A novel path planning approach for smart cargo ships based on anisotropic fast marching

Path planning is an essential tool for smart cargo ships that navigate in coastal waters, inland waters or other crowded waters. These ships require expert and intelligent systems to plan safe paths in order to avoid collision with both static and dynamic obstacles. This research proposes a novel path planning approach based on the anisotropic fast marching (FM) method to specifically assist with safe operations in complex marine navigation environments. A repulsive force field is specially produced to describe the safe area distribution surrounding obstacles based on the knowledge of human. In addition, a joint potential field is created to evaluate the travel cost and a gradient descent method is used to search for appropriate paths from the start point to the end point. Meanwhile, the approach can be used to constantly optimize the paths with the help of the expert knowledge in collision avoidance. Particularly, the approach is validated and evaluated through simulations. The obtained results show that it is capable of providing a reasonable and smooth path in a crowded waters. Moreover, the ability of this approach exhibits a significant contribution to the development of expert and intelligent systems in autonomous collision avoidance

Multiple Contour Finding and Perceptual Grouping as a set of Energy Minimizing Paths

We address the problem of finding a set of contour curves in an image

Minimal paths and deformable models for image analysis

We present an overview of part of our work on minimal paths. Introduced first in order to find the global minimum of active contours' energy using Fast Marching [18], we have then used minimal paths for finding multiple contours for contour completion from points, curves or regions in 2D or 3D images. Some variations allow to decrease computation time, make easier initialization and centering a path in a tubular structure. Fast Marching is also an efficient way to solve balloon model evolution using level sets. We show applications like for road and vessel segmentation and for virtual endoscopy.Nous présentons une synthèse d'une partie de nos travaux sur les chemins minimaux. Introduits au départ pour trouver le minimum global de l'énergie pour les contours actifs à l'aide du Fast Marching [18], nous les avons utilisés par la suite pour la recherche de contours multiples pour compléter des points, des courbes ou des régions dans des images 2D et 3D. Plusieurs variantes permettent d'améliorer le temps de calcul, de simplifier l'initialisation ou de centrer le chemin dans une structure tubulaire. Le Fast Marching est aussi un moyen efficace de résoudre l'évolution d'un modèle de contour actif ballon par "level sets". Nous montrons des applications notamment pour la segmentation de routes et vaisseaux et pour l'endoscopie virtuelle

A novel path planning approach for smart cargo ships based on anisotropic fast marching

Path planning is an essential tool for smart cargo ships that navigate in coastal waters, inland waters or other crowded waters. These ships require expert and intelligent systems to plan safe paths in order to avoid collision with both static and dynamic obstacles. This research proposes a novel path planning approach based on the anisotropic fast marching (FM) method to specifically assist with safe operations in complex marine navigation environments. A repulsive force field is specially produced to describe the safe area distribution surrounding obstacles based on the knowledge of human. In addition, a joint potential field is created to evaluate the travel cost and a gradient descent method is used to search for appropriate paths from the start point to the end point. Meanwhile, the approach can be used to constantly optimize the paths with the help of the expert knowledge in collision avoidance. Particularly, the approach is validated and evaluated through simulations. The obtained results show that it is capable of providing a reasonable and smooth path in a crowded waters. Moreover, the ability of this approach exhibits a significant contribution to the development of expert and intelligent systems in autonomous collision avoidance