A recent advance in vector field path following is the introduction of the Parametric Guiding Vector Field method. It allows for singularity-free vector fields with strong convergence guarantees, usable even for self-intersecting paths. However, the method requires significant gain tuning for practical use. In particular, for unbounded paths, the gains will inevitably become ill-suited for efficient path following. We propose a method to overcome this issue by introducing a dynamic step adaptation strategy, which provides additional normalization properties to the field. This allows the following of unbounded curves and reduces the number of gains to tune. The proposed improvements are verified in simulations using the PaparazziUAV software.

This article introduces a bio-inspired 3D flocking algorithm for a drone swarm, built upon a previously established 2D model, which has proven to be effective in promoting stability, alignment, and distance variation between agents within large groups of agents. The study highlights how the incorporation of a vertical interaction between agents and the acquisition by each agent of a minimal amount of information about their most influential neighbor impacts the collective behavior of the swarm. Additionally, we present a comprehensive investigation of the impacts of the intensity of alignment and attraction interactions on the collective motion patterns that emerge at the group level. These results, mostly conducted in a validated simulator, have significant implications for designing efficient UAV swarm systems and using collective patterns, or phases, in operational contexts such as corridor tracking, surveillance, and exploration. Further research will explore the effectiveness and efficiency of this UAV swarm flocking algorithm, as well as its ability to ensure safe transitions between collective phases in different operational contexts.

This work presents the deployment of UAVs for the exploration of clouds, from the system architecture and simulation tests to a real-flight campaign and trajectory analyzes. Thanks to their small size and low altitude, light UAVs have proven to be adapted for in-situ cloud data collection. The short life time of the clouds and limited endurance of the planes require to focus on the area of maximum interest to gather relevant data. Based on previous work on cloud adaptive sampling, the article focuses on the overall system architecture, the improvements made to the system based on preliminary tests and simulations, and finally the results of a field campaign. The Barbados experimental flight campaign confirmed the capacity of the system to map clouds and to collect relevant data in dynamic environment, and highlighted areas for improvement.

This paper presents an algorithm for solving the problem of tracking smooth curves by a fixed wing unmanned aerial vehicle travelling with a constant airspeed and under a constant wind disturbance. The algorithm is based on the idea of following a guiding vector field which is constructed from the implicit function that describes the desired (possibly time-varying) trajectory. The output of the algorithm can be directly expressed in terms of the bank angle of the UAV in order to achieve coordinated turns. Furthermore, the algorithm can be tuned offline such that physical constraints of the UAV, e.g. the maximum bank angle, will not be violated in a neighborhood of the desired trajectory. We provide the corresponding theoretical convergence analysis and performance results from actual flights.

In this paper we propose an algorithm for stabilizing circular formations of fixed-wing UAVs with constant speeds. The algorithm is based on the idea of tracking circles with different radii in order to control the inter-vehicle phases with respect to a target circumference. We prove that the origin of the error dynamics of the inter-vehicle phases is exponentially stable. Furthermore, thanks to the stability properties of the guidance vector field that guides the vehicles, the algorithm can be extended to other closed trajectories. One of the main advantages of this approach is that the algorithm guarantees the confinement of the team in a specific area, even when communications or sensing among vehicles are lost. We show the effectiveness of the algorithm with an actual formation flight of three aircraft. The algorithm is ready to use for the general public in the open-source Paparazzi autopilot.

Bayesian filtering often relies on a reduced system state relating to robot internal variables only. The exogenous variables and their effects on the measurement process are then encompassed within a global observation noise model. Even if Bayes filters proved to be robust to such approximations, special care has to be taken to handle some of these exogenous effects, usually by introducing complex observation distributions or rejection rules. No matter how complex these models are, they often fail in dealing with contextual incidence which can hardly be explicitly encoded. This article shows how contextual information can be introduced within the Bayesian filtering framework by coupling a filter with classification and regression probabilistic models. The classification model provides an efficient context-dependent measurement selection mechanism and is specifically trained with respect to the filter estimation performance. This first component is enhanced by the introduction of context-dependent observation noise provided by the regression model. The performance of this is approach is evaluated and compared with other methods in the context of altitude estimation for a UAV.

The goal of the paper is to perform a cooperation between multiple Unmanned Aerial Vehicles via a mission planner. This mission planner is a generic one which could be used not only for UAV domain but also for other human-machine systems such as maritime, automotive and manned aerial vehicle (MAV). It is a completed mission planner including the high level task planning and low level of collision avoidance. Our first scenario shows the cooperation between UAVs during doing tasks when the number of UAVs is greater than that of tasks. The later scenarios will be the cooperation between two types of UAV: fixed-wing aircraft and rotor-craft for different types of task. This mission planner contributes a new module to both simulation platform and real demonstrator of relevant projects.

The fusion of multi-sensor information for state estimation is a well studied problem in robotics. However, the classical methods may fail to take into account the measurements validity, therefore ruining the benefits of sensor redundancy. This work addresses this problem by learning context-dependent knowledge about sensor reliability. This knowledge is later used as a decision rule in the fusion task in order to dynamically select the most appropriate subset of sensors. For this purpose we use the Mixture of Experts framework. In our application, each expert is a Kalman filter fed by a subset of sensors, and a gating network serves as a mediator between individual filters, basing its decision on sensor inputs and contextual information to reason about the operation context. The performance of this model is evaluated for altitude estimation of a UAV.

In military missions in hostile environments involving teams of UAVs flying in formation, it is important to get the maximum benefits of the auto-protection systems of each aircraft to enhance the global security and efficiency of the team. One way to achieve this is to select a proper configuration for the formation. In this paper, we present an approach to autonomously adapt the configuration of a formation and we focus on its evaluation within a realistic framework where each UAV is simulated independently and communicate through a network.

International audience

This paper presents a decisional architecture and the associated algorithms for multi-UAV (unmanned aerial vehicle) systems. The architecture enables different schemes of decision distribution in the system, depending on the available decision making capabilities of the UAVs and on the operational constraints related to the tasks to achieve. The paper mainly focuses on the deliberative layer of the UAVs: we detail a planning scheme where a symbolic planner relies on refinement tools that exploit UAVs and environment models. Integration effort related to decisional features is highlighted, and preliminary simulation results are provided.