230 research outputs found
Letter. Late cretaceous seasonal ocean variability from the arctic
No full textThe modern Arctic Ocean is regarded as barometer of global change and amplifier of global warming1 and therefore records of past Arctic change are of a premium for palaeoclimate reconstruction. Little is known of the state of the Arctic Ocean in the greenhouse period of the late Cretaceous, yet records from such times may yield important clues to its future behaviour given current global warming trends. Here we present the first seasonally resolved sedimentary record from the Cretaceous from the Alpha Ridge of the Arctic Ocean. This “paleo-sediment trap” provides new insights into the workings of the Cretaceous marine biological carbon pump. Seasonal primary production was dominated by diatom algae but was not related to upwelling as previously hypothesised. Rather, production occurred within a stratified water column, involving specially adapted species in blooms resembling those of the modern North Pacific Subtropical Gyre, or those indicated for the Mediterranean sapropels. With increased CO2 levels and warming currently driving increased stratification in the global ocean, this style of production that is adapted to stratification may become more widespread. Our evidence for seasonal diatom production and flux testify to an ice-free summer, but thin accumulations of terrigenous sediment within the diatom ooze are consistent with the presence of intermittent sea ice in the winter, supporting a wide body of evidence for low temperatures in the Late Cretaceous Arctic Ocean, rather than recent suggestions of a 15 °C mean annual temperature at this time
Semi-autonomous Robotic Disassembly Enhanced by Mixed Reality
Get PDFIn this study, we introduce "SARDiM,"a modular semi-autonomous platform enhanced with mixed reality for industrial disassembly tasks. Through a case study focused on EV battery disassembly, SARDiM integrates Mixed Reality, object segmentation, teleoperation, force feedback, and Variable Autonomy. Utilising the ROS, Unity, and MATLAB platforms, alongside a joint impedance controller, SARDiM facilitates teleoperated disassembly. The approach combines FastSAM for real-time object segmentation, generating data which is subsequently processed through a cluster analysis algorithm to determine the centroid and orientation of the components, categorizing them by size and disassembly priority. This data guides the MoveIt platform in trajectory planning for the Franka Robot arm. SARDiM provides the capability to switch between two teleoperation modes: 1) manual and 2) semi-autonomous with Variable Autonomy. Each was evaluated using four different Interface Methods (IM): 1) direct view, 2) monitor feed, 3) mixed reality with monitor feed, and 4) point cloud mixed reality. Evaluations across the eight IMs demonstrated a 40.61% decrease in joint limit violations using Mode 2. Moreover, Mode 2-IM4 outperformed Mode 1-IM1 by achieving a 2.33%-time reduction while considerably increasing safety, making it optimal for operating in hazardous environments at a safe distance, with the same ease of use as teleoperation with a direct view of the environment.</p
Semi-autonomous Robotic Disassembly Enhanced by Mixed Reality
Get PDFIn this study, we introduce "SARDiM," a modular semi-autonomous platform
enhanced with mixed reality for industrial disassembly tasks. Through a case
study focused on EV battery disassembly, SARDiM integrates Mixed Reality,
object segmentation, teleoperation, force feedback, and variable autonomy.
Utilising the ROS, Unity, and MATLAB platforms, alongside a joint impedance
controller, SARDiM facilitates teleoperated disassembly. The approach combines
FastSAM for real-time object segmentation, generating data which is
subsequently processed through a cluster analysis algorithm to determine the
centroid and orientation of the components, categorizing them by size and
disassembly priority. This data guides the MoveIt platform in trajectory
planning for the Franka Robot arm. SARDiM provides the capability to switch
between two teleoperation modes: manual and semi-autonomous with variable
autonomy. Each was evaluated using four different Interface Methods (IM):
direct view, monitor feed, mixed reality with monitor feed, and point cloud
mixed reality. Evaluations across the eight IMs demonstrated a 40.61% decrease
in joint limit violations using Mode 2. Moreover, Mode 2-IM4 outperformed Mode
1-IM1 by achieving a 2.33%-time reduction while considerably increasing safety,
making it optimal for operating in hazardous environments at a safe distance,
with the same ease of use as teleoperation with a direct view of the
environment
Similar worldwide patterns in the sex pheromone signal and response in the oriental fruit moth, Grapholita molesta (Lepidoptera: Tortricidae)
No full textMulti-Robot Task Planning for Efficient Battery Disassembly in Electric Vehicles
Get PDFWith the surging interest in electric vehicles (EVs), there is a need for advancements in the development and dismantling of lithium-ion batteries (LIBs), which are highly important for the circular economy. This paper introduces an intelligent hybrid task planner designed for multi-robot disassembly and demonstrates its application to an EV lithium-ion battery pack. The objective is to enable multiple robots to operate collaboratively in a single workspace to execute battery disassembly tasks efficiently and without collisions. This approach can be generalized to almost any disassembly task. The planner uses logical and hierarchical strategies to identify object locations from data captured by cameras mounted on each robot’s end-effector, orchestrating coordinated pick-and-place operations. The efficacy of this task planner was assessed through simulations with three trajectory-planning algorithms: RRT, RRTConnect, and RRTStar. Performance evaluations focused on completion times for battery disassembly tasks. The results showed that completion times were similar across the planners, with 543.06 s for RRT, 541.89 s for RRTConnect, and 547.27 s for RRTStar, illustrating that the effectiveness of the task planner is independent of the specific joint-trajectory-planning algorithm used. This demonstrates the planner’s capability to effectively manage multi-robot disassembly operations
Haptic Teleoperation in Extended Reality for Electric Vehicle Battery Disassembly using Gaussian Mixture Regression
