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The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance., Comment: 8 pages,14 figures, ICUAS 2024

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400~g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors' integrity. Notably, the arm's damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for \ac{MAV} in real environments subject to perturbations during interaction., Comment: 6 pages, 7 figures, ROBOSOFT 2024

In this work, we present an autonomous inspection framework for remote sensing tasks in active open-pit mines. Specifically, the contributions are focused towards developing a methodology where an initial approximate operator-defined inspection plan is exploited by an online view-planner to predict an inspection path that can adapt to changes in the current mine-face morphology caused by route mining activities. The proposed inspection framework leverages instantaneous 3D LiDAR and localization measurements coupled with modelled sensor footprint for view-planning satisfying desired viewing and photogrammetric conditions. The efficacy of the proposed framework has been demonstrated through simulation in Feiring-Bruk open-pit mine environment and hardware-based outdoor experimental trials. The video showcasing the performance of the proposed work can be found here: https://youtu.be/uWWbDfoBvFc, Comment: Accepted for publication in IEEE ROBIO 2024

This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. The proposed approach is based on a Kubernetes-based scheduling mechanism designed to monitor and optimize the operation of CNMPCs, while addressing the scalability limitation of centralized control schemes. By leveraging a cluster in a real-time cloud environment, the proposed mechanism effectively offloads the computational burden of CNMPCs. Through experiments, we have demonstrated the effectiveness and performance of our system, especially in scenarios where the number of robots is subject to change. Our work contributes to the advancement of cloud-based control strategies and lays the foundation for enhanced performance in cloud-controlled robotic systems., Comment: 7 pages, 6 figures, IECON 2024

Recent advances in robotics are pushing real-world autonomy, enabling robots to perform long-term and large-scale missions. A crucial component for successful missions is the incorporation of loop closures through place recognition, which effectively mitigates accumulated pose estimation drift. Despite computational advancements, optimizing performance for real-time deployment remains challenging, especially in resource-constrained mobile robots and multi-robot systems since, conventional keyframe sampling practices in place recognition often result in retaining redundant information or overlooking relevant data, as they rely on fixed sampling intervals or work directly in the 3D space instead of the feature space. To address these concerns, we introduce the concept of sample space in place recognition and demonstrate how different sampling techniques affect the query process and overall performance. We then present a novel keyframe sampling approach for LiDAR-based place recognition, which focuses on redundancy minimization and information preservation in the hyper-dimensional descriptor space. This approach is applicable to both learning-based and handcrafted descriptors, and through the experimental validation across multiple datasets and descriptor frameworks, we demonstrate the effectiveness of our proposed method, showing it can jointly minimize redundancy and preserve essential information in real-time. The proposed approach maintains robust performance across various datasets without requiring parameter tuning, contributing to more efficient and reliable place recognition for a wide range of robotic applications., Comment: 20 pages, 15 figures. Submitted

This article studies the commonsense object affordance concept for enabling close-to-human task planning and task optimization of embodied robotic agents in urban environments. The focus of the object affordance is on reasoning how to effectively identify object's inherent utility during the task execution, which in this work is enabled through the analysis of contextual relations of sparse information of 3D scene graphs. The proposed framework develops a Correlation Information (CECI) model to learn probability distributions using a Graph Convolutional Network, allowing to extract the commonsense affordance for individual members of a semantic class. The overall framework was experimentally validated in a real-world indoor environment, showcasing the ability of the method to level with human commonsense. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://youtu.be/BDCMVx2GiQE, Comment: Accepted in IROS24

Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding. This article proposes BOX3D, a novel multi-modal and lightweight scheme for localizing objects of interest by fusing the information from RGB camera and 3D LiDAR. BOX3D is structured around a three-layered architecture, building up from the local perception of the incoming sequential sensor data to the global perception refinement that covers for outliers and the general consistency of each object's observation. More specifically, the first layer handles the low-level fusion of camera and LiDAR data for initial 3D bounding box extraction. The second layer converts each LiDAR's scan 3D bounding boxes to the world coordinate frame and applies a spatial pairing and merging mechanism to maintain the uniqueness of objects observed from different viewpoints. Finally, BOX3D integrates the third layer that supervises the consistency of the results on the global map iteratively, using a point-to-voxel comparison for identifying all points in the global map that belong to the object. Benchmarking results of the proposed novel architecture are showcased in multiple experimental trials on public state-of-the-art large-scale dataset of urban environments., Comment: Presented in MED 2024

