T
Thierry Siméon
Researcher at University of Toulouse
Publications - 114
Citations - 7290
Thierry Siméon is an academic researcher from University of Toulouse. The author has contributed to research in topics: Motion planning & Mobile robot. The author has an hindex of 45, co-authored 112 publications receiving 6808 citations. Previous affiliations of Thierry Siméon include Hoffmann-La Roche & University of California, Berkeley.
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Journal ArticleDOI
A Human Aware Mobile Robot Motion Planner
TL;DR: It is claimed that a human aware motion planner (HAMP) must not only provide safe robot paths, but also synthesize good, socially acceptable and legible paths to achieve motion and manipulation tasks in the presence or in synergy with humans.
Proceedings ArticleDOI
How may I serve you?: a robot companion approaching a seated person in a helping context
Kerstin Dautenhahn,Michael L. Walters,Sarah Woods,Kheng Lee Koay,Chrystopher L. Nehaniv,A. Sisbot,Rachid Alami,Thierry Siméon +7 more
TL;DR: The combined results of two studies that investigated how a robot should best approach and place itself relative to a seated human subject indicated that most subjects disliked a frontal approach, except for a small minority of females, and most subjects preferred to be approached from either the left or right side.
Journal ArticleDOI
Visibility-based probabilistic roadmaps for motion planning
TL;DR: A variant of probabilistic roadmap methods (PRM) that recently appeared as a promising approach to motion planning is presented, exploiting a free-space structuring of the configuration space into visibility domains in order to produce small roadmaps, called visibility roadmaps.
Journal ArticleDOI
Sampling-Based Path Planning on Configuration-Space Costmaps
TL;DR: The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap using the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states.
Journal ArticleDOI
Manipulation Planning with Probabilistic Roadmaps
TL;DR: A general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches.