The Mechanics and Control of Undulatory Robotic Locomotion

In this dissertation, we examine a formulation of problems of undulatory robotic locomotion within the context of mechanical systems with nonholonomic constraints and symmetries. Using tools from geometric mechanics, we study the underlying structure found in general problems of locomotion. In doing so, we decompose locomotion into two basic components: internal shape changes and net changes in position and orientation. This decomposition has a natural mathematical interpretation in which the relationship between shape changes and locomotion can be described using a connection on a trivial principal fiber bundle. We begin by reviewing the processes of Lagrangian reduction and reconstruction for unconstrained mechanical systems with Lie group symmetries, and present new formulations of this process which are easily adapted to accommodate external constraints. Additionally, important physical quantities such as the mechanical connection and reduced mass-inertia matrix can be trivially determined using this formulation. The presence of symmetries then allows us to reduce the necessary calculations to simple matrix manipulations. The addition of constraints significantly complicates the reduction process; however, we show that for invariant constraints, a meaningful connection can be synthesized by defining a generalized momentum representing the momentum of the system in directions allowed by the constraints. We then prove that the generalized momentum and its governing equation possess certain invariances which allows for a reduction process similar to that found in the unconstrained case. The form of the reduced equations highlights the synthesized connection and the matrix quantities used to calculate these equations. The use of connections naturally leads to methods for testing controllability and aids in developing intuition regarding the generation of various locomotive gaits. We present accessibility and controllability tests based on taking derivatives of the connection, and relate these tests to taking Lie brackets of the input vector fields. The theory is illustrated using several examples, in particular the examples of the snakeboard and Hirose snake robot. We interpret each of these examples in light of the theory developed in this thesis, and examine the generation of locomotive gaits using sinusoidal inputs and their relationship to the controllability tests based on Lie brackets

Design and Study of Multifunctional Systems Based on Magnetic Nanoparticles for Biomedical Applications

Nanomedicine is a new field employing nanotechnology tools for biomedical applications. Nanomaterials feature sizes between 1 and 100 hundred nanometers, and therefore they fill the gap between single molecules and bulk materials. They open the possibility to access biological processes on the same size scale. Nanoparticles have shown great potential to achieve the goal of personalized medicine due to the possibilities to modify their surface with proteins, targeting molecules, or imaging probes. Particularly, iron oxide nanoparticles show promising properties for successful application in biomedicine due to their low-cost production, high biocompatibility, and great magnetic response. Strategies for the functionalization of inorganic cores have been developed over the past years, and efforts have been devoted to engineering nanoparticles with multifunctional ligands on the surface to enable active tumor targeting, detection over different imaging modalities, and stimulus-driven cargo release. During this Ph.D. thesis, three main aspects of iron oxide nanoparticles have been evaluated. First, the stability of nanoparticles has been addressed in biological fluids. Second, the uptake of nanoparticles by stem cells for cell tracking applications was studied with nanoparticles of different sizes and bearing different organic coatings. Third, new possibilities of chemical functionalization of naked iron oxide nanoparticles by orthogonal processes have been explored, in order to achieve high yields, simplicity and high reproducibility

Laser-induced forward transfer (LIFT) of water soluble polyvinyl alcohol (PVA) polymers for use as support material for 3D-printed structures

The additive microfabrication method of laser-induced forward transfer (LIFT) permits the creation of functional microstructures with feature sizes down to below a micrometre [1]. Compared to other additive manufacturing techniques, LIFT can be used to deposit a broad range of materials in a contactless fashion. LIFT features the possibility of building out of plane features, but is currently limited to 2D or 2½D structures [2–4]. That is because printing of 3D structures requires sophisticated printing strategies, such as mechanical support structures and post-processing, as the material to be printed is in the liquid phase. Therefore, we propose the use of water-soluble materials as a support (and sacrificial) material, which can be easily removed after printing, by submerging the printed structure in water, without exposing the sample to more aggressive solvents or sintering treatments. Here, we present studies on LIFT printing of polyvinyl alcohol (PVA) polymer thin films via a picosecond pulsed laser source. Glass carriers are coated with a solution of PVA (donor) and brought into proximity to a receiver substrate (glass, silicon) once dried. Focussing of a laser pulse with a beam radius of 2 µm at the interface of carrier and donor leads to the ejection of a small volume of PVA that is being deposited on a receiver substrate. The effect of laser pulse fluence , donor film thickness and receiver material on the morphology (shape and size) of the deposits are studied. Adhesion of the deposits on the receiver is verified via deposition on various receiver materials and via a tape test. The solubility of PVA after laser irradiation is confirmed via dissolution in de-ionised water. In our study, the feasibility of the concept of printing PVA with the help of LIFT is demonstrated. The transfer process maintains the ability of water solubility of the deposits allowing the use as support material in LIFT printing of complex 3D structures. Future studies will investigate the compatibility (i.e. adhesion) of PVA with relevant donor materials, such as metals and functional polymers. References: [1] A. Piqué and P. Serra (2018) Laser Printing of Functional Materials. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA. [2] R. C. Y. Auyeung, H. Kim, A. J. Birnbaum, M. Zalalutdinov, S. A. Mathews, and A. Piqué (2009) Laser decal transfer of freestanding microcantilevers and microbridges, Appl. Phys. A, vol. 97, no. 3, pp. 513–519. [3] C. W. Visser, R. Pohl, C. Sun, G.-W. Römer, B. Huis in ‘t Veld, and D. Lohse (2015) Toward 3D Printing of Pure Metals by Laser-Induced Forward Transfer, Adv. Mater., vol. 27, no. 27, pp. 4087–4092. [4] J. Luo et al. (2017) Printing Functional 3D Microdevices by Laser-Induced Forward Transfer, Small, vol. 13, no. 9, p. 1602553

Putting reaction-diffusion systems into port-Hamiltonian framework

Reaction-diffusion systems model the evolution of the constituents distributed in space under the influence of chemical reactions and diffusion [6], [10]. These systems arise naturally in chemistry [5], but can also be used to model dynamical processes beyond the realm of chemistry such as biology, ecology, geology, and physics. In this paper, by adopting the viewpoint of port-controlled Hamiltonian systems [7] we cast reaction-diffusion systems into the portHamiltonian framework. Aside from offering conceptually a clear geometric interpretation formalized by a Stokes-Dirac structure [8], a port-Hamiltonian perspective allows to treat these dissipative systems as interconnected and thus makes their analysis, both quantitative and qualitative, more accessible from a modern dynamical systems and control theory point of view. This modeling approach permits us to draw immediately some conclusions regarding passivity and stability of reaction-diffusion systems. It is well-known that adding diffusion to the reaction system can generate behaviors absent in the ode case. This primarily pertains to the problem of diffusion-driven instability which constitutes the basis of Turing’s mechanism for pattern formation [11], [5]. Here the treatment of reaction-diffusion systems as dissipative distributed portHamiltonian systems could prove to be instrumental in supply of the results on absorbing sets, the existence of the maximal attractor and stability analysis. Furthermore, by adopting a discrete differential geometrybased approach [9] and discretizing the reaction-diffusion system in port-Hamiltonian form, apart from preserving a geometric structure, a compartmental model analogous to the standard one [1], [2] is obtaine

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way