VinePT-Map: Pole-Trunk Semantic Mapping for Resilient Autonomous Robotics in Vineyards

Reliable long-term deployment of autonomous robots in agricultural environments remains challenging due to perceptual aliasing, seasonal variability, and the dynamic nature of crop canopies. Vineyards, characterized by repetitive row structures and significant visual changes across phenological stages, represent a pivotal field challenge, limiting the robustness of conventional feature-based localization and mapping approaches. This paper introduces VinePT-Map, a semantic mapping framework that leverages vine trunks and support poles as persistent structural landmarks to enable season-agnostic and resilient robot localization. The proposed method formulates the mapping problem as a factor graph, integrating GPS, IMU, and RGB-D observations through robust geometrical constraints that exploit vineyard structure. An efficient perception pipeline based on instance segmentation and tracking, combined with a clustering filter for outlier rejection and pose refinement, enables accurate landmark detection using low-cost sensors and onboard computation. To validate the pipeline, we present a multi-season dataset for trunk and pole segmentation and tracking. Extensive field experiments conducted across diverse seasons demonstrate the robustness and accuracy of the proposed approach, highlighting its suitability for long-term autonomous operation in agricultural environments.

Hybrid Model Predictive Control with Physics-Informed Neural Network for Satellite Attitude Control

Reliable spacecraft attitude control depends on accurate prediction of attitude dynamics, particularly when model-based strategies such as Model Predictive Control (MPC) are employed, where performance is limited by the quality of the internal system model. For spacecraft with complex dynamics, obtaining accurate physics-based models can be difficult, time-consuming, or computationally heavy. Learning-based system identification presents a compelling alternative; however, models trained exclusively on data frequently exhibit fragile stability properties and limited extrapolation capability. This work explores Physics-Informed Neural Networks (PINNs) for modeling spacecraft attitude dynamics and contrasts it with a conventional data-driven approach. A comprehensive dataset is generated using high-fidelity numerical simulations, and two learning methodologies are investigated: a purely data-driven pipeline and a physics-regularized approach that incorporates prior knowledge into the optimization process. The results indicate that embedding physical constraints during training leads to substantial improvements in predictive reliability, achieving a 68.17% decrease in mean relative error relative. When deployed within an MPC architecture, the physics-informed models yield superior closed-loop tracking performance and improved robustness to uncertainty. Furthermore, a hybrid control formulation that merges the learned nonlinear dynamics with a nominal linear model enables consistent steady-state convergence and significantly faster response, reducing settling times by 61.52%-76.42% under measurement noise and reaction wheel friction.

Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning

Localization in agricultural environments is challenging due to their unstructured nature and lack of distinctive landmarks. Although agricultural settings have been studied in the context of object classification and segmentation, the place recognition task for mobile robots is not trivial in the current state of the art. In this study, we propose MinkUNeXt-VINE, a lightweight, deep-learning-based method that surpasses state-of-the-art methods in vineyard environments thanks to its pre-processing and Matryoshka Representation Learning multi-loss approach. Our method prioritizes enhanced performance with low-cost, sparse LiDAR inputs and lower-dimensionality outputs to ensure high efficiency in real-time scenarios. Additionally, we present a comprehensive ablation study of the results on various evaluation cases and two extensive long-term vineyard datasets employing different LiDAR sensors. The results demonstrate the efficiency of the trade-off output produced by this approach, as well as its robust performance on low-cost and low-resolution input data. The code is publicly available for reproduction.

