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Samer Ammoun

dblp:53/11207 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-authorSystems, architecture and hardware · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Energy systems and smart grids · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › mobile robot control
docking control
0.112012
A hybrid control for automatic docking of electric vehicles for recharging · ICRA 2012
Robotics › Motion planning and robot control › robot control
hybrid control
0.112012
A hybrid control for automatic docking of electric vehicles for recharging · ICRA 2012
Robotics › Motion planning and robot control
robot control
0.112012
A hybrid control for automatic docking of electric vehicles for recharging · ICRA 2012
Robotics › Motion planning and robot control › robot control › optimal control
time-optimal control
0.112012
A hybrid control for automatic docking of electric vehicles for recharging · ICRA 2012
Energy systems and smart grids
electric vehicle charging
0.012012
A hybrid control for automatic docking of electric vehicles for recharging · ICRA 2012

Methods — techniques the papers use, named apart from their topics

nonlinear control · 0.3infrared beacon localization · 0.3bang-bang control · 0.3
YearPublicationVenuePosition
2012 A hybrid control for automatic docking of electric vehicles for recharging
abstract
In this paper, we present the architecture of an innovative docking station for electric vehicles recharging and a hybrid control scheme for automatic docking of the vehicles. This work is a part of on-going project concerning the development of a smart charging station for electric vehicles equipped with an automated arm, which connect the vehicle to the charging station, and an infrared beacon system for localizing the automatically maneuvering vehicle in the docking area. The proposed control scheme combines time-optimal (bang-bang) control with continuous time-invariant nonlinear control, which stabilizes the vehicle to a small neighborhood of the docking point. Simulation and experimental results illustrate the effectiveness of the proposed controller.
Plamen Petrov 0001, Clement Boussard, Samer Ammoun, Fawzi Nashashibi
ICRA3
2010 Design of a new GIS for ADAS oriented applications
abstract
In this paper, we will present the design and the implementation of a new generation of maps specially designed for the ADAS-like applications. We will focus on the design of the map, the data structure and the choice of road attributes. We will present also the design of a software that enables our intelligent vehicle to perform as a mapping system for advanced attributes acquisition.
Samer Ammoun, Fawzi Nashashibi, Alexandre Bargeton
Intelligent Vehicles Symposium1
2004 Digitizing and 3D modeling of urban environments and roads using vehicle-borne laser scanner system
abstract
In this paper we present a system for three-dimensional environment modeling. It consists of an instrumented vehicle equipped with a 2D laser range scanner for data mapping, and GPS, INS and odometers for vehicle positioning and attitude information. The advantage of this system is its ability to perform data acquisition during the vehicle navigation; the sensor needed being a basic 2D scanner with opposition to traditional expensive 3D sensors. This system integrates the laser raw range data with the vehicle's internal state estimator and is capable of reconstructing the 3D geometry of the environment by real-time geo-referencing. We propose a high level representation of the urban scene while identifying automatically and in real time some types of existing objects in this environment. Thus, our modeling is articulated around three principal axes: the segmentation, decimation, the 3D reconstruction and visualization. The road is the most important object for us; some road features like the curvature and the width are extracted.
Iyad Abuhadrous, Samer Ammoun, Fawzi Nashashibi, François Goulette, Claude Laurgeau
IROS2