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Expansion of an Incident Field by Means of Vectorial Spherical Harmonics - Analysis in Terms of Active and Reactive Powers
No full textInternational audienc
Adaptive Traffic-Following Scheme for Orderly Distributed Control of Multi-Vehicle Systems
No full textInternational audienceWe present an adaptive control scheme to enable the emergence of order within distributed, autonomous multi-agent systems. Past studies showed that under high-density conditions, order generated from traffic-following behavior reduces travel times, while under low densities, choosing direct paths is more beneficial. In this paper, we leveraged those findings to allow aircraft to independently and dynamically adjust their degree of traffic-following behavior based on the current state of the airspace. This enables aircraft to follow other traffic only when beneficial. Quantitative analyses revealed that dynamic trafficfollowing behavior results in lower aircraft travel times at the cost of minimal levels of additional disorder to the airspace. The sensitivity of these benefits to temporal and spatial horizons was also investigated. Overall, this work highlights the benefits, and potential necessity, of incorporating self-organizing behavior in making distributed, multi-agent autonomous systems scalable.</div
Optimization of unmanned air vehicles trajectories in urban air mobility
No full textWe propose a mixed-integer linear programming model to design optimal trajectories of unmanned aerial vehicles in urban airspaces. The model integrates multiple decision levers, and takes account of the drone dynamics as well as of operational constraints characterizing the addressed problem. Two variants of the model are also presented, to the aim of assessing the impact of the optimization levers and their effect on the overall efficiency. Computational experiments demonstrate the scalability and effectiveness of the proposed approach even for high-density urban air traffic.</div
An Optimal Quantization-based Unscented Kalman Filter on SE(2): Application to Trajectory Tracking
No full textInternational audienceThis paper introduces an Unscented Kalman Filter (UKF) on the Special Euclidean group SE(2) based on optimal quantization, integrating it within the Invariant Linear Quadratic Gaussian (ILQG) regulator to solve trajectory tracking featuring high-angle variations. Traditional trajectory tracking methods like the Linear Quadratic Gaussian (LQG) regulator often face limitations due to linearization, especially under highly nonlinear model, high-angle maneuvers and/or stochastic uncertainties. The proposed approach overcomes these limitations by employing a sigma-point filter, the Unscented Kalman Filter on Lie groups (UKF-LG) enhanced with optimal quantization. This method improves the accuracy of the covariance computation during the filter's prediction step, enabling better estimation performances to provide robust trajectory tracking in scenarios with significant angular deviations. Simulations demonstrate the effectiveness of the proposed Optimal Quantization-based Left-UKF-LG (OQ-Left-UKF-LG) over the IEKF and standard UKF-LG in complex tracking scenarios. The approach offers promising potential for applications requiring precise tracking in dynamic environments, such as autonomous mobile robots or Unmanned Aerial Vehicles (UAVs)
An Automated Airborne Support Tool for Aircraft Emergencies: Selection of Landing Sites and 4D Diversion Trajectories
No full textInternational audienc
Towards systemic participatory prototyping of interactive software
No full textInternational audienceWith several planetary limits being exceeded, the problem of the sustainability of IT systems is becoming ever more urgent. From the outset, participatory design (PD) has encouraged end-users to take control of the design of the IT tools they are to use by fostering co-designing and collaborative prototyping, but we increasingly need to attend to the wider ecological and societal influence of the systems it helps create. Systemic design (SD) has been identified as an approach that makes it possible to address issues of strong sustainability by taking into account several scales in all their complexity. This paper therefore frames the methodological question of how participatory design of software tools and systemic design might be combined in a way that keeps participatory prototyping meaningful while bringing systemic concerns into the discussion. Building on Jones& van Patter's four design levels, we discuss the use of PD tools usually meant for interventions at levels 1-2 for supporting levels 3-4 analyses. We identify two research gaps: (1) sustainability-oriented PD rarely make use of SD tools, and (2) relevant SD representations for PD may be too abstract for end-users immersed in contextual prototyping. An exploratory use case provides an initial probe. Five researchers co-created a prototype of a digital travel diary during a workshop and later revisited the concept analyzing rebound effects and causal loops. The insights from these workshops show a potential to inform the design of future workshops where systemic prompts will be included from the start. We contribute two aspects: (1) a level-based perspective that identifies research gaps with respect to methods and tools, and (2) a first exploration, based on a workshop, offering insights into emerging perspectives. Together, these findings outline next steps for developing systemic participatory prototyping at multiple scales
