Doctor of Philosophy

dissertationThis dissertation explores the design and use of an electromagnetic manipulation system that has been optimized for the dipole-eld model. This system can be used for noncontact manipulation of adjacent magnetic tools and combines the eld strength control of current electromagnetic systems with the analytical modeling of permanent-magnet systems. To design such a system, it is rst necessary to characterize how the shape of the eld source aects the shape of the magnetic eld. The magnetic eld generated by permanent magnets and electromagnets can be modeled, far from the source, using a multipole expansion. The error associated with the multipole expansion is quantied, and it is shown that, as long as the point of interest is 1.5 radii of the smallest sphere that can fully contain the magnetic source, the full expansion will have less than 1% error. If only the dipole term, the rst term in the expansion, is used, then the error is minimized for cylindrical shapes with a diameter-to-length ratio of 4=3 and for rectangular-bars with a cube. Applying the multipole expansion to electromagnets, an omnidirectional electromagnet, comprising three orthogonal solenoids and a spherical core, is designed that has minimal dipole-eld error and equal strength in all directions. Although this magnet can be constructed with any size core, the optimal design contains a spherical core with a diameter that is 60% of the outer dimension of the magnet. The resulting magnet's ability to dextrously control the eld at a point is demonstrated by rotating an endoscopic-pill mockup to drive it though a lumen and roll a permanent-magnet ball though several trajectories. Dipole elds also apply forces on adjacent magnetized objects. The ability to control these forces is demonstrated by performing position control on an orientation-constrained magnetic oat and nally by steering a permanent magnet, which is aligned with the applied dipole eld, around a rose curve

Soft micromachines with programmable motility and morphology

Nature provides a wide range of inspiration for building mobile micromachines that can navigate through confined heterogenous environments and perform minimally invasive environmental and biomedical operations. For example, microstructures fabricated in the form of bacterial or eukaryotic flagella can act as artificial microswimmers. Due to limitations in their design and material properties, these simple micromachines lack multifunctionality, effective addressability and manoeuvrability in complex environments. Here we develop an origami-inspired rapid prototyping process for building self-folding, magnetically powered micromachines with complex body plans, reconfigurable shape and controllable motility. Selective reprogramming of the mechanical design and magnetic anisotropy of body parts dynamically modulates the swimming characteristics of the micromachines. We find that tail and body morphologies together determine swimming efficiency and, unlike for rigid swimmers, the choice of magnetic field can subtly change the motility of soft microswimmers

Empirically Comparing Magnetic Needle Steering Models Using Expectation-Maximization

Straight-line needle insertion is a prevalent tool in surgical interventions in the brain, such as Deep Brain Stimulation and Convection-Enhanced Delivery, that treat a range of conditions from Alzheimer’s disease to brain cancer. Using a steerable needle to execute curved trajectories and correct positional deviation could enable more intervention possibilities, while reducing the risk of complication in these procedures. This paper experimentally identifies model parameters using an expectation-maximization (EM) algorithm for two different steerable needle models. The results compared a physically motivated model to the established bicycle needle model and found the former to be preferred for modeling soft brain tissue needle insertion. The results also supported the experimentally parameterized models’ use in future applications such as needle steering control

Empirically Comparing Magnetic Needle Steering Models Using Expectation-Maximization

Straight-line needle insertion is a prevalent tool in surgical interventions in the brain, such as Deep Brain Stimulation and Convection-Enhanced Delivery, that treat a range of conditions from Alzheimer’s disease to brain cancer. Using a steerable needle to execute curved trajectories and correct positional deviation could enable more intervention possibilities, while reducing the risk of complication in these procedures. This paper experimentally identifies model parameters using an expectation-maximization (EM) algorithm for two different steerable needle models. The results compared a physically motivated model to the established bicycle needle model and found the former to be preferred for modeling soft brain tissue needle insertion. The results also supported the experimentally parameterized models’ use in future applications such as needle steering control

