Kinematics Computing for Soft Robots: Method based on Geometric Computing and Machine Learning

Soft robots that are built from materials with mechanical properties similar to those of living tissues can achieve tasks like never before in comparison to conventional rigid robots. Powered by the compliance of soft materials and novel structure designs, complex motion (e.g., bending, twisting, and extension) can be accomplished in robotic bodies. We now see soft robots being used to grasp fragile objects and detect confined areas. However, conventional modeling and control approaches, which rely on the rigidity of the robot body, are less effective when directly applied to soft robotic systems. Therefore, new methods and algorithms need to be developed that allow modeling and kinematics control for soft robots.In model-based robot control, kinematics comprise the fundamental knowledge that can be used to build the mathematical connection between control parameters and robot status. Unlike rigid robots, whose kinematics are well studied and have fast (analytical) solutions, effective and general kinematics computing methods for soft robot systems are still lacking. According to the modeling perspective (i.e., forward kinematics (FK)), predicting the whole-body shape of soft robots under actuation is a non-trivial task since the non-linear deformation in robot bodies and the hyperplastic properties of soft materials create challenges in balancing accuracy and computational costs in existing FK models. The lack of modeling tools further brings the difficulties in developing advanced algorithms to inverse kinematics (IK) and (statics) control thereafter. This Ph.D. project aims to develop a general soft robot kinematics computing pipeline, that can contribute to the effective control of soft robot systems to accomplish given tasks. A fast numerical simulator for soft robots is firstly presented in this thesis, in which the shape of the robot body is discretely represented by volumetric elements. The development of this simulator was inspired by the fact that the hard-to-model actuation input (e.g., cable force, pressure, and electronic field) in soft robot systems can be directly modeled or transformed to fit the shape change in actuation elements. An optimization pipeline was built to minimize elastic energy in the body elements and compute the deformed shape with actuation parameters as input. As a general numerical simulator, it supports the modeling of various types of actuation, and the hyperelastic soft material properties are integrated. A fast collision checking and response model was added to predict the behavior of soft robots under robot-robot collisions and robot-environment interactions. The numerical computing process of our simulator shows good convergence, even for soft robots with large (rotational) deformation in their bodies, and can therefore balance the computational cost and model precision. In comparison to commercial \textit{finite element analysis} (FEA) software, this geometry-based simulator demonstrates a 20-fold faster computing speed, and the simulation result can well fit the shape that was captured from the physical setup.The IK problem of soft robots is defined as computing proper actuation parameters that drive soft robots to accomplish given tasks. In this thesis, task-specific IK objectives (which are mainly geometrically defined) are formulated, and the optimal actuation parameters are detected using gradient-based iteration. Through the developed simulator, the gradients of objective functions are estimated using numerical differences. The sequence of motion can be successfully computed using this IK solver, and its efficiency has been verified in two case studies, which include path-following and object pick-and-place.For the final stage of this Ph.D. project, the speed and precision of the IK solver are enhanced through machine learning. Fully connected neural networks are invited to fit functions of FK and the Jacobian of IK-related objectives. With the high efficiency in the forward propagation of networks (in analytical form), the gradient-based IK solver can run in real-time. Sim-to-real transfer learning is applied to eliminate the reality gap and make the computed actuation parameters more precise in physical setups. Applying sim-to-real transfer learning can also benefit the efficiency of the data generation process. In our pipeline, massive training data is first generated in a virtual environment using a fast simulator; thereafter, a lightweight network layer is employed to map the result of the simulation to the physical hardware. As a result, the amount of physical data can be reduced by 60% to train a network that accurately computes IK solutions.In conclusion, this dissertation presents a pipeline that computes kinematics solutions for soft robots. A fast geometry-based simulator is presented to contribute to building an iteration-based numerical IK solver. Machine learning is applied to accelerate IK computing to real-time speed with enhanced precision. Task-specific kinematics control is realized in different soft robot systems to verify the effectiveness of the proposed method. The algorithms and code presented in this Ph.D. thesis are open-sourced for researchers and designers, and have the potential to become a general tool for designing and controlling soft robots. Future studies on the design optimization and high-level control of soft robots can all benefit from the research outcomes of this project.Materials and Manufacturin

Block Transmissions over Doubly Selective Channels: Iterative Channel Estimation and Turbo Equalization

