Attentional Graph Neural Networks for Robust Massive Network Localization

Graph neural networks (GNNs) have gained significant popularity for classification tasks in machine learning, yet their applications to regression problems remain limited. Concurrently, attention mechanisms have emerged as powerful tools in sequential learning tasks. In this paper, we employ GNNs and attention mechanisms to address a classical but challenging nonlinear regression problem: network localization. We propose a novel GNN-based network localization method that achieves exceptional stability and accuracy in the presence of severe non-line-of-sight (NLOS) propagations, while eliminating the need for laborious offline calibration or NLOS identification. Extensive experimental results validate the effectiveness and high accuracy of our GNN-based localization model, particularly in challenging NLOS scenarios. However, the proposed GNN-based model exhibits limited flexibility, and its accuracy is highly sensitive to a specific hyperparameter that determines the graph structure. To address the limitations and extend the applicability of the GNN-based model to real scenarios, we introduce two attentional graph neural networks (AGNNs) that offer enhanced flexibility and the ability to automatically learn the optimal hyperparameter for each node. Experimental results confirm that the AGNN models are able to enhance localization accuracy, providing a promising solution for real-world applications. We also provide some analyses of the improved performance achieved by the AGNN models from the perspectives of dynamic attention and signal denoising characteristics.

Design of Bistable Soft Deployable Structures via a Kirigami-inspired Planar Fabrication Approach

Fully soft bistable mechanisms have shown extensive applications ranging from soft robotics, wearable devices, and medical tools, to energy harvesting. However, the lack of design and fabrication methods that are easy and potentially scalable limits their further adoption into mainstream applications. Here a top-down planar approach is presented by introducing Kirigami-inspired engineering combined with a pre-stretching process. Using this method, Kirigami-Pre-stretched Substrate-Kirigami trilayered precursors are created in a planar manner; upon release, the strain mismatch -- due to the pre-stretching of substrate -- between layers would induce an out-of-plane buckling to achieve targeted three dimensional (3D) bistable structures. By combining experimental characterization, analytical modeling, and finite element simulation, the effect of the pattern size of Kirigami layers and pre-stretching on the geometry and stability of resulting 3D composites is explored. In addition, methods to realize soft bistable structures with arbitrary shapes and soft composites with multistable configurations are investigated, which could encourage further applications. Our method is demonstrated by using bistable soft Kirigami composites to construct two soft machines: (i) a bistable soft gripper that can gently grasp delicate objects with different shapes and sizes and (ii) a flytrap-inspired robot that can autonomously detect and capture objects.

An origami crawling robot driven by a folded self-sustained oscillator

Locomotive robots that do not rely on electronics and/or electromagnetic components will open up new perspectives and applications for robotics. However, these robots usually involve complicated and tedious fabrication processes, limiting their applications. Here, we develop an easy-to-fabricate crawling robot by embedding simple control and actuation into origami-inspired mechanisms through folding, eliminating the need for discrete electronics and transducers. Our crawling robot locomotes through directional friction propelled by an onboard origami self-sustained oscillator, which generates periodic actuation from a single source of constant power. The crawling robot is lightweight (~ 3.8 gram), ultra low-cost (~ US $1), nonmagnetic, and electronic-free; it may enable practical applications in extreme environments, e.g., large radiation or magnetic fields. The robot can be fabricated through a monolithic origami-inspired folding-based method with universal materials, i.e., sheet materials and conductive threads. This rapid design and fabrication approach enables the programmable assembly of various mechanisms within this manufacturing paradigm, laying the foundation for autonomous, untethered robots without requiring electronics.

A cut-and-fold self-sustained compliant oscillator for autonomous actuation of origami-inspired robots

Origami-inspired robots are of particular interest given their potential for rapid and accessible design and fabrication of elegant designs and complex functionalities through cutting and folding of flexible 2D sheets or even strings, i.e.printable manufacturing. Yet, origami robots still require bulky, rigid components or electronics for actuation and control to accomplish tasks with reliability, programmability, ability to output substantial force, and durability, restricting their full potential. Here, we present a printable self-sustained compliant oscillator that generates periodic actuation using only constant electrical power, without discrete components or electronic control hardware. This oscillator is robust (9 out of 10 prototypes worked successfully on the first try), configurable (with tunable periods from 3 s to 12 s), powerful (can overcome hydrodynamic resistance to consistently propel a swimmer at ~1.6 body lengths/min), and long-lasting (~10^3 cycles); it enables driving macroscale devices with prescribed autonomous behaviors, e.g. locomotion and sequencing. This oscillator is also fully functional underwater and in high magnetic fields. Our analytical model characterizes essential parameters of the oscillation period, enabling programmable design of the oscillator. The printable oscillator can be integrated into origami-inspired systems seamlessly and monolithically, allowing rapid design and prototyping; the resulting integrated devices are lightweight, low-cost, compliant, electronic-free, and nonmagnetic, enabling practical applications in extreme areas. We demonstrate the functionalities of the oscillator with: (i) autonomous gliding of a printable swimmer, (ii) LED flashing, and (iii) fluid stirring. This work paves the way for realizing fully printable autonomous robots with a high integration of actuation and control.

