International audience

This paper introduces a novel distributed approach for forming UAV-based multi-hop relay networks by adapting traditional flocking models to create relay chains between remote points. Our method modifies the standard flocking paradigm by incorporating dynamic agent roles, allowing UAVs to self-organize based solely on local state and neighbor information, and integrates networking information such as routing decisions directly into mobility control. A side contribution is the introduction of a Line-of-Sight (LOS) conservation force, which mitigates communication failures due to obstacles and is easily adaptable to the flocking model. The proposed algorithm is evaluated using a joint robotics and network co-simulator that combines realistic multi-rotor physics with ns-3-based network simulations. Simulation results across diverse environments and varying mission complexities demonstrate that our approach effectively maintains connectivity, enhances Quality of Service (QoS), and scales robustly, thereby bridging the gap between robotic control and aerial wireless network design.

In this paper, we propose a theoretical framework for designing a multi-robot formation equipped with Ultra-wideband (UWB) sensors to localize a target robot. In the presence of noisy range measurements, the accuracy of the target robot’s pose estimation is highly dependent on the chosen formation geometry. Different from existing works, we account for the heterogeneous standard deviations of range measurements across different UWB transmitter-receiver pairs. We establish new optimality conditions for formation geometries and conduct a sensitivity analysis of optimal formations under robot positioning errors. In a 2D setting, we derive necessary and sufficient conditions for both optimality and robustness to robot positioning uncertainty. Experimental results confirm the heterogeneous standard deviations of UWB range measurements and validate the target robot’s confidence ellipse model. An experimental comparison of formation geometries, optimized with and without considering heterogeneous noise, emphasizes the importance of accounting for the heterogeneous standard deviations of range measurements. In addition, we experimentally demonstrate that robust formation geometries improve the target robot’s confidence ellipse in the presence of positioning errors.

In autonomous robotics, a significant challenge involves devising robust solutions for Active Collaborative SLAM (AC-SLAM). This process requires multiple robots to cooperatively explore and map an unknown environment by intelligently coordinating their movements and sensor data acquisition. In this article, we present an efficient visual AC-SLAM method using aerial and ground robots for environment exploration and mapping. We propose an efficient frontiers filtering method that takes into account the common IoU map frontiers and reduces the frontiers for each robot. Additionally, we present an approach to guide robots to previously visited goal positions to promote loop closure and reduce SLAM uncertainty. The proposed method is implemented in ROS and evaluated through simulations on publicly available datasets and similar methods, achieving an accumulative average of 59% increase in area coverage.

This work focuses on localizing a single target robot with multi-robot formations in 2D space. The cooperative robots employ inter-robot range measurements to assess the target position. In the presence of noisy measurements, the choice of formation geometries significantly impacts the accuracy of the target robot’s pose estimation. While an infinite number of geometries exists to optimize localization accuracy, the current practice is to choose the final formation geometry based on convenience criteria such as simplicity or proximity to the initial position of the robots. The former leads to the selection of regular polygon-shaped formations, while the latter results in behaviour-based formations. Different from existing works, we conduct a complete robustness study of formation geometries in the presence of deviations from the desired formation and range measurement errors. In 2D scenarios, we establish necessary and sufficient conditions for formation geometries to be robust against robot positioning errors. This result substantiates the extensive use of regular polygon formations. However, our analysis reveals the lack of robustness of the commonly used square formation geometry, which stands as an exception. Simulation results illustrate the advantages of these robust geometries in enhancing target localization accuracy.

The decentralization of formations using onboard sensing is important for multirobot systems, improving the robustness and independence of fleet operations. Bearing measurements (obtainable from embedded cameras) are an attractive choice for use in decentralized formation control, however, this requires that the formation framework be bearing rigid. Rigidity may be checked numerically for a given formation framework, however, it remains difficult to determine the geometric conditions under which otherwise rigid formations become flexible. This article models the sensor and robot constraints in bearing formations of quadrotors as a kinematic mechanism with analogous properties to find geometric conditions for the degeneration of bearing rigidity (singularities) and the resulting uncontrollable motions. A classification of singularities based on graph substructures is developed, and it is shown that arbitrarily large formations may be designed for which all singularities lie within a known set of geometric conditions. An application on how to use the knowledge of all singularity cases in a formation for singularity-free control maintenance is provided.

