1,721,172 research outputs found
Introduction and State of the Art
No full textThis chapter introduces the notion of cloaking and reviews the main ideas that have been developed during the last fifteen years to take this concept from theory to experimental assessment, underlining issues that remain open challenges for future research
Design and Experimental Validation of an Elliptic Cloak
No full textIn this chapter we use the method introduced in Chap. 4 to design a non-axisymmetric cloak and produce an underwater experimental validation of its functioning
Wave Propagation in Periodic Media
No full textThis chapter is devoted to the review of some useful results related to wave propagation both in homogeneous and in periodic media and is intended to recall the fundamental knowledge on which the following of this work builds, other than to set the general notation used
Transformation Acoustics in Elliptic Coordinates
No full textIn this chapter, a method based on transformation acoustics is introduced to tackle systematically the design of pentamode cloaks aiming at reducing the acoustic scattering of elliptical obstacles
Transformation Acoustics
No full textThe purpose of this chapter is to review the background knowledge on transformation methods that is then used in the following of the book
MLIO: Multiple LiDARs and Inertial Odometry
No full textWith the decreasing cost of LiDAR sensors, sensor setups with multiple LiDARs are becoming available. In such advanced setups with multiple LiDARs the sensor temporal asynchronicity and spatial miscalibration are critical factors for vehicle localization increasing measurement uncertainty. Hence, simple merging of synchronized point clouds as done in some literature can lead to sub-optimal results. To tackle this problem we propose MLIO, a factor graph-based odometry computation algorithm that fuses multiple LiDARs with an inertial measurement unit (IMU) and provides an accurate solution mitigating the effect of temporal asynchronisity and spatial miscalibration. The proposed algorithm is validated using a custom dataset. We compare the proposed algorithm with the state-of-the-art LiDAR-only odometry algorithms, such as KISS-ICP, and LiDAR-IMU fusion LIO-SAM and demonstrate its superiority. We were able to achieve up to 40% and 16% increment in positional and orientation accuracy compared to KISS-ICP and 25% increment in positional accuracy compared to LIO-SAM
Vehicle State Estimation Through Dynamics Modeled Factor Graph
No full textEgo Vehicle state estimation is integral to every autonomous driving software stack. Thereby, the estimation of the state and its components as for example the side slip angle, is a crucial component to track the vehicle maneuvers. In the absence of a direct sensor measuring side slip angle, most of the existing literature either use observers like Kalman Filters or non-modular factor graphs by modeling lateral dynamics. However, the modularity of such graphs, to integrate multiple asynchronous sensors that provide disentangled measurements, like LiDAR, GNSS, and IMU is still overlooked in the literature. In this work, we propose a novel factor graph-based architecture that builds upon the vehicle dynamics at its core to enable the fusion of multiple sensors asynchronously and enables to perform robust and accurate state estimation. We validate the proposed algorithm against two baselines, a model-based Extended Kalman Filter and a factor graph-based state estimator that uses the IMU pre-integration factor as a reference factor. The algorithms are validated in a custom dataset collected using an in-house vehicle
Optimal design of broadband, low-directivity graded index acoustic lenses for underwater communication
No full textManipulating underwater pressure waves is crucial for marine exploration, as electromagnetic signals are strongly absorbed in water. However, the multi-path phenomenon complicates the accurate capture of acoustic waves by receivers. Although graded index lenses, based on metamaterials with smoothly varying properties, successfully focus pressure waves, they tend to have high directivity, which hinders practical application. This work introduces three 2D acoustic lenses made from a metamaterial composed of solid inclusions in water. We propose an optimization scheme where the pressure dynamics is governed by Helmholtz's equation, with control parameters affecting each lens cell's density and bulk modulus. Through an appropriate cost function, the optimization encourages a broadband, low-directivity lens. The large-scale optimization is solved using the Lagrangian approach, which provides an analytical expression for the cost gradient. This scheme avoids the need for a separate discretization step, allowing the design to transition directly from the desired smooth refractive index to a practical lattice structure. As a result, the optimized lens closely aligns with real-world behavior. The homogenized numerical model is validated against finite elements, which considers acoustic-elastic coupling at the microstructure level. When homogenization holds, this approach proves to be an effective design tool for achieving broadband, low-directivity acoustic lenses
A Rule-Defined Adaptive MPC Based Motion Planner for Autonomous Driving Applications
No full textIn autonomous driving systems, motion planning to reach a given destination while avoiding obstacles becomes a task entirely managed by the on-board unit. In this work, we present a rule-defined motion planning algorithm for autonomous driving applications based on an adaptive Model Predictive Controller (MPC) framework. The motion planning task is first formulated as an Optimal Control Problem (OCP) subject to time-varying Control Barrier Function (CBF) constraints. It is then integrated within an MPC framework with adaptive weights settings, enabling the algorithm to dynamically adjust the MPC weights according to the rule-defined driving scenarios. The developed motion planner generates optimized trajectories for a high-fidelity Autonomous Vehicle (AV) model within IPG CarMaker software. Simulations performed showed that the developed motion planner adeptly facilitates successful overtaking, following, and stopping of the AV behind the Obstacle Vehicle (OV) based on rule-defined scenarios perceived by the AV
Enhancing Disassembly Practices for Electric Vehicle Battery Packs: A Narrative Comprehensive Review
Full text linkIn the context of current societal challenges, such as climate neutrality, industry digitization, and circular economy, this paper addresses the importance of improving recycling practices for electric vehicle (EV) battery packs, with a specific focus on lithium–ion batteries (LIBs). To achieve this, the paper conducts a systematic review (using Google Scholar, Scopus, and Web of Science as search engines), considering the last 10 years, to examine existing recycling methods, robotic/collaborative disassembly cells, and associated control techniques. The aim is to provide a comprehensive and detailed review that can serve as a valuable resource for future research in the industrial domain. By analyzing the current state of the field, this review identifies emerging needs and challenges that need to be addressed for the successful implementation of automatic robotic disassembly cells for end-of-life (EOL) electronic products, such as EV LIBs. The findings presented in this paper enhance our understanding of recycling practices and lay the groundwork for more precise research directions in this important area
- …
