A Scalable Strategy for Open Loop Magnetic Control of Microrobots Using Critical Points

A novel scalable strategy for open loop control of ferromagnetic microrobots on a plane using a scalable array of electromagnets is presented. Instead of controlling the microrobot directly, we create equilibrium points in the magnetic force field that are stable and attractive on the plane in which the microrobot is to be controlled. The microrobot moves into these equilibrium points rapidly in presence of low viscous forces, and thus controlling the equilibrium points let us control the microrobot precisely. An unit/cell in the array of electromagnets allows precise control of the microrobot in the unit/cell’s domain. Motion synthesis across multiple overlapping domains allows control of the microrobot in large regions across the array. We perform numerical analysis and demonstrate the control of the ferromagnetic microrobot using the proposed method through simulations

Microfluidics and Bio-MEMS for Next Generation Healthcare.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Bacterial Biohybrid Microswimmers

Over millions of years, Nature has optimized the motion of biological systems at the micro and nanoscales. Motor proteins to motile single cells have managed to overcome Brownian motion and solve several challenges that arise at low Reynolds numbers. In this review, we will briefly describe naturally motile systems and their strategies to move, starting with a general introduction that surveys a broad range of developments, followed by an overview about the physical laws and parameters that govern and limit motion at the microscale. We characterize some of the classes of biological microswimmers that have arisen in the course of evolution, as well as the hybrid structures that have been constructed based on these, ranging from Montemagno's ATPase motor to the SpermBot. Thereafter, we maintain our focus on bacteria and their biohybrids. We introduce the inherent properties of bacteria as a natural microswimmer and explain the different principles bacteria use for their motion. We then elucidate different strategies that have been employed for the coupling of a variety of artificial microobjects to the bacterial surface, and evaluate the different effects the coupled objects have on the motion of the 'biohybrid.' Concluding, we give a short overview and a realistic evaluation of proposed applications in the field

Design og styring av smarte robotsystemer for applikasjoner innen biovitenskap: biologisk prøvetaking og jordbærhøsting

This thesis aims to contribute knowledge to support fully automation in life-science applications, which includes design, development, control and integration of robotic systems for sample preparation and strawberry harvesting, and is divided into two parts. Part I shows the development of robotic systems for the preparation of fungal samples for Fourier transform infrared (FTIR) spectroscopy. The first step in this part developed a fully automated robot for homogenization of fungal samples using ultrasonication. The platform was constructed with a modified inexpensive 3D printer, equipped with a camera to distinguish sample wells and blank wells. Machine vision was also used to quantify the fungi homogenization process using model fitting, suggesting that homogeneity level to ultrasonication time can be well fitted with exponential decay equations. Moreover, a feedback control strategy was proposed that used the standard deviation of local homogeneity values to determine the ultrasonication termination time. The second step extended the first step to develop a fully automated robot for the whole process preparation of fungal samples for FTIR spectroscopy by adding a newly designed centrifuge and liquid-handling module for sample washing, concentration and spotting. The new system used machine vision with deep learning to identify the labware settings, which frees the users from inputting the labware information manually. Part II of the thesis deals with robotic strawberry harvesting. This part can be further divided into three stages. i) The first stage designed a novel cable-driven gripper with sensing capabilities, which has high tolerance to positional errors and can reduce picking time with a storage container. The gripper uses fingers to form a closed space that can open to capture a fruit and close to push the stem to the cutting area. Equipped with internal sensors, the gripper is able to control a robotic arm to correct for positional errors introduced by the vision system, improving the robustness. The gripper and a detection method based on color thresholding were integrated into a complete system for strawberry harvesting. ii) The second stage introduced the improvements and updates to the first stage where the main focus was to address the challenges in unstructured environment by introducing a light-adaptive color thresholding method for vision and a novel obstacle-separation algorithm for manipulation. At this stage, the new fully integrated strawberry-harvesting system with dual-manipulator was capable of picking strawberries continuously in polytunnels. The main scientific contribution of this stage is the novel obstacle-separation path-planning algorithm, which is fundamentally different from traditional path planning where obstacles are typically avoided. The algorithm uses the gripper to push aside surrounding obstacles from an entrance, thus clearing the way for it to swallow the target strawberry. Improvements were also made to the gripper, the arm, and the control. iii) The third stage improved the obstacle-separation method by introducing a zig-zag push for both horizontal and upward directions and a novel dragging operation to separate upper obstacles from the target. The zig-zag push can help the gripper capture a target since the generated shaking motion can break the static contact force between the target and obstacles. The dragging operation is able to address the issue of mis-capturing obstacles located above the target, in which the gripper drags the target to a place with fewer obstacles and then pushes back to move the obstacles aside for further detachment. The separation paths are determined by the number and distribution of obstacles based on the downsampled point cloud in the region of interest.Denne avhandlingen tar sikte på å bidra med kunnskap om automatisering og robotisering av applikasjoner innen livsvitenskap. Avhandlingen er todelt, og tar for seg design, utvikling, styring og integrering av robotsystemer for prøvetaking og jordbærhøsting. Del I omhandler utvikling av robotsystemer til bruk under forberedelse av sopprøver for Fourier-transform infrarød (FTIR) spektroskopi. I første stadium av denne delen ble det utviklet en helautomatisert robot for homogenisering av sopprøver ved bruk av ultralyd-sonikering. Plattformen ble konstruert ved å modifisere en billig 3D-printer og utstyre den med et kamera for å kunne skille prøvebrønner fra kontrollbrønner. Maskinsyn ble også tatt i bruk for å estimere soppens homogeniseringsprosess ved hjelp av matematisk modellering, noe som viste at homogenitetsnivået faller eksponensielt med tiden. Videre ble det foreslått en strategi for regulering i lukker sløyfe som brukte standardavviket for lokale homogenitetsverdier til å bestemme avslutningstidspunkt for sonikeringen. I neste stadium ble den første plattformen videreutviklet til en helautomatisert robot for hele prosessen som forbereder prøver av sopprøver for FTIR-spektroskopi. Dette ble gjort ved å legge til en nyutviklet sentrifuge- og væskehåndteringsmodul for vasking, konsentrering og spotting av prøver. Det nye systemet brukte maskinsyn med dyp læring for å identifisere innstillingene for laboratorieutstyr, noe som gjør at brukerne slipper å registrere innstillingene manuelt.Norwegian University of Life SciencespublishedVersio