Model-Based Calibration for Magnetic Manipulation

ISSN:0018-9464ISSN:1941-006

A Bi-State Shape Memory Material Composite Soft Actuator

Shape memory materials have been widely used as programmable soft matter for developing multifunctional hybrid actuators. Several challenges of fabrication and effective modelling of these soft actuating systems can be addressed by implementing novel 3D printing techniques and simulations to aid the designer. In this study, the temperature-dependent recovery of an embedded U-shaped Shape Memory Alloy (SMA) and the shape fixity of a 3D-printed Shape Memory Polymer (SMP) matrix were exploited to create a bi-state Shape Memory Composite (SMC) soft actuator. Electrical heating allowed the SMA to achieve the bi-state condition, undergoing phase transformation to a U shape in the rubbery phase and a flat shape in the glassy phase of the SMP. A COMSOL Multiphysics model was developed to predict the deformation and recovery of the SMC by leveraging the in-built SMA constitutive relations and user-defined material subroutine for the SMP. The bi-state actuation model was validated by capturing the mid-point displacement of the 80 mm length × 10 mm width × 2 mm-thick 3D-printed SMC. The viability of the SMC as a periodic actuator in terms of shape recovery was addressed through modelling and simulation. Results indicated that the proposed COMSOL model was in good agreement with the experiment. In addition, the effect of varying the volume ratio of the SMA wire in the SMC on the maximum and recovered deflection was also obtained. Our model can be used to design SMC actuators with various performance profiles to facilitate future designs in soft robotics and wearable technology applications

A Bi-State Shape Memory Material Composite Soft Actuator

Shape memory materials have been widely used as programmable soft matter for developing multifunctional hybrid actuators. Several challenges of fabrication and effective modelling of these soft actuating systems can be addressed by implementing novel 3D printing techniques and simulations to aid the designer. In this study, the temperature-dependent recovery of an embedded U-shaped Shape Memory Alloy (SMA) and the shape fixity of a 3D-printed Shape Memory Polymer (SMP) matrix were exploited to create a bi-state Shape Memory Composite (SMC) soft actuator. Electrical heating allowed the SMA to achieve the bi-state condition, undergoing phase transformation to a U shape in the rubbery phase and a flat shape in the glassy phase of the SMP. A COMSOL Multiphysics model was developed to predict the deformation and recovery of the SMC by leveraging the in-built SMA constitutive relations and user-defined material subroutine for the SMP. The bi-state actuation model was validated by capturing the mid-point displacement of the 80 mm length × 10 mm width × 2 mm-thick 3D-printed SMC. The viability of the SMC as a periodic actuator in terms of shape recovery was addressed through modelling and simulation. Results indicated that the proposed COMSOL model was in good agreement with the experiment. In addition, the effect of varying the volume ratio of the SMA wire in the SMC on the maximum and recovered deflection was also obtained. Our model can be used to design SMC actuators with various performance profiles to facilitate future designs in soft robotics and wearable technology applications

Unified Parameterization and Calibration of Serial, Parallel, and Hybrid Manipulators

In this work, we present methods allowing parallel, hybrid, and serial manipulators to be analyzed, calibrated, and controlled with the same analytical tools. We introduce a general approach to describe any robotic manipulator using established serial-link representations. We use this framework to generate analytical kinematic and calibration Jacobians for general manipulator constructions using null space constraints and extend the methods to hybrid manipulator types with complex geometry. We leverage the analytical Jacobians to develop detailed expressions for post-calibration pose uncertainties that are applied to describe the relationship between data set size and post-calibration uncertainty. We demonstrate the calibration of a hybrid manipulator assembled from high precision calibrated industrial components resulting in 91.1 μm RMS position error and 71.2 μrad RMS rotation error, representing a 46.7% reduction compared to the baseline calibration of assembly offsets

Observed Control of Magnetic Continuum Devices

This paper models an extensible catheter with an embedded magnet at its distal tip subject to an external magnetic field. We implement a control method coined observed control to perform model-based predictive control of the catheter using a Kalman smoother framework. Using this same smoother framework, we also solve for catheter shape and orientation given magnetic and insertion control using Cosserat rod theory and implement a disturbance observer for closed-loop control. We demonstrate observed control experimentally by traversing a 3D cube trajectory with the catheter tip. The catheter achieved positional accuracy of 3.3 mm average error in open-loop, while closed-loop control improved the accuracy to 0.33 mm

Unified Parameterization and Calibration of Serial, Parallel, and Hybrid Manipulators

In this work, we present methods allowing parallel, hybrid, and serial manipulators to be analyzed, calibrated, and controlled with the same analytical tools. We introduce a general approach to describe any robotic manipulator using established serial-link representations. We use this framework to generate analytical kinematic and calibration Jacobians for general manipulator constructions using null space constraints and extend the methods to hybrid manipulator types with complex geometry. We leverage the analytical Jacobians to develop detailed expressions for post-calibration pose uncertainties that are applied to describe the relationship between data set size and post-calibration uncertainty. We demonstrate the calibration of a hybrid manipulator assembled from high precision calibrated industrial components resulting in 91.1 μm RMS position error and 71.2 μrad RMS rotation error, representing a 46.7% reduction compared to the baseline calibration of assembly offsets