This letter presents a novel cascade state estimation framework for the three-dimensional (3-D) center of mass (CoM) estimation of walking humanoid robots. The proposed framework, called State Estimation RObot Walking (SEROW), fuses effectively joint encoder, inertial, feet pressure, and visual odometry measurements. Initially, we consider the humanoid's Newton-Euler dynamics and rigorously derive the nonlinear CoM estimator. The latter accurately estimates the 3D-CoM position, velocity, and external forces acting on the CoM, while directly considering the presence of uneven terrain and the body's angular momentum rate and, thus, effectively coupling the frontal with the lateral plane dynamics. Furthermore, we extend an established floating mass estimator to take into account the support foot pose, yielding in such a way the mandatory, for CoM estimation, affine transformations and forming a cascade state estimation scheme. Subsequently, we quantitatively and qualitatively assess the proposed scheme by comparing it to other estimation structures in terms of accuracy and robustness to disturbances, both in simulation and on an actual NAO robot walking outdoors over an inclined terrain. To facilitate further research endeavors, our implementation is offered as an open-source ROS/C++ package.

In the quest for sustainable manufacturing, the integration of robotic assembly and disassembly processes has emerged as a pivotal innovation. This paper explores advancements in robotic assembly and disassembly aimed at enhancing sustainable manufacturing. Key advancements include machine learning for optimized robotic motion, advanced sensors for real-time control, and collaborative robots (cobots) for safe human-robot interaction. These technologies enable precise and efficient assembly of complex products and effective end-of-life disassembly for recycling. Case studies across various industries demonstrate significant reductions in waste, energy consumption, and environmental impact. The paper discusses current challenges and future directions, emphasizing the need for interdisciplinary collaboration and ongoing technological development to achieve fully sustainable manufacturing systems.

Globally, workplace safety is a critical concern, and statistics highlight the widespread impact of occupational hazards. According to the International Labour Organization (ILO), an estimated 2.78 million work-related fatalities occur worldwide each year, with an additional 374 million non-fatal workplace injuries and illnesses. These incidents result in significant economic and social costs, emphasizing the urgent need for effective safety measures across industries. The construction sector in particular faces substantial challenges, contributing a notable share to these statistics due to the nature of its operations. As technology, including machine vision algorithms and robotics, continues to advance, there is a growing opportunity to enhance global workplace safety standards and mitigate the human toll of occupational hazards on a broader scale. This paper explores the development and evaluation of two distinct algorithms designed for the accurate detection of safety equipment on construction sites. The first algorithm leverages the Faster R-CNN architecture, employing ResNet-50 as its backbone for robust object detection. Subsequently, the results obtained from Faster R-CNN are compared with those of the second algorithm, Few-Shot Object Detection (FsDet). The selection of FsDet is motivated by its efficiency in addressing the time-intensive process of compiling datasets for network training in object recognition. The research methodology involves training and fine-tuning both algorithms to assess their performance in safety equipment detection. Comparative analysis aims to evaluate the effectiveness of novel training methods employed in the development of these machine vision algorithms.

Intravenous Access (IVA) is the most common invasive medical procedure. Globally, it is estimated that over one billion IVA devices (needles/catheters) are used annually. However, the overall failure rate in this procedure is unacceptably high, reaching values between 35 and 50%. This has driven a great deal of research and technological development over recent decades, including the integration of different levels of autonomy in IVA medical devices to greatly improve this process. This paper will review these recent technical developments, including methods and systems for vein imaging and localization, needle insertion, venipuncture detection, catheter placement, and complete robotic IVA platforms. Furthermore, this paper explores emerging technical aspects, current limitations, and new research directions that may enable wider clinical translation and better acceptance of robotic IVA technologies.

Needle intervention is widely employed in clinical practices such as biopsies, regional anesthesia, blood sampling, neurosurgery, and brachytherapy. Traditional needle insertion relies on surgeon expertise and kinesthetic feedback, yet accurately targeting deep tissue structures remains challenging. To address this, significant research has advanced sensing technologies to aid insertion accuracy. This paper comprehensively reviews recent developments in needle insertion sensing techniques, encompassing needle tip position tracking, proximity measurement, and puncture detection. It evaluates these methods across metrics including accuracy, cost-effectiveness, portability, compatibility, noise resistance capability, Technology Readiness Level (TRL), and future trends. Emerging research directions highlight advancements in machine learning integration, miniaturization, and enhanced multimodal sensing capabilities to improve procedural outcomes and expand application domains.