Fabrication-aware Design for Furniture with Planar Pieces

We propose a computational design tool to enable casual end-users to easily design, fabricate, and assemble flat-pack furniture with guaranteed manufacturability. Using our system, users select parameterized components from a library and constrain their dimensions. Then they abstractly specify connections among components to define the furniture. Once fabrication specifications (e.g. materials) designated, the mechanical implementation of the furniture is automatically handled by leveraging encoded domain expertise. Afterwards, the system outputs 3D models for visualization and mechanical drawings for fabrication. We demonstrate the validity of our approach by designing, fabricating, and assembling a variety of flat-pack (scaled) furniture on demand.

Computational Design and Fabrication of Corrugated Mechanisms from Behavioral Specifications

Orthogonally assembled double-layered corrugated (OADLC) mechanisms are a class of foldable structures that harness origami-inspired methods to enhance the structural stiffness of resulting devices; these mechanisms have extensive applications due to their lightweight, compact nature as well as their high strength-to-weight ratio. However, the design of these mechanisms remains challenging. Here, we propose an efficient method to rapidly design OADLC mechanisms from desired behavioral specifications, i.e. in-plane stiffness and out-of-plane stiffness. Based on an equivalent plate model, we develop and validate analytical formulas for the behavioral specifications of OADLC mechanisms; the analytical formulas can be described as expressions of design parameters. On the basis of the analytical expressions, we formulate the design of OADLC mechanisms from behavioral specifications into an optimization problem that minimizes the weight with given design constraints. The 2D folding patterns of the optimized OADLC mechanisms can be generated automatically and directly delivered for fabrication. Our rapid design method is demonstrated by developing stiffness-enhanced mechanisms with a desired out-of-plane stiffness for a foldable gripper that enables a blimp to perch steadily under air disturbance and weight limit.

Towards One-Dollar Robots: An Integrated Design and Fabrication Strategy for Electromechanical Systems

To improve the accessibility of robotics, we propose a design and fabrication strategy to build low-cost electromechanical systems for robotic devices. Our method, based on origami-inspired cut-and-fold and E-textiles techniques, aims at minimizing the resources for robot creation. Specifically, we explore techniques to create robots with the resources restricted to single-layer sheets (e.g. polyester film) and conductive sewing threads. To demonstrate our strategy's feasibility, these techniques are successfully integrated into an electromechanical oscillator (about 0.40 USD), which can generate electrical oscillation under constant-current power and potentially be used as a simple robot controller in lieu of additional external electronics.

Graph Neural Network for Large-Scale Network Localization

Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to regression problems. In this work, we adopt GNN for a classic but challenging nonlinear regression problem, namely the network localization. Our main findings are in order. First, GNN is potentially the best solution to large-scale network localization in terms of accuracy, robustness and computational time. Second, thresholding of the communication range is essential to its superior performance. Simulation results corroborate that the proposed GNN based method outperforms all benchmarks by far. Such inspiring results are further justified theoretically in terms of data aggregation, non-line-of-sight (NLOS) noise removal and lowpass filtering effect, all affected by the threshold for neighbor selection. Code is available at https://github.com/Yanzongzi/GNN-For-localization.

Analytical Modeling for Rapid Design of Bistable Buckled Beams

Double-clamped bistable buckled beams, as the most elegant bistable mechanisms, demonstrate great versatility in various fields, such as robotics, energy harvesting, and MEMS. However, their design is always hindered by time-consuming and expensive computations. In this work, we present a method to easily and rapidly design bistable buckled beams subjected to a transverse point force. Based on the Euler-Bernoulli beam theory, we establish a theoretical model of bistable buckled beams to characterize their snap-through properties. This model is verified against the results from an FEA model, with discrepancy less than 7 %. By analyzing and simplifying our theoretical model, we derive explicit analytical expressions for critical behavioral values on the force-displacement curve of the beam. These behavioral values include critical force, critical displacement, and travel, which are generally sufficient for characterizing the snap-through properties of a bistable buckled beam. Based on these analytical formulas, we investigate the influence of a bistable buckled beam's key design parameters, including its actuation position and precompression, on its critical behavioral values, with our results validated by FEA simulations. This way, our method enables fast and computationally inexpensive design of bistable buckled beams and can guide the design of complex systems that incorporate bistable mechanisms.