Formation control of multi-agent systems deals with groups of robots forming specific spatial geometries. Combined with the advancements of unmanned aerial vehicles (UAVs) in the past decade, formation control may potentially be applied to tasks such as search-and-rescue, surveillance, even collaborative manipulation. A key challenge is the decentralization of formation control, where each agent behaves independently using onboard sensors and computation, improving the scaleability and robustness of the system.This paper proposes a decentralized controller based on model predictive control (MPC), for the control of formations of quadrotor UAVs defined by inter-agent bearings. The use of MPC allows the controller to account for attitude kinematics, improving upon the results of existing bearing formation control methods based on rigidity and visual servoing approaches, which typically only consider the quadrotor as a single or double integrator. Furthermore the near-optimality of MPC permits a more optimal use of the quadrotors dynamic capabilities for faster maneuvering. Extensive simulations are performed to demonstrate the improved transient formation convergence and fast maneuvering permitted by this controller. Experiments show that it is indeed a real-time feasible solution for bearing formation control.

Aerial manipulators have great potential in accomplishing a variety of aerial tasks. One class of aerial manipulators, multi-UAV parallel robots, consists of multiple UAVs connected to a payload or an end-effector by passive kinematic chains. The primary limitation of such aerial manipulators is the dependence on motion capture (MOCAP) systems that provide precise and high-rate exteroceptive pose measurements of all bodies in a common inertial frame, but which are impractical in the majority of real applications.This paper proposes a novel methodology of controlling multi-UAV parallel robots, using a Flying Parallel Robot (FPR) as a case study, that could be deployed without a system of external localisation. Intrinsic measurements acquired onboard the UAVs are used to recover a set of robot states that avoid using coordinates derived from a global frame and allow control of the robot by teleoperation. Two decentralized control methods are proposed, based on inter-UAV communicating or non-communicating scenarios. Experiments with intrinsic measurements emulated by MOCAP are carried out to show the performance of the proposed method.

Managing multiple robots into a formation can be beneficial, especially in logistics sectors where multiple robots can work together to transport larger loads. This paper presents a consensus control law for formation with navigation and obstacle avoidance of multiple wheeled mobile robots. The formation control is based on adapting a consensus algorithm from flocking, and we propose an obstacle avoidance methodology that ensures the formation while navigating around obstacles. Simulations of the control law using four wheeled mobile robots as well as experiments using actual industrial robots are shown in order to validate the theory.

Fully distributed localization methods with no central server are relevant for autonomous vehicles that need real-time cooperation. In this paper, mobile vehicles share estimates of GNSS pseudoranges common errors also known as biases. The biases that affect the pseudoranges are mainly due to signal propagation and inaccurate ephemeris data. By describing the measurements models as geometric constraints on intervals, cooperative localization turns into distributed set inversion problem. The solution of this problem is guaranteed to contain the true vehicles positions. We consider vehicles which cooperate and exchange information in order to improve the absolute and relative estimation by fusing pseudoranges corrections shared between them. Results using real measurements are presented to illustrate the performance of the proposed approach in comparison with a standalone method.

We propose a new method, called 3-D image warping-based depth video compression (IW-DVC), for fast and efficient compression of depth images captured by mobile RGB-D sensors. The emergence of low-cost RGB-D sensors has created opportunities to find new solutions for a number of computer vision and networked robotics problems, such as 3-D map building, immersive telepresence, or remote sensing. However, efficient transmission and storage of depth data still presents a challenging task to the research community in these applications. Image/video compression has been comprehensively studied and several methods have already been developed. However, these methods result in unacceptably suboptimal outcomes when applied to the depth images. We have designed the IW-DVC method to exploit the special properties of the depth data to achieve a high compression ratio while preserving the quality of the captured depth images. Our solution combines the egomotion estimation and 3-D image warping techniques and includes a lossless coding scheme that is capable of adapting to depth data with a high dynamic range. IW-DVC operates at a high speed, suitable for real-time applications, and is able to attain an enhanced motion compensation accuracy compared with the conventional approaches. In addition, it removes the existing redundant information between the depth frames to further increase compression efficiency. Our experiments show that IW-DVC attains a very high performance yielding significant compression ratios without sacrificing image quality.

This paper presents a fault tolerant approach for a coaxial octorotor regarding rotor failures. A complete architecture including error detection, fault isolation and system recovery is presented. The diagnosis system is designed with a nonlinear observer to generate residuals and an inference model to evaluate them and isolate the faulty motor. Once the motor failure is diagnosed, a recovery algorithm is applied. It uses the built-in hardware redundancy of the octorotor and compensates the loss of the failing motor by controlling its dual to keep a stable flight that allows the multirotor to continue its mission. This architecture is validated on real flights.

This paper presents a fault tolerant control strategy based on an offline control mixing for an octorotor unmanned aerial vehicle (UAV) regarding several rotor failures. This strategy consists of a set of explicit laws, computed offline, each one dedicated to a fault situation. The corresponding law is selected according to the output of a fault detection and isolation (FDI) module. This module is designed with a non-linear sliding mode observer. The main advantage of this architecture is the deterministic character of the solution, its fast operation and the low computational load. The effectiveness of this approach is illustrated through real experimental application to a coaxial octorotor, where up to four motor failures are considered.

When mobile robots need to cooperate, mutual localization is a key issue. The objective is to enable cooperative localization capabilities, such that each robot determines the partners positions in a common frame with reliable confidence estimates. Exteroceptive sensors can measure distances to known beacons in order to provide absolute information. It often exists biases that affect these measurements because of particular environment conditions or because of an inaccurate knowledge of the beacons positions. In this work, each robot is also equipped with proprioceptive sensors, but no sensor can measure the inter-distance between the robots. The method that we consider is fully distributed between the robots, which share positions and biases estimates. In order to handle the data incest problem, we use constraint propagation techniques on intervals. The distributed cooperative localization method gives sets that always contain the true positions of the robots without any over-convergence. Simulation results show that the so-called method improves localization performance compared to standalone methods.

Camera-based estimation of drivable image areas is still in evolution. These systems have been developed for improved safety and convenience, without the need to adapt itself to the environment. Machine Vision is an important tool to identify the region that includes the road in images. Road detection is the major task of autonomous vehicle guidance. In this way, this work proposes a drivable region detection algorithm that generates the region of interest from a dynamic threshold search method and from a drag process (DP). Applying the DP to estimation of drivable image areas has not been done yet, making the concept unique. Our system was has been evaluated from real data obtained by intelligent platforms and tested in different types of image texture, which include occlusion case, obstacle detection and reactive navigation.

In this paper, the coordination and trajectory tracking control design of multiple mini rotorcraft systems are discussed. The dynamic model of a mini rotorcraft is presented using the Newton-Euler formalism. Our approach is based on a leader/follower structure of multiple robot systems. The centroid of the coordinated control subsystem is used for trajectory tracking purposes. A nonlinear coordinated control design for multiple autonomous vehicle synchronization is developed. The analytic results are supported by simulation tests.

In this paper, we address the hover flight and speed regulation of a quadrotor rotorcraft to perform autonomous navigation. For this purpose, we have developed a vision system which estimates the altitude, the lateral position and the forward speed of the engine during flights. We show that the visual information allows the construction of control strategies for different kinds of flying modes: hover flight, forward flight at constant speed. A hierarchical control strategy is developed and implemented. The local stabilization of the vehicle is proven. Experimental autonomous flight was successfully achieved which validates the visual algorithm and the control law.

Autonomous navigation of an unmanned aerial vehicle (UAV) can be achieved with a reactive system which allows the robot to overcome all the unexpected changes in its environment. In this article, we propose a new approach to avoid frontal obstacles using known properties of the optical flow and by taking advantage of the capability of stationary flight of the rotorcraft. A state machine is proposed as a solution to equip the UAV with all reactions necessary for indoor navigation. We show how smooth transitions can be achieved by decreasing the speed of the vehicle proportional to the distance to an obstacle and by brief instants of hovering flight. Each stage of our algorithm has been tested in a mobile robot.

We aim at developing a vision-based autopilot for autonomous small aerial vehicle applications. This paper presents a new approach for the estimation of optical flow, aircraft motion and scene structure (range map), using monocular vision and inertial data. The proposed algorithm is based on 3 nested Kalman filters (3NKF) and results in an efficient and robust estimation process. The 3NKF-based algorithm was tested extensively in simulation using synthetic images, and in real-time experiments.

This paper presents an original configuration for a small aerial vehicle based on three rotors with fixed-angle propellers. The process of control vectors generation is described, and a detailed mathematical modeling of the aircraft dynamics is presented. We have also proposed a control strategy for full stabilization of the tri-rotor aircraft considering some real constraints specific to small UAVs. The synthesized controller is simple and results in good performance as demonstrated in simulations and in real-time experiments. We have also developed a useful Simulink-based platform for real-time control purposes

This paper presents recent work concerning a small tiltrotor aircraft with a reduced number of rotors. The design consists of two propellers which can tilt laterally and longitudinally. A model of the full birotor dynamics is provided, and a controller based on the backstepping procedure is synthesized for the purposes of stabilization and trajectory tracking. The proposed control strategy has been tested in simulation