213 research outputs found

    Safety-critical model predictive control with control barrier function for dynamic obstacle avoidance

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    In this paper, a safety critical control scheme for a nonholonomic robot is developed to generate control signals that result in optimal obstacle-free paths through dynamic environments. A barrier function is used to obtain a safety envelope for the robot. We formulate the control synthesis problem as an optimal control problem that enforces control barrier function (CBF) constraints to achieve obstacle avoidance. A nonlinear model predictive control (NMPC) with CBF is studied to guarantee system safety and accomplish optimal performance at a short prediction horizon, which reduces computational burden in real-time NMPC implementation. An obstacle avoidance constraint under the Euclidean norm is also incorporated into NMPC to emphasize the effectiveness of CBF in both point stabilization and trajectory tracking problem of the robot. The performance of the proposed controller achieving both static and dynamic obstacle avoidance is verified using several simulation scenarios.Comment: 6 pages, 6 figures, IFAC World Congress 202

    The Kinematics and Dynamics Motion Analysis of a Spherical Robot

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    Mobile robot application has reach more aspect of life in industry and domestic. One of the mobile robot types is a spherical robot whose components are shielded inside a rigid cell. The spherical robot is an interesting type of robot that combined the concept of a mobile robot and inverted pendulum for inner mechanism. This combination adds to more complex controllerdesignthantheothertypeofmobilerobots.Asidefrom these challenges, the application of a spherical robot is extensive, from being a simple toy, to become an industrial surveillance robot. This paper discusses the mathematical analysis of the kinematics and dynamics motion analysis of a spherical robot. The analysis combines mobile robot and pendulum modeling as the robot motion generated by a pendulum mechanism. This paper is expected to give a complete discussion of the kinematics and dynamics motion analysis of a spherical robot

    Navigation 3D d'un UAV avec évitement d'obstacles à l'aide des fonctions de Lyapunov barrières

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    International audienceWe address the safe-navigation problem for aerial robots in the presence of mobile obstacles. Our approach relies on an original dynamic model defined in a cylindrical-coordinate space. It is assumed that the environment contains moving obstacles, that are encoded as state constraints so that they are embedded in the control design: the controller is constructed so as to generate a force field which, in turn, is derived from a potential with negative gradient in the vicinity of stable equilibria and positive gradient in the vicinity of obstacles. In particular, we combine the so-called Barrier Lyapunov Functions (BLF) method with the backstepping technique to obtain a smooth time-invariant controller. It is guaranteed that the robot reaches its destination from any initial condition in the valid workspace (that is, the environment stripped of the obstacles' safety neighborhoods) while avoiding collisions. Furthermore, the performance of our control approach is illustrated via simulations and experiments on a quadrotor benchmark.Nous abordons le problème de la navigation sécurisée pour des robots aériens en présence d'obstacles mobiles. Notre approche repose sur un modèle dynamique original défini dans un espace de coordonnées cylindriques. Il est supposé que l'environnement contient des obstacles mobiles, qui sont définis en tant que contraintes d'état, de manière à être intégrés dans la conception de la commande : le contrôleur est construit de manière à générer un champ de force qui, à son tour, est dérivé d'un potentiel à gradient négatif au voisinage des équilibres stables et de gradient positif au voisinage des obstacles. En particulier, nous combinons la méthode dite des fonctions de Lyapunov barrières (BLF) avec la technique du backstepping pour obtenir une commande lisse et invariante dans le temps. Il est garanti que le robot atteigne sa destination à partir de n’importe quelle condition initiale dans l’espace de travail valide (c'est-à-dire, l'espace de travail sans les zones de sécurité des obstacles) tout en évitant les collisions. De plus, la performance de notre approche de contrôle est illustrée via des simulations et des expériences sur des quadrotors

    Global Formulation and Control of a Class of Nonholonomic Systems

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    This thesis study motion of a class of non-holonomic systems using geometric mechanics, that provide us an efficient way to formulate and analyze the dynamics and their temporal evolution on the configuration manifold. The kinematics equations of the system, viewed as a rigid body, are constrained by the requirement that the system maintain contact with the surface. They describe the constrained translation of the point of contact on the surface. In this thesis, we have considered three different examples with nonholonomic constraint i-e knife edge or pizza cutter, a circular disk rolling without slipping, and rolling sphere. For each example, the kinematics equations of the system are defined without the use of local coordinates, such that the model is globally defined on the manifold without singularities or ambiguities. Simulation results are included that show effectiveness of the proposed control laws

    An MPC-based Optimal Motion Control Framework for Pendulum-driven Spherical Robots

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    Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot's attitude and motors' current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption.Comment: This paper has been submitted to Control Engineering Practic

    A novel control architecture based on behavior trees for an omni-directional mobile robot

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    Robotic systems are increasingly present in dynamic environments. This paper proposes a hierarchical control structure wherein a behavior tree (BT) is used to improve the flexibility and adaptability of an omni-directional mobile robot for point stabilization. Flexibility and adaptability are crucial at each level of the sense–plan–act loop to implement robust and effective robotic solutions in dynamic environments. The proposed BT combines high-level decision making and continuous execution monitoring while applying non-linear model predictive control (NMPC) for the point stabilization of an omni-directional mobile robot. The proposed control architecture can guide the mobile robot to any configuration within the workspace while satisfying state constraints (e.g., obstacle avoidance) and input constraints (e.g., motor limits). The effectiveness of the controller was validated through a set of realistic simulation scenarios and experiments in a real environment, where an industrial omni-directional mobile robot performed a point stabilization task with obstacle avoidance in a workspace.This work was financed by national funds from the FCT (Foundation for Science and Technology), I.P., through IDMEC under LAETA, project UIDB\50022\2020. The work of Rodrigo Bernardo was supported by the PhD Scholarship BD\6841\2020 from the FCT. This work indirectly received funding from the European Union’s Horizon 2020 programme under StandICT.eu 2026 (Grant Agreement No. 101091933).info:eu-repo/semantics/publishedVersio

    Coordination and navigation of heterogeneous MAV-UGV formations localized by a 'hawk-eye'-like approach under a model predictive control scheme

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    n approach for coordination and control of 3D heterogeneous formations of unmanned aerial and ground vehicles under hawk-eye-like relative localization is presented in this paper. The core of the method lies in the use of visual top-view feedback from flying robots for the stabilization of the entire group in a leader–follower formation. We formulate a novel model predictive control-based methodology for guiding the formation. The method is employed to solve the trajectory planning and control of a virtual leader into a desired target region. In addition, the method is used for keeping the following vehicles in the desired shape of the group. The approach is designed to ensure direct visibility between aerial and ground vehicles, which is crucial for the formation stabilization using the hawk-eye-like approach. The presented system is verified in numerous experiments inspired by search-and-rescue applications, where the formation acts as a searching phalanx. In addition, stability and convergence analyses are provided to explicitly determine the limitations of the method in real-world applications

    Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

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    In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance
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