Locomotion Optimization of Photoresponsive Small-scale Robot: A Deep Reinforcement Learning Approach

Soft robots comprise of elastic and flexible structures, and actuatable soft materials are often used to provide stimuli-responses, remotely controlled with different kinds of external stimuli, which is beneficial for designing small-scale devices. Among different stimuli-responsive materials, liquid crystal networks (LCNs) have gained a significant amount of attention for soft small-scale robots in the past decade being stimulated and actuated by light, which is clean energy, able to transduce energy remotely, easily available and accessible to sophisticated control. One of the persistent challenges in photoresponsive robotics is to produce controllable autonomous locomotion behavior. In this Thesis, different types of photoresponsive soft robots were used to realize light-powered locomotion, and an artificial intelligence-based approach was developed for controlling the movement. A robot tracking system, including an automatic laser steering function, was built for efficient robotic feature detection and steering the laser beam automatically to desired locations. Another robot prototype, a swimmer robot, driven by the automatically steered laser beam, showed directional movements including some degree of uncertainty and randomness in their locomotion behavior. A novel approach is developed to deal with the challenges related to the locomotion of photoresponsive swimmer robots. Machine learning, particularly deep reinforcement learning method, was applied to develop a control policy for autonomous locomotion behavior. This method can learn from its experiences by interacting with the robot and its environment without explicit knowledge of the robot structure, constituent material, and robotic mechanics. Due to the requirement of a large number of experiences to correlate the goodness of behavior control, a simulator was developed, which mimicked the uncertain and random movement behavior of the swimmer robots. This approach effectively adapted the random movement behaviors and developed an optimal control policy to reach different destination points autonomously within a simulated environment. This work has successfully taken a step towards the autonomous locomotion control of soft photoresponsive robots

Concept, modeling and experimental characterization of the modulated friction inertial drive (MFID) locomotion principle:application to mobile microrobots

