presente artículo se realizó la búsqueda de las palabras clave en bases de datos que contenían material científico y de resultados de investigación, y se extrajo alguna de la información más relevante de acuerdo con diferentes criterios de inclusión. El principal resultado de este artículo es la exploración de tecnologías robóticas bioinspiradas y usadas en robótica blanda las cuales pudieran usarse en el desarrollo de dispositivos, procesos o procedimientos médicos. Con la información encontrada se logró dar un vistazo general de las principales tecnologías robóticas bioinspiradas, las que se usan en el desarrollo de robots blandos y como pudieran llegar a integrarse y complementarse para la solución de problemas en el campo de la medicina y la bioingeniería.Soft robotics and especially bio inspired soft robotics is a field that has a great capacity for medical applications, because it focuses on the development of devices that are more human-friendly and that adapt to more biological environments. For the search of the information that is dealt with in this article, the search of the key words was carried out in databases that contained scientific material and research results, and some of the most relevant information was extracted according to different criteria. The main result of this article is the exploration of bio inspired robotic technologies used in soft robotics which could be used in the development of medical devices, processes or procedures. With the information found, it was possible to give an overview of the main bio inspired robotic technologies, those used in the development of soft robots and how they could be integrated and complemented for the solution of problems in the field of medicine and bioengineering

Analysis, Design and Fabrication of Micromixers, Volume II

Micromixers are an important component in micrototal analysis systems and lab-on-a-chip platforms which are widely used for sample preparation and analysis, drug delivery, and biological and chemical synthesis. The Special Issue "Analysis, Design and Fabrication of Micromixers II" published in Micromachines covers new mechanisms, numerical and/or experimental mixing analysis, design, and fabrication of various micromixers. This reprint includes an editorial, two review papers, and eleven research papers reporting on five active and six passive micromixers. Three of the active micromixers have electrokinetic driving force, but the other two are activated by mechanical mechanism and acoustic streaming. Three studies employs non-Newtonian working fluids, one of which deals with nano-non-Newtonian fluids. Most of the cases investigated micromixer design

Path planning, flow estimation, and dynamic control for underwater vehicles

Underwater vehicles such as robotic fish and long-endurance ocean-sampling platforms operate in challenging fluid environments. This dissertation incorporates models of the fluid environment in the vehicles' guidance, navigation, and control strategies while addressing uncertainties associated with estimates of the environment's state. Coherent flow structures may be on the same spatial scale as the vehicle or substantially larger than the vehicle. This dissertation argues that estimation and control tasks across widely varying spatial scales, from vehicle-scale to long-range, may be addressed using common tools of empirical observability analysis, nonlinear/non-Gaussian estimation, and output-feedback control. As an application in vehicle-scale flow estimation and control, this dissertation details the design, fabrication, and testing of a robotic fish with an artificial lateral-line inspired by the lateral-line flow-sensing organ present in fish. The robotic fish is capable of estimating the flow speed and relative angle of the oncoming flow. Using symmetric and asymmetric sensor configurations, the robot achieves the primitive fish behavior called rheotaxis, which describes a fish's tendency to orient upstream. For long-range flow estimation and control, path planning may be accomplished using observability-based path planning, which evaluates a finite set of candidate control inputs using a measure related to flow-field observability and selects an optimizer over the set. To incorporate prior information, this dissertation derives an augmented observability Gramian using an optimal estimation strategy known as Incremental 4D-Var. Examination of the minimum eigenvalue of an empirical version of this Gramian yields a novel measure for path planning, called the empirical augmented unobservability index. Numerical experiments show that this measure correctly selects the most informative paths given the prior information. As an application in long-range flow estimation and control, this dissertation considers estimation of an idealized pair of ocean eddies by an adaptive Lagrangian sensor (i.e., a platform that uses its position data as measurements of the fluid transport, after accounting for its own control action). The adaptive sampling is accomplished using the empirical augmented unobservability index, which is extended to non-Gaussian posterior densities using an approximate expected-cost calculation. Output feedback recursively improves estimates of the vehicle position and flow-field states