Modern wireless communication systems require high transmission rates, giving rise to frequency selectivity due to multipath propagation. In addition, high-mobility terminals and scatterers induce Doppler shifts that introduce time selectivity. Therefore, advanced techniques are needed to accurately model the time- and frequency-selective (i.e., doubly selective) channels and to counteract the related performance degradation. In this paper, we develop new receivers for orthogonal frequency-division multiplexing (OFDM) systems and single-carrier (SC) systems in doubly selective channels by embedding the channel estimation task within low-complexity block turbo equalizers. Linear minimum mean-squared error (MMSE) pilot-assisted channel estimators are presented, and the soft data estimates from the turbo equalizers are used to improve the quality of the channel estimates.Microelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc

Additively manufactured scaffolds for bone tissue engineering and the prediction of their mechanical behavior: A review

Additive manufacturing (AM), nowadays commonly known as 3D printing, is a revolutionary materials processing technology, particularly suitable for the production of low-volume parts with high shape complexities and often with multiple functions. As such, it holds great promise for the fabrication of patient-specific implants. In recent years, remarkable progress has been made in implementing AM in the bio-fabrication field. This paper presents an overview on the state-of-the-art AM technology for bone tissue engineering (BTE) scaffolds, with a particular focus on the AM scaffolds made of metallic biomaterials. It starts with a brief description of architecture design strategies to meet the biological and mechanical property requirements of scaffolds. Then, it summarizes the working principles, advantages and limitations of each of AM methods suitable for creating porous structures and manufacturing scaffolds from powdered materials. It elaborates on the finite-element (FE) analysis applied to predict the mechanical behavior of AM scaffolds, as well as the effect of the architectural design of porous structure on its mechanical properties. The review ends up with the authors’ view on the current challenges and further research directions.Biomaterials & Tissue Biomechanic

Physical simulation method for the investigation of weld seam formation during the extrusion of aluminum alloys

Extrusion through the porthole die is a predominant forming process used in the production of hollow aluminum alloy profiles across the aluminum extrusion industry. Longitudinal weld seams formed during the process may negatively influence the quality of extruded profiles. It is therefore of great importance to understand the formation of weld seams inside the welding chamber during extrusion, as affected by extrusion process variables and die design. Previously developed physical simulation methods could not fully reproduce the thermomechanical conditions inside the welding chamber of porthole die. In this research, a novel physical simulation method for the investigation of weld seam formation during extrusion was developed. With a tailor-designed tooling set mounted on a universal testing machine, the effects of temperature, speed, and strain on the weld seam quality of the 6063 alloy were investigated. The strains inside the welding chamber were found to be of paramount importance for the bonding of metal streams, accompanied by microstructural changes, i.e., recovery or recrystallization, depending on the local deformation condition. The method was shown to be able to provide guidelines for the design of porthole dies and choice of extrusion process variables, thereby reducing the scrap rate of aluminum extrusion operation.Accepted Author ManuscriptBiomaterials & Tissue Biomechanic

Reinforced FDM: Multi-axis filament alignment with controlled anisotropic strength

The anisotropy of mechanical strength on a 3D printed model can be controlled in a multi-axis 3D printing system as materials can be accumulated along dynamically varied directions. In this paper, we present a new computational framework to generate specially designed layers and toolpaths of multi-axis 3D printing for strengthening a model by aligning filaments along the directions with large stresses. The major challenge comes from how to effectively decompose a solid into a sequence of strength-aware and collision-free working surfaces. We formulate it as a problem to compute an optimized governing field together with a selected orientation of fabrication setup. Iso-surfaces of the governing field are extracted as working surface layers for filament alignment. Supporting structures in curved layers are constructed by extrapolating the governing field to enable the fabrication of overhangs. Compared with planar-layer based Fused Deposition Modeling (FDM) technology, models fabricated by our method can withstand up to 6.35× loads in experimental tests. Materials and Manufacturin

Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

This article presents an efficient learning-based method to solve the <italic>inverse kinematic</italic> (IK) problem on soft robots with highly nonlinear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Materials and ManufacturingMechatronic Desig

Forming of magnesium alloy microtubes in the fabrication of biodegradable stents

Magnesium alloys have, in recent years, been recognized as highly promising biodegradable materials, especially for vascular stent applications. Forming of magnesium alloys into high-precision thin-wall tubes has however presented a technological barrier in the fabrication of vascular stents, because of the poor workability of magnesium at room temperature. In the present study, the forming processes, i.e., hot indirect extrusion and multi-pass cold drawing were used to fabricate seamless microtubes of a magnesium alloy. The magnesium alloy ZM21 was selected as a representative biomaterial for biodegradable stent applications. Microtubes with an outside diameter of 2.9 mm and a wall thickness of 0.2 mm were successfully produced at the fourth pass of cold drawing without inter-pass annealing. Dimensional evaluation showed that multi-pass cold drawing was effective in correcting dimensional non-uniformity arising from hot indirect extrusion. Examinations of the microstructures of microtubes revealed the generation of a large number of twins as a result of accumulated work hardening at the third and fourth passes of cold drawing, corresponding to the significantly raised forming forces. The work demonstrated the viability of the forming process route selected for the fabrication of biodegradable magnesium alloy microtubes.Biomechanical EngineeringMechanical, Maritime and Materials Engineerin