A mobile microrobot is defined as a robot with a size ranging from 1 in3 down to 100 µm3 and a motion range of at least several times the robot's length. Mobile microrobots have a great potential for a wide range of mid-term and long-term applications such as minimally invasive surgery, inspection, surveillance, monitoring and interaction with the microscale world. A systematic study of the state of the art of locomotion for mobile microrobots shows that there is a need for efficient locomotion solutions for mobile microrobots featuring several degrees of freedom (DOF). This thesis proposes and studies a new locomotion concept based on stepping motion considering a decoupling of the two essential functions of a locomotion principle: slip generation and slip variation. The proposed "Modulated Friction Inertial Drive" (MFID) principle is defined as a stepping locomotion principle in which slip is generated by the inertial effect of a symmetric, axial vibration, while the slip variation is obtained from an active modulation of the friction force. The decoupling of slip generation and slip variation also has lead to the introduction of the concept of a combination of on-board and off-board actuation. This concept allows for an optimal trade-off between robot simplicity and power consumption on the one hand and on-board motion control on the other hand. The stepping motion of a MFID actuator is studied in detail by means of simulation of a numeric model and experimental characterization of a linear MFID actuator. The experimental setup is driven by piezoelectric actuators that vibrate in axial direction in order to generate slip and in perpendicular direction in order to vary the contact force. After identification of the friction parameters a good match between simulation and experimental results is achieved. MFID motion velocity has shown to depend sinusoidally on the phase shift between axial and perpendicular vibration. Motion velocity also increases linearly with increasing vibration amplitudes and driving frequency. Two parameters characterizing the MFID stepping behavior have been introduced. The step efficiency ηstep expresses the efficiency with which the actuator is capable of transforming the axial vibration in net motion. The force ratio qF evaluates the ease with which slip is generated by comparing the maximum inertial force in axial direction to the minimum friction force. The suitability of the MFID principle for mobile microrobot locomotion has been demonstrated by the development and characterization of three locomotion modules with between 2 and 3 DOF. The microrobot prototypes are driven by piezoelectric and electrostatic comb drive actuators and feature a characteristic body length between 20 mm and 10 mm. Characterization results include fast locomotion velocities up to 3 mm/s for typical driving voltages of some tens of volts and driving frequencies ranging from some tens of Hz up to some kHz. Moreover, motion resolutions in the nanometer range and very low power consumption of some tens of µW have been demonstrated. The advantage of the concept of a combination of on-board and off-board actuation has been demonstrated by the on-board simplicity of two of the three prototypes. The prototypes have also demonstrated the major advantage of the MFID principle: resonance operation has shown to reduce the power consumption, reduce the driving voltage and allow for simple driving electronics. Finally, with the fabrication of 2 × 2 mm2 locomotion modules with 2 DOF, a first step towards the development of mm-sized mobile microrobots with on-board motion control is made

Minimally invasive therapies for the brain using magnetic particles

Delivering a therapy with precision, while reducing off target effects is key to the success of any novel therapeutic intervention. This is of most relevance in the brain, where the preservation of surrounding healthy tissue is crucial in reducing the risk of cognitive impairment and improving patient prognosis. Our scientific understanding of the brain would also benefit from minimally invasive investigations of specific cell types so that they may be observed in their most natural physiological environment. Magnetic particles based techniques have the potential to deliver cellular precision in a minimally invasive manner. When inside the body, Magnetic particles can be actuated remotely using externally applied magnetic fields while their position can be detected non-invasively using MRI. The magnetic forces applied to the particles however, rapidly decline with increasing distance from the magnetic source. It is therefore critical to understand the amount of force needed for a particular application. The properties of the magnetic particle such as the size, shape and magnetic content, as well as the properties of the applied magnetic field, can then be tailored to that application. The aim of this thesis was to develop magnetic particle based techniques for precise manipulation of cells in the brain. Two different approaches were explored, utilising the versatile nature of magnetic actuation for two different applications. The first approach uses magnetic nanoparticles to mechanically stimulate a specific cell type. Magnetic particles conjugated with the antibody ACSA-1 would selectively bind to astrocytes to evoke the controlled release of ATP and induce a calcium flux which are used for communication with neighbouring cells. This approach allows for the investigation into the role of astrocytes in localised brain regions using a naturally occurring actuation process (mechanical force) without effecting their natural environment. The second approach uses a millimetre sized magnetic particle which can be navigated through the brain and ablate localised regions of cells using a magnetic resonance imaging system. The magnetic particle causes a distinct contrast in MRI images, allowing for precise detection of its location so that it may be iteratively guided along a pre-determined path to avoid eloquent brain regions. Once at the desired location, an alternating magnetic field can be applied causing the magnetic particle to heat and deliver controllable, well defined regions of cell death. The forces needed for cell stimulation are orders of magnitude less than the forces needed to guide particles through the brain. Chapters 4 and 5 use external magnets to deliver forces in the piconewton range. While stimulation was demonstrated in small animals, scaling up this technique to human proportions remains a challenge. Chapters 6 and 7 use a preclinical MRI system to generate forces in the millinewton range, allowing the particle to be moved several centimetres through the brain within a typical surgical timescale. When inside the scanner, an alternating magnetic field causes the particle to heat rapidly, enabling the potential for multiple ablations within a single surgery. For clinical translation of this technique, MRI scanners would require a dedicated propulsion gradient set and heating coil