--In this letter, we present a distributed algorithm for flocking in complex environments that operates at constant altitude, without explicit communication, no a priori information about the environment, and by using only on-board sensing and computation capabilities. We provide sufficient conditions to guarantee that each robot reaches its goal region in a finite time, avoiding collisions with obstacles and other robots without exceeding a desired maximum distance from a predefined set of neighbors (flocking or proximity constraint). The proposed approach allows to operate in crowded scenarios and to deal with tracking errors and on-board sensing errors, without violating safety and proximity constraints. The algorithm was verified through simulations with varying number of UA Vs and also through numerous real-world experiments in a dense forest involving up to four UA Vs. Index T erms--Multi-robot systems, Distributed control, Lloyd-based algorithms. Over the past few decades, numerous applications have emerged for multi-robot systems, exhibiting their undisputed benefits in diverse fields.

--Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects. For example, vision-based object-relative navigation relies on a DL-based 6D object pose predictor to provide the relative pose between the object and the robot as measurements to the robot's state estimator . Accurately knowing the uncertainty inherent in such Deep Neural Network (DNN) based measurements is essential for probabilistic state estimators subsequently guiding the robot's tasks. Thus, in this letter, we show that we can extend any existing DL-based object-relative pose predictor for aleatoric uncertainty inference simply by including two multi-layer perceptrons detached from the translational and rotational part of the DL predictor . This allows for efficient training while freezing the existing pre-trained predictor . We then use the inferred 6D pose and its uncertainty as a measurement and corresponding noise covariance matrix in an extended Kalman filter (EKF). Our approach induces minimal computational overhead such that the state estimator can be deployed on edge devices while benefiting from the dynamically inferred measurement uncertainty. This increases the performance of the object-relative state estimation task compared to a fix-covariance approach. We conduct evaluations on synthetic data and real-world data to underline the benefits of aleatoric uncertainty inference for the object-relative state estimation task. Deep neural networks (DNNs) excel at computer vision tasks such as object detection, classification, and 6D object pose prediction.

The uncertainty quantification of sensor measurements coupled with deep learning networks is crucial for many robotics systems, especially for safety-critical applications such as self-driving cars. This paper develops an uncertainty quantification approach in the context of visual localization for autonomous driving, where locations are selected based on images. Key to our approach is to learn the measurement uncertainty using light-weight sensor error model, which maps both image feature and semantic information to 2-dimensional error distribution. Our approach enables uncertainty estimation conditioned on the specific context of the matched image pair, implicitly capturing other critical, unannotated factors (e.g., city vs highway, dynamic vs static scenes, winter vs summer) in a latent manner. We demonstrate the accuracy of our uncertainty prediction framework using the Ithaca365 dataset, which includes variations in lighting and weather (sunny, night, snowy). Both the uncertainty quantification of the sensor+network is evaluated, along with Bayesian localization filters using unique sensor gating method. Results show that the measurement error does not follow a Gaussian distribution with poor weather and lighting conditions, and is better predicted by our Gaussian Mixture model.

Time series forecasting plays a crucial role in various applications, particularly in healthcare, where accurate predictions of future health trajectories can significantly impact clinical decision-making. Ensuring transparency and explainability of the models responsible for these tasks is essential for their adoption in critical settings. Recent work has explored a top-down approach to bi-level transparency, focusing on understanding trends and properties of predicted time series using static features. In this work, we extend this framework by incorporating exogenous time series features alongside static features in a structured manner, while maintaining cohesive interpretation. Our approach leverages the insights of trajectory comprehension to introduce an encoding mechanism for exogenous time series, where they are decomposed into meaningful trends and properties, enabling the extraction of interpretable patterns. Through experiments on several synthetic datasets, we demonstrate that our approach remains predictive while preserving interpretability and robustness. This work represents a step towards developing robust, and generalized time series forecasting models. The code is available at https://github.com/jeremy-qin/TIMEVIEW

Scan matching is a widely used technique in state estimation. Point-cloud alignment, one of the most popular methods for scan matching, is a weighted least-squares problem in which the weights are determined from the inverse covariance of the measured points. An inaccurate representation of the covariance will affect the weighting of the least-squares problem. For example, if ellipsoidal covariance bounds are used to approximate the curved, "banana-shaped" noise characteristics of many scanning sensors, the weighting in the least-squares problem may be overconfident. Additionally, sensor-to-vehicle extrinsic uncertainty and odometry uncertainty during submap formation are two sources of uncertainty that are often overlooked in scan matching applications, also likely contributing to overconfidence on the scan matching estimate. This paper attempts to address these issues by developing a model for range-azimuth-elevation sensors on matrix Lie groups. The model allows for the seamless incorporation of extrinsic and odometry uncertainty. Illustrative results are shown both for a simulated example and for a real point-cloud submap collected with an underwater laser scanner.