An Equivariant von Mises-Gaussian distribution on SE(n) for Unscented Kalman Filtering
No full textThe characterization and propagation of uncertainties on the Special Euclidean Lie groups SE(2) and SE(3) are crucial in robotics and state estimation. Applications such as navigation and SLAM require accurate modeling of pose uncertainty involving both position and attitude. Lie groups offer a structured state space that preserves system properties and improves consistency in nonlinear estimation. Kalman filters on Lie groups improve robustness but rely on a Gaussian assumption, which fails for large uncertainties. To overcome these limitations, we used an alternative probability density function, based on a maximum entropy criterion, leading to a filter that we call vMG-UKF-LG. The resulting method is validated on experimental datasets against four benchmark filters and indicates improved accuracy when dealing with complex trajectories and overestimating the process noise.</div
EM Manifold Estimation of GNSS Synchronization Parameters Under Constant Modulus Interference
No full textInternational audienceGlobal Navigation Satellite Systems (GNSS) rely on estimating the signal propagation delay and Doppler shift to a set of visible satellites, which in turn allows to determine the receiver position, velocity and timing. However, the presence of interfering signals degrades the estimation of such synchronization parameters, reason why robust solutions must be accounted for. Considering constant modulus (CM) interferences, which include chirp and continuous wave signals, a recent solution proposed an expectation-maximization (EM) algorithm to estimate both interference and signal parameters, which relies on the von Mises distribution to exploit the interference CM property. In this contribution, we exploit the geometric properties of the CM family using a Riemannian framework, where CM interferences are modeled as a Riemannian manifold. This modeling allows the E-step of the EM algorithm to be replaced by a Riemannian gradient descent over that manifold. Results show that the proposed method improves the estimation performance and reduces the complexity compared to the classical EM approach
Interactive Content Retrieval in Egocentric Videos Based on Vague Semantic Queries
No full textInternational audienceRetrieving specific, often instantaneous, content from hours-long egocentric video footage based on hazily remembered details is challenging. Vision–language models (VLMs) have been employed to enable zero-shot textual-based content retrieval from videos. But, they fall short if the textual query contains ambiguous terms or users fail to specify their queries enough, leading to vague semantic queries. Such queries can refer to several different video moments, not all of which can be relevant, making pinpointing content harder. We investigate the requirements for an egocentric video content retrieval framework that helps users handle vague queries. First, we narrow down vague query formulation factors and limit them to ambiguity and incompleteness. Second, we propose a zero-shot, user-centered video content retrieval framework that leverages a VLM to provide video data and query representations that users can incrementally combine to refine queries. Third, we compare our proposed framework to a baseline video player and analyze user strategies for answering vague video content retrieval scenarios in an experimental study. We report that both frameworks perform similarly, users favor our proposed framework, and, as far as navigation strategies go, users value classic interactions when initiating their search and rely on the abstract semantic video representation to refine their resulting moments
Safe and wind-aware synchronous path planning for a fleet of fixed-wing constant speed aircraft
No full textPath planning for multiple vehicles is a difficult task, but even more so for fixed-wing aircraft. When flying several of them, multiple constraints may apply: constant airspeed, to maintain optimal operational conditions; synchronous arrival to destination, to achieve formation flight; taking into account wind, for limiting deviation from plan, especially for smaller Unmanned Aerial Vehicles. We present a method to solve this problem based on enumerating variations of Dubins paths until a conflict-free solution is found. This provides a simple and parallelizable scheme that can be extended to handle sequencing tasks. We showcase different situations solved in simulation to illustrate the possible applications: transitions between formations, getting into formation and airport arrival sequencing