Get PDFWe present a comprehensive teleoperation framework for electric vehicle (EV) battery cell handling, integrating haptic feedback, extended reality (XR) visualization, and task-parameterized Gaussian mixture regression (TP-GMR) for adaptive, real-time trajectory generation. The system enables seamless switching between manual and autonomous operation through a variable autonomy mechanism, while constraint barrier functions (CBFs) enforce spatial safety constraints. A lightweight intent prediction module anticipates user deviation and precomputes corrective trajectories, reducing response time from 2.0 s to under 1 ms. The framework is implemented on an industrial KUKA robotic manipulator and validated in structured and real-world EV battery disassembly scenarios. Results show that combining XR and haptic feedback reduces task completion time by up to 48% and path deviation by 32%, compared to manual teleoperation without assistance. Predictive replanning improves continuity of force feedback and reduces unnecessary user motion. The integration of XR-based spatial computing, learning-from-demonstration, and real-time control enables safe, precise, and efficient manipulation in high-risk environments. This study demonstrates a scalable human-in-the-loop solution for battery recycling and other semi-structured tasks, where full automation is impractical. The proposed system significantly improves operator performance while maintaining safety and flexibility, marking a meaningful advancement in collaborative field robotics
Household savings in transition economies
Get PDFDuring the transition from central planning to market economies now under way in Eastern Europe, output levels first collapsed by 40 to 50 percent in most countries, then staged a modest recovery in the last two years. Longer-term revival of growth requires a resumption of investment and thus, realistically, of domestic savings. To explore the determinants of household savings rates in transition economies, the authors studies matching household surveys for three Central European economies: Bulgaria, Hungary, and Poland. They find that savings rates strongly increase with relative income, suggesting that increasing income inequality may play a role in determining savings rates. Savings rates are significantly higher for households that do not own their homes or that own few of the standard consumer durables-possibly because, with no retail credit or mortgage markets, households must save to purchase houses and durables. The influence of demographic factors broadly matches earlier findings for developing countries. Perhaps surprisingly, variables associated with the household's position in the transition process-including either sector of employment (public or private) or form of employment-do not play a significant role in determining savings rates.Environmental Economics&Policies,Services&Transfers to Poor,Economic Theory&Research,Banks&Banking Reform,Payment Systems&Infrastructure,Safety Nets and Transfers,Rural Poverty Reduction,Environmental Economics&Policies,Banks&Banking Reform,Economic Theory&Research
A mini-review on mobile manipulators with Variable Autonomy
Get PDFThis paper presents a mini-review of the current state of research in mobile manipulators with variable levels of autonomy, emphasizing their associated challenges and application environments. The need for mobile manipulators in different environments, especially hazardous ones such as decommissioning and search and rescue, is evident due to the unique challenges and risks each presents. Many systems deployed in these environments are not fully autonomous, requiring human-robot teaming to ensure safe and reliable operations under uncertainties. Through this analysis, we identify gaps and challenges in the literature on Variable Autonomy, including cognitive workload and communication delays, and propose future directions, including whole-body Variable Autonomy for mobile manipulators, virtual reality frameworks, and large language models to reduce operators’ complexity and cognitive load in some challenging and uncertain scenarios
Hyperparameter-optimized CNN and CNN-LSTM for Predicting the Remaining Useful Life of Lithium-Ion Batteries
Get PDFThis paper introduces novel advancements in predicting the Remaining Useful Life (RUL) of Lithium-Ion Batteries (LIBs) using Convolutional Neural Network (CNN) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models, fine-tuned with Bayesian Optimization. Our study presents three key novelties. First, the research is rooted in the utilization of a publicly available dataset comprising 124 LIB cells, ensuring enhanced model generalization, and capturing a diverse array of battery usage patterns. Second, Bayesian Optimization is employed to optimize the hyperparameters of both models, leading to enhanced predictive accuracy. Third, we perform a rigorous direct comparison between the CNN and CNN-LSTM models, demonstrating the superiority of the CNN-LSTM model in RUL prediction by approximately 0.89%. Additionally, this study sheds light on the interpretability of the CNN-LSTM model, providing valuable insights into factors influencing RUL estimation. Both models exhibit high precision, with Mean Absolute Error (MAE) values of 85.6365 and 84.8746 cycles, respectively. The outcomes underscore the practical significance of accurate RUL prediction in LIBs, benefiting Electric Vehicles, battery manufacturing, and efficient maintenance planning. Our research contributes to advancing RUL prediction for LIBs in electric vehicles and energy storage systems
An Exploratory Study on Crack Detection in Concrete through Human-Robot Collaboration
Get PDFStructural inspection in nuclear facilities is vital for maintaining operational safety and integrity. Traditional methods of manual inspection pose significant challenges, including safety risks, high cognitive demands, and potential inaccuracies due to human limitations. Recent advancements in Artificial Intelligence (AI) and robotic technologies have opened new possibilities for safer, more efficient, and accurate inspection methodologies. Specifically, Human-Robot Collaboration (HRC), leveraging robotic platforms equipped with advanced detection algorithms, promises significant improvements in inspection outcomes and reductions in human workload. This study explores the effectiveness of AI-assisted visual crack detection integrated into a mobile Jackal robot platform. The experiment results indicate that HRC enhances inspection accuracy and reduces operator workload, resulting in potential superior performance outcomes compared to traditional manual methods
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