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

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

Navigational Guidance – A Deep Learning Approach

The useful navigation guidance is favorable to considerably reducing navigation time. The navigation problems involved with multiple destinations are formulated as the Directed Steiner Tree (DST) problems over directed graphs. In this paper, we propose a deep learning (to be exact, graph neural networks) based approach to tackle the DST problem in a supervised manner. Experiments are conducted to evaluate the proposed approach, and the results suggest that our approach can effectively solve the DST problems. In particular, the accuracy of the network model can reach 95.04% or even higher

Conformal Navigation Transformations with Application to Robot Navigation in Complex Workspaces

Navigation functions provide both path and motion planning, which can be used to ensure obstacle avoidance and convergence in the sphere world. When dealing with complex and realistic scenarios, constructing a transformation to the sphere world is essential and, at the same time, challenging. This work proposes a novel transformation termed the conformal navigation transformation to achieve collision-free navigation of a robot in a workspace populated with obstacles of arbitrary shapes. The properties of the conformal navigation transformation, including uniqueness, invariance of navigation properties, and no angular deformation, are investigated, which contribute to the solution of the robot navigation problem in complex environments. Based on navigation functions and the proposed transformation, feedback controllers are derived for the automatic guidance and motion control of kinematic and dynamic mobile robots. Moreover, an iterative method is proposed to construct the conformal navigation transformation in a multiply-connected workspace, which transforms the multiply-connected problem into multiple simply-connected problems to achieve fast convergence. In addition to the analytic guarantees, simulation studies verify the effectiveness of the proposed methodology in workspaces with non-trivial obstacles

A Hybrid Controller for Obstacle Avoidance in an n-dimensional Euclidean Space

For a vehicle moving in an $n$-dimensional Euclidean space, we present a construction of a hybrid feedback that guarantees both global asymptotic stabilization of a reference position and avoidance of an obstacle corresponding to a bounded spherical region. The proposed hybrid control algorithm switches between two modes of operation: stabilization (motion-to-goal) and avoidance (boundary-following). The geometric construction of the flow and jump sets of the hybrid controller, exploiting a hysteresis region, guarantees robust switching (chattering-free) between the stabilization and avoidance modes. Simulation results illustrate the performance of the proposed hybrid control approach for a 3-dimensional scenario.Comment: 8 pages, 3 figures, conferenc