Support-free volume printing by multi-axis motion

This paper presents a new method to fabricate 3D models on a robotic printing system equipped with multi-axis motion. Materials are accumulated inside the volume along curved tool-paths so that the need of supporting structures can be tremendously reduced - if not completely abandoned - on all models. Our strategy to tackle the challenge of tool-path planning for multi-axis 3D printing is to perform two successive decompositions, first volume-to-surfaces and then surfaces-to-curves. The volume-to-surfaces decomposition is achieved by optimizing a scalar field within the volume that represents the fabrication sequence. The field is constrained such that its isovalues represent curved layers that are supported from below, and present a convex surface affording for collision-free navigation of the printer head. After extracting all curved layers, the surfaces-to-curves decomposition covers them with tool-paths while taking into account constraints from the robotic printing system. Our method successfully generates tool-paths for 3D printing models with large overhangs and high-genus topology. We fabricated several challenging cases on our robotic platform to verify and demonstrate its capabilities.Accepted author manuscriptMaterials and Manufacturin

Role of ammonia-oxidizing microorganisms in the removal of organic micropollutants during simulated riverbank filtration

Biodegradation plays an important role in the removal of organic micropollutants (OMPs) during riverbank filtration (RBF) for drinking water production. The ability of ammonia-oxidizing microorganisms (AOM) to remove OMPs has attracted increasing attention. However, the distribution of AOM in RBF and its role in the degradation of OMPs remains unknown. In this study, the behavior of 128 selected OMPs and the distribution of AOM and their roles in the degradation of OMPs in RBF were explored by column and batch experiments simulating the first meter of the riverbank. The results showed that the selected OMPs were effectively removed (82/128 OMPs, >70% removal) primarily by biodegradation and partly by adsorption. Inefficiently removed OMPs tended to have low molecular weights, low log P, and contain secondary amides, secondary sulfonamides, secondary ketimines, and benzyls. In terms of the microbial communities, the relative abundance of AOM increased from 0.1%–0.2% (inlet-sand) to 5.3%–5.9% (outlet-sand), which was dominated by ammonia-oxidizing archaea whose relative abundance increased from 23%–72% (inlet-sand) to 97% (outlet-sand). Comammox accounted for 23%–64% in the inlet-sand and 1% in the outlet-sand. The abundances of AOM amoA genes kept stable in the inlet-sand of control columns, while decreased by 78% in the treatment columns, suggesting the inhibition effect of allylthiourea (ATU) on AOM. It is observed that AOM played an important role in the degradation of OMPs, where its inhibition led to the corresponding inhibition of 32 OMPs (5/32 were completely suppressed). In particular, OMPs with low molecular weights and containing primary amides, secondary amides, benzyls, and secondary sulfonamides were more likely to be removed by AOM. This study reveals the vital role of AOM in the removal of OMPs, deepens our understanding of the degradation of OMPs in RBF, and offers valuable insights into the physiochemical properties of OMPs and their AOM co-metabolic potential.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Water Managemen

Delamination toughening in a low carbon microalloyed steel plate rolled in the dual-phase region

It is still a big challenge to obtain excellent low-temperature toughness for bulk steel materials. Delamination is an effective method to improve low-temperature toughness. In the present study, delamination toughening in a low carbon microalloyed steel plate with elongated and ultrafine-grained microstructure rolled in the dual-phase region has been investigated in detail. When toughness was measured along normal direction, the steel plate had a high upper shelf energy and no delamination occurred in the upper shelf region. A large delaminated crack parallel to rolling plane started to appear and changed the propagation path of main crack when testing temperature was lower than −60 °C. We find this kind of delamination induces a second upper shelf in the Charpy transition–temperature curve. The second upper shelf, reaching up to 300 J in the temperature range of −60 °C to −140 °C, results in excellent low-temperature toughness for the steel plate, and the ductile-brittle transition temperature is lowered to −157 °C. The developed steel plate also has high low-temperature toughness measured along transverse direction due to delamination. The effect factors on upper shelf energy, delamination mechanism and delamination toughening are discussed.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.RST/Fundamental Aspects of Materials and EnergyNovel Aerospace Material