This work presents a fully integrated tree-based combined exploration-planning algorithm: Exploration-RRT (ERRT). The algorithm is focused on providing real-time solutions for local exploration in a fully unknown and unstructured environment while directly incorporating exploratory behavior, robot-safe path planning, and robot actuation into the central problem. ERRT provides a complete sampling and tree-based solution for evaluating "where to go next" by considering a trade-off between maximizing information gain, and minimizing the distances travelled and the robot actuation along the path. The complete scheme is evaluated in extensive simulations, comparisons, as well as real-world field experiments in constrained and narrow subterranean and GPS-denied environments. The framework is fully ROS-integrated, straight-forward to use, and we open-source it at https://github.com/LTU-RAI/ExplorationRRT., Comment: 19 pages, 29 figures Transactions on Robotics

In this article, we introduce a novel strategy for robotic exploration in unknown environments using a semantic topometric map. As it will be presented, the semantic topometric map is generated by segmenting the grid map of the currently explored parts of the environment into regions, such as intersections, pathways, dead-ends, and unexplored frontiers, which constitute the structural semantics of an environment. The proposed exploration strategy leverages metric information of the frontier, such as distance and angle to the frontier, similar to existing frameworks, with the key difference being the additional utilization of structural semantic information, such as properties of the intersections leading to frontiers. The algorithm for generating semantic topometric mapping utilized by the proposed method is lightweight, resulting in the method's online execution being both rapid and computationally efficient. Moreover, the proposed framework can be applied to both structured and unstructured indoor and outdoor environments, which enhances the versatility of the proposed exploration algorithm. We validate our exploration strategy and demonstrate the utility of structural semantics in exploration in two complex indoor environments by utilizing a Turtlebot3 as the robotic agent. Compared to traditional frontier-based methods, our findings indicate that the proposed approach leads to faster exploration and requires less computation time., Comment: Accepted to ICRA 24

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to \blue92% fewer nodes than the next best solution., Comment: Submitted to Journal of Intelligent & Robotic Systems

Neutrino event generators make use of intranuclear cascade models (INCs), to predict the kinematics of hadrons produced in neutrino-nucleus interactions. We perform a consistent comparison of different INCs, by using the same set of events as input to the NEUT, NuWro, Achilles and INCL INCs. The inputs correspond to calculations of the fully differential single-proton knockout cross section, either in the distorted-wave impulse approximation (DWIA) or plane-wave impulse approximation (PWIA), both including realistic nuclear hole spectral functions. We compare the INC results to DWIA calculations with an optical potential, used extensively in the analysis of (e,e'p) experiments. We point out a systematic discrepancy between both approaches. We apply the INC results to recent MicroBooNE data. We assess the influence of the choice of spectral function, finding that large variations in realistic spectral functions are indistinguishable with present data. The data is underpredicted, with strength missing in the region where two-nucleon knockout and resonance production contribute. However, the data is underpredicted also in regions of low transverse missing momentum, where one-nucleon knockout dominates. The inclusion of the interference with two-body currents could lead to additional strength in this region., Comment: 26 pages including appendices, comments welcome

This article introduces a novel method for converting 3D voxel maps, commonly utilized by robots for localization and navigation, into 2D occupancy maps for both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The generated 2D maps can be used for more efficient global navigation for both UAVs and UGVs, in enabling algorithms developed for 2D maps to be useful in 3D applications, and allowing for faster transfer of maps between multiple agents in bandwidth-limited scenarios. The proposed method uses the free space representation in the UFOMap mapping solution to generate 2D occupancy maps. During the 3D to 2D map conversion, the method conducts safety checks and eliminates free spaces in the map with dimensions (in the height axis) lower than the robot's safety margins. This ensures that an aerial or ground robot can navigate safely, relying primarily on the 2D map generated by the method. Additionally, the method extracts the height of navigable free space and a local estimate of the slope of the floor from the 3D voxel map. The height data is utilized in converting paths generated using the 2D map into paths in 3D space for both UAVs and UGVs. The slope data identifies areas too steep for a ground robot to traverse, marking them as occupied, thus enabling a more accurate representation of the terrain for ground robots. The effectiveness of the proposed method in enabling computationally efficient navigation for both aerial and ground robots is validated in two different environments, over both static maps and in online implementation in an exploration mission. The methods proposed within this article have been implemented in the popular robotics framework ROS and are open-sourced. The code is available at: https://github.com/LTU-RAI/Map-Conversion-3D-Voxel-Map-to-2D-Occupancy-Map., Comment: Submitted to IEEE Robotics and Automation Letters (RAL)

Neutrino-induced single-pion production constitutes an essential interaction channel in modern neutrino oscillation experiments, with its products building up a significant fraction of the observable hadronic final states. Frameworks of oscillation analyses strongly rely on Monte Carlo neutrino event generators, which provide theoretical predictions of neutrino interactions on nuclear targets. Thus, it is crucial to integrate state-of-the-art single-pion production models with Monte Carlo simulations to prepare for the upcoming systematics-dominated landscape of neutrino measurements. In this work, we present the implementation of the Ghent Hybrid model for neutrino-induced single-pion production in the NuWro Monte Carlo event generator. The interaction dynamics includes coherently-added contributions from nucleon resonances and a non-resonant background, merged into the pythia branching predictions in the deep-inelastic regime, as instrumented by NuWro. This neutrino-nucleon interaction model is fully incorporated into the nuclear framework of the generator, allowing it to account for the influence of both initial- and final-state nuclear medium effects. We compare the predictions of this integrated implementation with recent pion production data from accelerator-based neutrino experiments. The results of the novel model show improved agreement of the generator predictions with the data and point to the significance of the refined treatment of the description of pion-production processes beyond the $\Delta$ region., Comment: 26 pages, 16 figures

In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps, eliminating the need for the time-consuming global feature extraction and feature matching process. The estimated overlapping regions are used to calculate a homogeneous rigid transform, which serves as an initial condition for the GICP point cloud registration algorithm to refine the alignment between the maps. The advantages of this approach include faster processing time, improved accuracy, and increased robustness in challenging environments. Furthermore, the effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration in a variety of different underground environments., Comment: 28 pages, 24 figures. Accepted to the IEEE Transactions on Field Robotics

In our exploration of Composable Memory systems utilizing CXL, we focus on overcoming adoption barriers at Hyperscale, underscored by economic models demonstrating Total Cost of Ownership (TCO). While CXL addresses the pressing memory capacity needs of emerging Hyperscale applications, the escalating demands from evolving use cases such as AI outpace the capabilities of current CXL solutions. Hyperscalers resort to software-based memory (de)compression technology, alleviating memory capacity, storage, and network constraints but incurring a notable "Tax" on Compute CPU cycles. As a pivotal guide to the CXL community, Hyperscalers have formulated the groundbreaking Open Compute Project (OCP) Hyperscale CXL Tiered Memory Expander specification. If implemented, this specification lowers TCO adoption barriers, enabling diverse CXL deployments at both Hyperscaler and Enterprise levels. We present a CXL integrated solution, aligning with the aforementioned specification, introducing an energy-efficient, scalable, hardware-accelerated, Lossless Compressed Memory CXL Tier. This solution, slated for mid-2024 production and open for integration with Memory Expander controller manufacturers, offers 2-3X CXL memory compression in nanoseconds, delivering a 20-25% reduction in TCO for end customers without requiring additional physical slots. In our discussion, we pinpoint areas for collaborative innovation within the CXL Community to expedite software/hardware advancements for CXL Tiered Memory Expansion. Furthermore, we delve into unresolved challenges in Pooled deployment and explore potential solutions, collectively aiming to make CXL adoption a "No Brainer" at Hyperscale., Comment: Presented at the 3rd Workshop on Heterogeneous Composable and Disaggregated Systems (HCDS 2024)

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which is tracked by an adaptive controller to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera., Comment: 7 pages, 7 figures, accepted to be published in IEEE International Conference on Robotics and Automation (ICRA 2024)

The fifth generation (5G) cellular network technology is mature and increasingly utilized in many industrial and robotics applications, while an important functionality is the advanced Quality of Service (QoS) features. Despite the prevalence of 5G QoS discussions in the related literature, there is a notable absence of real-life implementations and studies concerning their application in time-critical robotics scenarios. This article considers the operation of time-critical applications for 5G-enabled unmanned aerial vehicles (UAVs) and how their operation can be improved by the possibility to dynamically switch between QoS data flows with different priorities. As such, we introduce a robotics oriented analysis on the impact of the 5G QoS functionality on the performance of 5G-enabled UAVs. Furthermore, we introduce a novel framework for the dynamic selection of distinct 5G QoS data flows that is autonomously managed by the 5G-enabled UAV. This problem is addressed in a novel feedback loop fashion utilizing a probabilistic finite state machine (PFSM). Finally, the efficacy of the proposed scheme is experimentally validated with a 5G-enabled UAV in a real-world 5G stand-alone (SA) network., Comment: Accepted in ICRA 2024 -> to be available in conference proceedings

This article introduces an experimental emulation of a novel chunk-based flexible multi-DoF aerial 3D printing framework. The experimental demonstration of the overall autonomy focuses on precise motion planning and task allocation for a UAV, traversing through a series of planned space-filling paths involved in the aerial 3D printing process without physically depositing the overlaying material. The flexible multi-DoF aerial 3D printing is a newly developed framework and has the potential to strategically distribute the envisioned 3D model to be printed into small, manageable chunks suitable for distributed 3D printing. Moreover, by harnessing the dexterous flexibility due to the 6 DoF motion of UAV, the framework enables the provision of integrating the overall autonomy stack, potentially opening up an entirely new frontier in additive manufacturing. However, it's essential to note that the feasibility of this pioneering concept is still in its very early stage of development, which yet needs to be experimentally verified. Towards this direction, experimental emulation serves as the crucial stepping stone, providing a pseudo mockup scenario by virtual material deposition, helping to identify technological gaps from simulation to reality. Experimental emulation results, supported by critical analysis and discussion, lay the foundation for addressing the technological and research challenges to significantly push the boundaries of the state-of-the-art 3D printing mechanism., Comment: This paper has been accepted for publication at IEEE International Conference on Robotics and Automation (ICRA) 2024

In this article, we propose the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information. We propose a graph-based learning methodology for the computation of belief (also referred to as expectation) on any given 3D scene graph, which is then used to strategically add new nodes (referred to as blind nodes) that are relevant to a robotic mission. We propose the method of Computation of Expectation based on Correlation Information (CECI), to reasonably approximate real Belief/Expectation, by learning histograms from available training data. A novel Graph Convolutional Neural Network (GCN) model is developed, to learn CECI from a repository of 3D scene graphs. As no database of 3D scene graphs exists for the training of the novel CECI model, we present a novel methodology for generating a 3D scene graph dataset based on semantically annotated real-life 3D spaces. The generated dataset is then utilized to train the proposed CECI model and for extensive validation of the proposed method. We establish the novel concept of \textit{Belief Scene Graphs} (BSG), as a core component to integrate expectations into abstract representations. This new concept is an evolution of the classical 3D scene graph concept and aims to enable high-level reasoning for task planning and optimization of a variety of robotics missions. The efficacy of the overall framework has been evaluated in an object search scenario, and has also been tested in a real-life experiment to emulate human common sense of unseen-objects. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://youtu.be/hsGlSCa12iY, Comment: Accepted at ICRA 2024

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-the-art graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor.

In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet's methodology involves a transformative process: it projects 3D point clouds into range images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, restoring the original point cloud. Additionally, RecNet utilizes the latent vector extracted from this process for efficient place recognition tasks. This approach not only achieves comparable place recognition results but also maintains a compact representation, suitable for sharing among robots to reconstruct their collective maps. The evaluation of RecNet encompasses an array of metrics, including place recognition performance, the structural similarity of the reconstructed point clouds, and the bandwidth transmission advantages, derived from sharing only the latent vectors. Our proposed approach is assessed using both a publicly available dataset and field experiments$^1$, confirming its efficacy and potential for real-world applications., Comment: Accepted for publication in the 2024 IEEE International Conference on Robotics and Automation in Yokohama, (ICRA24)

We introduce the novel approach of using the superscaling variable as an observable and an analysis tool in the context of charged current neutrino-nucleus interactions. We study the relation between the superscaling variable and the removal energy, in addition to other fundamental parameters of the neutrino-nucleus interaction models. In the second half of the paper, we discuss the experimental viability of this measurement following a study of neutrino energy and missing momentum reconstruction. We show that the superscaling variable is measurable in neutrino interaction experiments provided that the proton is detected in the final state. We discuss the resolution of this measurement, and the limitation imposed by the proton's detection threshold.

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art.

Theoretical predictions for lepton-induced single-pion production (SPP) on $^{12}$C are revisited in order to assess the effect of different treatments of the current operator. On one hand we have the asymptotic approximation, which consists in replacing the particle four-vectors that enter in the operator by their asymptotic values, i.e., their values out of the nucleus. On the other hand we have the full calculation, which is a more accurate approach to the problem. We also compare with results in which the final nucleon is described by a relativistic plane wave, to rate the effect of the nucleon distortion. The study is performed for several lepton kinematics, reproducing inclusive and semi-inclusive cross sections belonging to the low-$Q^2$ region (between 0.05 and 1 GeV$^2$), which is of special interest in charged-current (CC) neutrino-nucleus 1$\pi$ production. Inclusive electron results are compared with experimental data. We find non-trivial corrections comparable in size with the effect of the nucleon distortion, namely, corrections up to 6\%, either increasing or diminishing the asymptotic prediction, and a shift of the distributions towards higher energy transfer. For the semi-inclusive cross sections, we observe the correction to be prominent mainly at low values of the outgoing nucleon kinetic energy. Finally, for CC neutrino-induced 1$\pi^+$ production, we find a reduction at low-$Q^2$ with respect to both the plane-wave approach and the asymptotic case.

In recent years, the need for resources for handling processes with high computational complexity for mobile robots is becoming increasingly urgent. More specifically, robots need to autonomously operate in a robust and continuous manner, while keeping high performance, a need that led to the utilization of edge computing to offload many computationally demanding and time-critical robotic procedures. However, safe mechanisms should be implemented to handle situations when it is not possible to use the offloaded procedures, such as if the communication is challenged or the edge cluster is not available. To this end, this article presents a switching strategy for safety, redundancy, and optimized behavior through an edge computing-based Model Predictive Controller (MPC) and a low-level onboard-PID controller for edge-connected Unmanned Aerial Vehicles (UAVs). The switching strategy is based on the communication Key Performance Indicators (KPIs) over 5G to decide whether the UAV should be controlled by the edge-based or have a safe fallback based on the onboard controller., Comment: 8 pages, 9 figures, isie2023

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi fingerprinting in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks., Comment: 7 pages, 5 figures. Accepted for publication in the 21st International Conference on Advanced Robotics (ICAR 2023)

We introduce SAGE; a Generative LLM for inferring attribute values for products across world-wide e-Commerce catalogs. We introduce a novel formulation of the attribute-value prediction problem as a Seq2Seq summarization task, across languages, product types and target attributes. Our novel modeling approach lifts the restriction of predicting attribute values within a pre-specified set of choices, as well as, the requirement that the sought attribute values need to be explicitly mentioned in the text. SAGE can infer attribute values even when such values are mentioned implicitly using periphrastic language, or not-at-all-as is the case for common-sense defaults. Additionally, SAGE is capable of predicting whether an attribute is inapplicable for the product at hand, or non-obtainable from the available information. SAGE is the first method able to tackle all aspects of the attribute-value-prediction task as they arise in practical settings in e-Commerce catalogs. A comprehensive set of experiments demonstrates the effectiveness of the proposed approach, as well as, its superiority against state-of-the-art competing alternatives. Moreover, our experiments highlight SAGE's ability to tackle the task of predicting attribute values in zero-shot setting; thereby, opening up opportunities for significantly reducing the overall number of labeled examples required for training., Comment: (17 pages)

The task of establishing and maintaining situational awareness in an unknown environment is a critical step to fulfil in a mission related to the field of rescue robotics. Predominantly, the problem of visual inspection of urban structures is dealt with view-planning being addressed by map-based approaches. In this article, we propose a novel approach towards effective use of Micro Aerial Vehicles (MAVs) for obtaining a 3-D shape of an unknown structure of objects utilizing a map-independent planning framework. The problem is undertaken via a bifurcated approach to address the task of executing a closer inspection of detected structures with a wider exploration strategy to identify and locate nearby structures, while being equipped with limited sensing capability. The proposed framework is evaluated experimentally in a controlled indoor environment in presence of a mock-up environment validating the efficacy of the proposed inspect-explore policy.

The future of 3D printing utilizing unmanned aerial vehicles (UAVs) presents a promising capability to revolutionize manufacturing and to enable the creation of large-scale structures in remote and hard- to-reach areas e.g. in other planetary systems. Nevertheless, the limited payload capacity of UAVs and the complexity in the 3D printing of large objects pose significant challenges. In this article we propose a novel chunk-based framework for distributed 3D printing using UAVs that sets the basis for a fully collaborative aerial 3D printing of challenging structures. The presented framework, through a novel proposed optimisation process, is able to divide the 3D model to be printed into small, manageable chunks and to assign them to a UAV for partial printing of the assigned chunk, in a fully autonomous approach. Thus, we establish the algorithms for chunk division, allocation, and printing, and we also introduce a novel algorithm that efficiently partitions the mesh into planar chunks, while accounting for the inter-connectivity constraints of the chunks. The efficiency of the proposed framework is demonstrated through multiple physics based simulations in Gazebo, where a CAD construction mesh is printed via multiple UAVs carrying materials whose volume is proportionate to a fraction of the total mesh volume., Comment: This paper has been accepted for publication at 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

LiDAR is currently one of the most utilized sensors to effectively monitor the status of power lines and facilitate the inspection of remote power distribution networks and related infrastructures. To ensure the safe operation of the smart grid, various remote data acquisition strategies, such as Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TSL) have been leveraged to allow continuous monitoring of regional power networks, which are typically surrounded by dense vegetation. In this article, an unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage, as well as the surrounding vegetation in a Power Line Corridor (PLC) solely from LiDAR data. Initially, the proposed approach eliminates the ground points from higher elevation points based on statistical analysis that applies density criteria and histogram thresholding. After denoising and transforming of the remaining candidate points by applying Principle Component Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a two-stage DBSCAN clustering to identify each power line individually. Finally, all high elevation points in the PLC are identified based on their distance to the newly segmented power lines. Conducted experiments illustrate that the proposed framework is an agnostic method that can efficiently detect the power lines and perform PLC-based hazard analysis., Comment: Accepted in the 22nd World Congress of the International Federation of Automatic Control [IFAC WC 2023]

Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean (Sub-T) environments in the presence of aerosol particles have recently become the main focus in the field of robotics. Aerosol particles such as smoke and dust directly affect the performance of any mobile robotic platform due to their reliance on their onboard perception systems for autonomous navigation and localization in Global Navigation Satellite System (GNSS)-denied environments. Although obstacle avoidance and object detection algorithms are robust to the presence of noise to some degree, their performance directly relies on the quality of captured data by onboard sensors such as Light Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel modular agnostic filtration pipeline based on intensity and spatial information such as local point density for removal of detected smoke particles from Point Cloud (PCL) prior to its utilization for collision detection. Furthermore, the efficacy of the proposed framework in the presence of smoke during multiple frontier exploration missions is investigated while the experimental results are presented to facilitate comparison with other methodologies and their computational impact. This provides valuable insight to the research community for better utilization of filtration schemes based on available computation resources while considering the safe autonomous navigation of mobile robots., Comment: Accepted in the 49th Annual Conference of the IEEE Industrial Electronics Society [IECON2023]

This review paper emphasizes the significance of microscopic calculations with quantified theoretical error estimates in studying lepton-nucleus interactions and their implications for electron-scattering and accelerator neutrino-oscillation measurements. We investigate two approaches: Green's Function Monte Carlo and the extended factorization scheme, utilizing realistic nuclear target spectral functions. In our study, we include relativistic effects in Green's Function Monte Carlo and validate the inclusive electron-scattering cross section on carbon using available data. We compare the flux folded cross sections for neutrino-Carbon scattering with T2K and MINER$\nu$A experiments, noting the substantial impact of relativistic effects in reducing the theoretical curve strength when compared to MINER$\nu$A data. Additionally, we demonstrate that quantum Monte Carlo-based spectral functions accurately reproduce the quasi-elastic region in electron-scattering data and T2K flux folded cross sections. By comparing results from Green's Function Monte Carlo and the spectral function approach, which share a similar initial target state description, we quantify errors associated with approximations in the factorization scheme and the relativistic treatment of kinematics in Green's Function Monte Carlo., Comment: 30 pages, 9 figures

Change detection and irregular object extraction in 3D point clouds is a challenging task that is of high importance not only for autonomous navigation but also for updating existing digital twin models of various industrial environments. This article proposes an innovative approach for change detection in 3D point clouds using deep learned place recognition descriptors and irregular object extraction based on voxel-to-point comparison. The proposed method first aligns the bi-temporal point clouds using a map-merging algorithm in order to establish a common coordinate frame. Then, it utilizes deep learning techniques to extract robust and discriminative features from the 3D point cloud scans, which are used to detect changes between consecutive point cloud frames and therefore find the changed areas. Finally, the altered areas are sampled and compared between the two time instances to extract any obstructions that caused the area to change. The proposed method was successfully evaluated in real-world field experiments, where it was able to detect different types of changes in 3D point clouds, such as object or muck-pile addition and displacement, showcasing the effectiveness of the approach. The results of this study demonstrate important implications for various applications, including safety and security monitoring in construction sites, mapping and exploration and suggests potential future research directions in this field., Comment: Accepted for publication in the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

Vitamin-V is a 2023-2025 Horizon Europe project that aims to develop a complete RISC-V open-source software stack for cloud services with comparable performance to the cloud-dominant x86 counterpart and a powerful virtual execution environment for software development, validation, verification, and test that considers the relevant RISC-V ISA extensions for cloud deployment., Comment: Paper accepted and presented at the RISC-V Summit Europe, Barcelona, 5-9th June 2023. arXiv admin note: substantial text overlap with arXiv:2305.01983

This article presents a novel approach to identifying and classifying intersections for semantic and topological mapping. More specifically, the proposed novel approach has the merit of generating a semantically meaningful map containing intersections, pathways, dead ends, and pathways leading to unexplored frontiers. Furthermore, the resulting semantic map can be used to generate a sparse topological map representation, that can be utilized by robots for global navigation. The proposed solution also introduces a built-in filtering to handle noises in the environment, to remove openings in the map that the robot cannot pass, and to remove small objects to optimize and simplify the overall mapping results. The efficacy of the proposed semantic and topological mapping method is demonstrated over a map of an indoor structured environment that is built from experimental data. The proposed framework, when compared with similar state-of-the-art topological mapping solutions, is able to produce a map with up to 89% fewer nodes than the next best solution.

Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from Radio Detection and Rangings (RADARs) which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented., Comment: Accepted in the 31st Mediterranean Conference on Control and Automation [MED2023]

Microscopic calculations of neutrino-nucleus scattering cross sections are critical for the success of the neutrino-oscillation program. In addition to retaining nuclear correlations in the initial and final state of the reaction, they are based on consistent nuclear interactions and transition current operators, thereby enabling robust uncertainty quantification. In this work, we address a significant limitation of these microscopic methods, which arises from their nonrelativistic nature. By performing the calculations in a reference frame that minimizes nucleon momenta and utilizing the so-called ``two-fragment'' model, we extend the applicability of Green's function Monte Carlo calculations of neutrino-nucleus scattering to higher momenta than currently possible. To validate this approach, we compare our theoretical predictions against inclusive data measured by the MiniBooNE, T2K, and MINER$\nu$A experiments., Comment: 12 pages, 7 figures

In this article, we propose a novel LiDAR and event camera fusion modality for subterranean (SubT) environments for fast and precise object and human detection in a wide variety of adverse lighting conditions, such as low or no light, high-contrast zones and in the presence of blinding light sources. In the proposed approach, information from the event camera and LiDAR are fused to localize a human or an object-of-interest in a robot's local frame. The local detection is then transformed into the inertial frame and used to set references for a Nonlinear Model Predictive Controller (NMPC) for reactive tracking of humans or objects in SubT environments. The proposed novel fusion uses intensity filtering and K-means clustering on the LiDAR point cloud and frequency filtering and connectivity clustering on the events induced in an event camera by the returning LiDAR beams. The centroids of the clusters in the event camera and LiDAR streams are then paired to localize reflective markers present on safety vests and signs in SubT environments. The efficacy of the proposed scheme has been experimentally validated in a real SubT environment (a mine) with a Pioneer 3AT mobile robot. The experimental results show real-time performance for human detection and the NMPC-based controller allows for reactive tracking of a human or object of interest, even in complete darkness., Comment: Accepted at IFAC World Congress 2023

In this paper, we study the multi-robot task assignment and path-finding problem (MRTAPF), where a number of agents are required to visit all given goal locations while avoiding collisions with each other. We propose a novel two-layer algorithm SA-reCBS that cascades the simulated annealing algorithm and conflict-based search to solve this problem. Compared to other approaches in the field of MRTAPF, the advantage of SA-reCBS is that without requiring a pre-bundle of goals to groups with the same number of groups as the number of robots, it enables a part of agents needed to visit all goals in collision-free paths. We test the algorithm in various simulation instances and compare it with state-of-the-art algorithms. The result shows that SA-reCBS has a better performance with a higher success rate, less computational time, and better objective values.

In this article, we propose a reactive task allocation architecture for a multi-agent system for scenarios where the tasks arrive at random times and are grouped into multiple queues. Two stage tasks are considered where every task has a beginning, an intermediate and a final part, typical in pick-and-drop and inspect-and-report scenarios. A centralized auction-based task allocation system is proposed, where an auction system takes into consideration bids submitted by the agents for individual tasks, current length of the queues and the waiting times of the tasks in the queues to decide on a task allocation strategy. The costs associated with these considerations, along with the constraints of having unique mappings between tasks and agents and constraints on the maximum number of agents that can be assigned to a queue, results in a Linear Integer Program (LIP) that is solved using the SCIP solver. For the scenario where the queue lengths are penalized but not the waiting times, we demonstrate that the auction system allocates tasks in a manner that all the queue lengths become constant, which is termed balancing. For the scenarios where both the costs are considered, we qualitatively analyse the effect of the choice of the relative weights on the resulting task allocation and provide guidelines for the choice of the weights. We present simulation results that illustrate the balanced allocation of tasks and validate the analysis for the trade-off between the costs related to queue lengths and task waiting times., Comment: Submitted to The 22nd World Congress of the International Federation of Automatic Control (IFAC 2023)

This article presents an architecture for multi-agent task allocation and task execution, through the unification of a market-inspired task-auctioning system with Behavior Trees for managing and executing lower level behaviors. We consider the scenario with multi-stage tasks, such as 'pick and place', whose arrival times are not known a priori. In such a scenario, a coordinating architecture is expected to be reactive to newly arrived tasks and the resulting rerouting of agents should be dependent on the stage of completion of their current multi-stage tasks. In the novel architecture proposed in this article, a central auctioning system gathers bids (cost-estimates for completing currently available tasks) from all agents, and solves a combinatorial problem to optimally assign tasks to agents. For every agent, it's participation in the auctioning system and execution of an assigned multi-stage task is managed using behavior trees, which switch among several well-defined behaviors in response to changing scenarios. The auctioning system is run at a fixed rate, allowing for newly added tasks to be incorporated into the auctioning system, which makes the solution reactive and allows for the rerouting of some agents (subject to the states of the behavior trees). We demonstrate that the proposed architecture is especially well-suited for multi-stage tasks, where high costs are incurred when rerouting agents who have completed one or more stages of their current tasks. The scalability analysis of the proposed architecture reveals that it scales well with the number of agents and number of tasks. The proposed framework is experimentally validated in multiple scenarios in a lab environment. A video of a demonstration can be viewed at: https://youtu.be/ZdEkoOOlB2g}., Comment: Submitted to the 7th IEEE Conference on Control Technology and Applications (CCTA) 2023