Learning Satellite Attitude Dynamics with Physics-Informed Normalising Flow

Attitude control is a fundamental aspect of spacecraft operations. Model Predictive Control (MPC) has emerged as a powerful strategy for these tasks, relying on accurate models of the system dynamics to optimize control actions over a prediction horizon. In scenarios where physics models are incomplete, difficult to derive, or computationally expensive, machine learning offers a flexible alternative by learning the system behavior directly from data. However, purely data-driven models often struggle with generalization and stability, especially when applied to inputs outside their training domain. To address these limitations, we investigate the benefits of incorporating Physics-Informed Neural Networks (PINNs) into the learning of spacecraft attitude dynamics, comparing their performance with that of purely data-driven approaches. Using a Real-valued Non-Volume Preserving (Real NVP) neural network architecture with a self-attention mechanism, we trained several models on simulated data generated with the Basilisk simulator. Two training strategies were considered: a purely data-driven baseline and a physics-informed variant to improve robustness and stability. Our results demonstrate that the inclusion of physics-based information significantly enhances the performance in terms of the mean relative error of the best architectures found by 27.08%. These advantages are particularly evident when the learned models are integrated into an MPC framework, where PINN-based models consistently outperform their purely data-driven counterparts in terms of control accuracy and robustness, yielding improvements of up to 42.86% in performance stability error and increased robustness-to-noise.

Autonomous Robotic Pruning in Orchards and Vineyards: a Review

Manual pruning is labor intensive and represents up to 25% of annual labor costs in fruit production, notably in apple orchards and vineyards where operational challenges and cost constraints limit the adoption of large-scale machinery. In response, a growing body of research is investigating compact, flexible robotic platforms capable of precise pruning in varied terrains, particularly where traditional mechanization falls short. This paper reviews recent advances in autonomous robotic pruning for orchards and vineyards, addressing a critical need in precision agriculture. Our review examines literature published between 2014 and 2024, focusing on innovative contributions across key system components. Special attention is given to recent developments in machine vision, perception, plant skeletonization, and control strategies, areas that have experienced significant influence from advancements in artificial intelligence and machine learning. The analysis situates these technological trends within broader agricultural challenges, including rising labor costs, a decline in the number of young farmers, and the diverse pruning requirements of different fruit species such as apple, grapevine, and cherry trees. By comparing various robotic architectures and methodologies, this survey not only highlights the progress made toward autonomous pruning but also identifies critical open challenges and future research directions. The findings underscore the potential of robotic systems to bridge the gap between manual and mechanized operations, paving the way for more efficient, sustainable, and precise agricultural practices.

Adaptive Robot Localization with Ultra-wideband Novelty Detection

Ultra-wideband (UWB) technology has shown remarkable potential as a low-cost general solution for robot localization. However, limitations of the UWB signal for precise positioning arise from the disturbances caused by the environment itself, due to reflectance, multi-path effect, and Non-Line-of-Sight (NLOS) conditions. This problem is emphasized in cluttered indoor spaces where service robotic platforms usually operate. Both model-based and learning-based methods are currently under investigation to precisely predict the UWB error patterns. Despite the great capability in approximating strong non-linearity, learning-based methods often do not consider environmental factors and require data collection and re-training for unseen data distributions, making them not practically feasible on a large scale. The goal of this research is to develop a robust and adaptive UWB localization method for indoor confined spaces. A novelty detection technique is used to recognize outlier conditions from nominal UWB range data with a semi-supervised autoencoder. Then, the obtained novelty scores are combined with an Extended Kalman filter, leveraging a dynamic estimation of covariance and bias error for each range measurement received from the UWB anchors. The resulting solution is a compact, flexible, and robust system which enables the localization system to adapt the trustworthiness of UWB data spatially and temporally in the environment. The extensive experimentation conducted with a real robot in a wide range of testing scenarios demonstrates the advantages and benefits of the proposed solution in indoor cluttered spaces presenting NLoS conditions, reaching an average improvement of almost 60% and greater than 25cm of absolute positioning error.

Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection

Satellites are used for a multitude of applications, including communications, Earth observation, and space science. Neural networks and deep learning-based approaches now represent the state-of-the-art to enhance the performance and efficiency of these tasks. Given that satellites are susceptible to various faults, one critical application of Artificial Intelligence (AI) is fault detection. However, despite the advantages of neural networks, these systems are vulnerable to radiation errors, which can significantly impact their reliability. Ensuring the dependability of these solutions requires extensive testing and validation, particularly using fault injection methods. This study analyses a physics-informed (PI) real-valued non-volume preserving (Real NVP) normalizing flow model for fault detection in space systems, with a focus on resilience to Single-Event Upsets (SEUs). We present a customized fault injection framework in TensorFlow to assess neural network resilience. Fault injections are applied through two primary methods: Layer State injection, targeting internal network components such as weights and biases, and Layer Output injection, which modifies layer outputs across various activations. Fault types include zeros, random values, and bit-flip operations, applied at varying levels and across different network layers. Our findings reveal several critical insights, such as the significance of bit-flip errors in critical bits, that can lead to substantial performance degradation or even system failure. With this work, we aim to exhaustively study the resilience of Real NVP models against errors due to radiation, providing a means to guide the implementation of fault tolerance measures.

Towards Safer Planetary Exploration: A Hybrid Architecture for Terrain Traversability Analysis in Mars Rovers

The field of autonomous navigation for unmanned ground vehicles (UGVs) is in continuous growth and increasing levels of autonomy have been reached in the last few years. However, the task becomes more challenging when the focus is on the exploration of planet surfaces such as Mars. In those situations, UGVs are forced to navigate through unstable and rugged terrains which, inevitably, open the vehicle to more hazards, accidents, and, in extreme cases, complete mission failure. The paper addresses the challenges of autonomous navigation for unmanned ground vehicles in planetary exploration, particularly on Mars, introducing a hybrid architecture for terrain traversability analysis that combines two approaches: appearance-based and geometry-based. The appearance-based method uses semantic segmentation via deep neural networks to classify different terrain types. This is further refined by pixel-level terrain roughness classification obtained from the same RGB image, assigning different costs based on the physical properties of the soil. The geometry-based method complements the appearance-based approach by evaluating the terrain's geometrical features, identifying hazards that may not be detectable by the appearance-based side. The outputs of both methods are combined into a comprehensive hybrid cost map. The proposed architecture was trained on synthetic datasets and developed as a ROS2 application to integrate into broader autonomous navigation systems for harsh environments. Simulations have been performed in Unity, showing the ability of the method to assess online traversability analysis.

Unsupervised Novelty Detection Methods Benchmarking with Wavelet Decomposition

Novelty detection is a critical task in various engineering fields. Numerous approaches to novelty detection rely on supervised or semi-supervised learning, which requires labelled datasets for training. However, acquiring labelled data, when feasible, can be expensive and time-consuming. For these reasons, unsupervised learning is a powerful alternative that allows performing novelty detection without needing labelled samples. In this study, numerous unsupervised machine learning algorithms for novelty detection are compared, highlighting their strengths and weaknesses in the context of vibration sensing. The proposed framework uses a continuous metric, unlike most traditional methods that merely flag anomalous samples without quantifying the degree of anomaly. Moreover, a new dataset is gathered from an actuator vibrating at specific frequencies to benchmark the algorithms and evaluate the framework. Novel conditions are introduced by altering the input wave signal. Our findings offer valuable insights into the adaptability and robustness of unsupervised learning techniques for real-world novelty detection applications.

Learning Social Cost Functions for Human-Aware Path Planning

Achieving social acceptance is one of the main goals of Social Robotic Navigation. Despite this topic has received increasing interest in recent years, most of the research has focused on driving the robotic agent along obstacle-free trajectories, planning around estimates of future human motion to respect personal distances and optimize navigation. However, social interactions in everyday life are also dictated by norms that do not strictly depend on movement, such as when standing at the end of a queue rather than cutting it. In this paper, we propose a novel method to recognize common social scenarios and modify a traditional planner's cost function to adapt to them. This solution enables the robot to carry out different social navigation behaviors that would not arise otherwise, maintaining the robustness of traditional navigation. Our approach allows the robot to learn different social norms with a single learned model, rather than having different modules for each task. As a proof of concept, we consider the tasks of queuing and respect interaction spaces of groups of people talking to one another, but the method can be extended to other human activities that do not involve motion.