Land cover classification involves the production of land cover maps, which determine the type of land through remote sensing imagery. Over recent years, such classification is being performed by machine learning classification models, which can give highly accurate predictions on land cover per pixel using large quantities of input training data. However, such models do not currently take account of input measurement uncertainty, which is vital for traceability in metrology. In this work we propose a Bayesian classification framework using generative modelling to take account of input measurement uncertainty. We take the specific case of Bayesian quadratic discriminant analysis, and apply it to land cover datasets from Copernicus Sentinel-2 in 2020 and 2021. We benchmark the performance of the model against more popular classification models used in land cover maps such as random forests and neural networks. We find that such Bayesian models are more trustworthy, in the sense that they are more interpretable, explicitly model the input measurement uncertainty, and maintain predictive performance of class probability outputs across datasets of different years and sizes, whilst also being computationally efficient.

Estimating the 6D pose of textureless objects from RBG images is an important problem in robotics. Due to appearance ambiguities, rotational symmetries, and severe occlusions, single-view based 6D pose estimators are still unable to handle a wide range of objects, motivating research towards multi-view pose estimation and next-best-view prediction that addresses these limitations. In this work, we propose a comprehensive active perception framework for estimating the 6D poses of textureless objects using only RGB images. Our approach is built upon a key idea: decoupling the 6D pose estimation into a sequential two-step process can greatly improve both accuracy and efficiency. First, we estimate the 3D translation of each object, resolving scale and depth ambiguities inherent to RGB images. These estimates are then used to simplify the subsequent task of determining the 3D orientation, which we achieve through canonical scale template matching. Building on this formulation, we then introduce an active perception strategy that predicts the next best camera viewpoint to capture an RGB image, effectively reducing object pose uncertainty and enhancing pose accuracy. We evaluate our method on the public ROBI dataset as well as on a transparent object dataset that we created. When evaluated using the same camera viewpoints, our multi-view pose estimation significantly outperforms state-of-the-art approaches. Furthermore, by leveraging our next-best-view strategy, our method achieves high object pose accuracy with substantially fewer viewpoints than heuristic-based policies.

We introduce a Bayesian framework for measuring spatio-temporal couplings (STCs) in ultra-intense lasers that reconceptualizes what constitutes a 'single-shot' measurement. Moving beyond traditional distinctions between single- and multi-shot devices, our approach provides rigorous criteria for determining when measurements can truly resolve individual laser shots rather than statistical averages. This framework shows that single-shot capability is not an intrinsic device property but emerges from the relationship between measurement precision and inherent parameter variability. Implementing this approach with a new measurement device at the ATLAS-3000 petawatt laser, we provide the first quantitative uncertainty bounds on pulse front tilt and curvature. Notably, we observe that our Bayesian method reduces uncertainty by up to 60% compared to traditional approaches. Through this analysis, we reveal how the interplay between measurement precision and intrinsic system variability defines achievable resolution -- insights that have direct implications for applications where precise control of laser-matter interaction is critical.

For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major challenges, especially when dynamic robots must balance competing objectives like formation control and obstacle avoidance in the presence of stochastic dynamics and sensor uncertainty. In this paper, we propose a distributed, multi-agent receding-horizon feedback motion planning approach using Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC) that is able to reason about both model and measurement uncertainty to achieve robust multi-agent formation control while navigating cluttered obstacle fields and avoiding inter-robot collisions. Our approach relies not only on the underlying PAC-NMPC algorithm but also on a terminal cost-function derived from gyroscopic obstacle avoidance. Through numerical simulation, we show that our distributed approach performs on par with a centralized formulation, that it offers improved performance in the case of significant measurement noise, and that it can scale to more complex dynamical systems.

Control barrier function (CBF)-based methods provide the minimum modification necessary to formally guarantee safety in the context of quadratic programming, and strict safety guarantee for safety critical systems. However, most CBF-related derivatives myopically focus on present safety at each time step, a reasoning over a look-ahead horizon is exactly missing. In this paper, a predictive safety matrix is constructed. We then consolidate the safety condition based on the smallest eigenvalue of the proposed safety matrix. A predefined deconfliction strategy of motion paths is embedded into the trajectory tracking module to manage deadlock conflicts, which computes the deadlock escape velocity with the minimum attitude angle. Comparison results show that the introduction of the predictive term is robust for measurement uncertainty and is immune to oscillations. The proposed deadlock avoidance method avoids a large detour, without obvious stagnation.