With the ever growing number of space debris in orbit, the need to prevent further space population is becoming more and more apparent. Refueling, servicing, inspection and deorbiting of spacecraft are some example missions that require precise navigation and docking in space. Having multiple, collaborating robots handling these tasks can greatly increase the efficiency of the mission in terms of time and cost. This article will introduce a modern and efficient control architecture for satellites on collaborative docking missions. The proposed architecture uses a centralized scheme that combines state-of-the-art, ad-hoc implementations of algorithms and techniques to maximize robustness and flexibility. It is based on a Model Predictive Controller (MPC) for which efficient cost function and constraint sets are designed to ensure a safe and accurate docking. A simulation environment is also presented to validate and test the proposed control scheme., Comment: \copyright 2023 Fragiskos Fourlas, Vignesh Kottayam Viswanathan, Sumeet Satpute and George Nikolakopoulos. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND

Autonomous navigation of robots in harsh and GPS denied subterranean (SubT) environments with lack of natural or poor illumination is a challenging task that fosters the development of algorithms for pose estimation and mapping. Inspired by the need for real-life deployment of autonomous robots in such environments, this article presents an experimental comparative study of 3D SLAM algorithms. The study focuses on state-of-the-art Lidar SLAM algorithms with open-source implementation that are i) lidar-only like BLAM, LOAM, A-LOAM, ISC-LOAM and hdl graph slam, or ii) lidar-inertial like LeGO-LOAM, Cartographer, LIO-mapping and LIO-SAM. The evaluation of the methods is performed based on a dataset collected from the Boston Dynamics Spot robot equipped with 3D lidar Velodyne Puck Lite and IMU Vectornav VN-100, during a mission in an underground tunnel. In the evaluation process poses and 3D tunnel reconstructions from SLAM algorithms are compared against each other to find methods with most solid performance in terms of pose accuracy and map quality., Comment: 6 pages, 5 figures, 2 tables, \c{opyright} 2022 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND

In neutrino event generators, for models for neutrino and electron scattering only inclusive cross sections are implemented. When these models are used to describe a semi-inclusive cross section, the event generator attaches the hadron variables based on some assumptions. In this work we compare the nucleon kinematics given by the method used in the GENIE event generator, e.g. in the implementation of the SuSAv2 model, to a fully unfactorized calculation using the relativistic distorted wave impulse approximation (RDWIA). We focus on kinematics relevant to the $e4\nu$ analysis and show that observables obtained with RDWIA differ significantly from those of the approximate method used in GENIE, the latter should be considered unrealistic., Comment: Submitted as part of the proceedings of the 23rd Intl Workshop on Neutrinos from Accelerators (NuFact2022) [C22-07-30]

In recent years, cloud and edge architectures have gained tremendous focus for offloading computationally heavy applications. From machine learning and Internet of Thing (IOT) to industrial procedures and robotics, cloud computing have been used extensively for data processing and storage purposes, thanks to its "infinite" resources. On the other hand, cloud computing is characterized by long time delays due to the long distance between the cloud servers and the machine requesting the resources. In contrast, edge computing provides almost real-time services since edge servers are located significantly closer to the source of data. This capability sets edge computing as an ideal option for real-time applications, like high level control, for resource-constrained platforms. In order to utilize the edge resources, several technologies, with basic ones as containers and orchestrators like Kubernetes, have been developed to provide an environment with many features, based on each application's requirements. In this context, this works presents the implementation and evaluation of a novel edge architecture based on Kubernetes orchestration for controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle (UAV) by enabling Model Predictive Control (MPC)., Comment: 6 pages, 6 figures, conference article, CSCC 2022

With the advent of technologies such as Edge computing, the horizons of remote computational applications have broadened multidimensionally. Autonomous Unmanned Aerial Vehicle (UAV) mission is a vital application to utilize remote computation to catalyze its performance. However, offloading computational complexity to a remote system increases the latency in the system. Though technologies such as 5G networking minimize communication latency, the effects of latency on the control of UAVs are inevitable and may destabilize the system. Hence, it is essential to consider the delays in the system and compensate for them in the control design. Therefore, we propose a novel Edge-based predictive control architecture enabled by 5G networking, PACED-5G (Predictive Autonomous Control using Edge for Drones over 5G). In the proposed control architecture, we have designed a state estimator for estimating the current states based on the available knowledge of the time-varying delays, devised a Model Predictive controller (MPC) for the UAV to track the reference trajectory while avoiding obstacles, and provided an interface to offload the high-level tasks over Edge systems. The proposed architecture is validated in two experimental test cases using a quadrotor UAV.

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations., Comment: to be published