Exploration of a cluttered environment using Voronoi Transform and Fast Marching.

Robotics Laboratory, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain
Robotics and Autonomous Systems (Impact Factor: 1.16). 01/2008; 56(12):1069-1081. DOI: 10.1016/j.robot.2008.02.003
Source: DBLP

ABSTRACT The Extended Voronoi Transform and the Fast Marching Method combination provide potential maps for robot navigation in previously unexplored dynamic environments. The Extended Voronoi Transform of a binary image of the environment gives a grey scale that is darker near the obstacles and walls and lighter far from them. The Logarithm of the Extended Voronoi Transform imitates the repulsive electric potential from walls and obstacles. The method proposed, called Voronoi Fast Marching method, uses a Fast Marching technique on the Extended Voronoi Transform of the environment’s image, provided by sensors, to determine a motion plan. The computational efficiency of the method lets the planner operate at high rate sensor frequencies. This avoids the need for collision avoidance algorithms. The robot is directed towards the most unexplored and free zones of the environment so as to be able to explore all the workspace. This method is very fast and reliable and the trajectories are similar to the human trajectories: smooth and not very close to obstacles and walls. In this article we propose its application to the task of exploring unknown environments.

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    ROBOT: Robótica Experimental; 01/2012
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    ABSTRACT: This paper presents an interesting technique for �nding the trajectory of an outdoor robot. This technique applies Fast Marching to a 3D surface terrain represented by a triangular mesh in order to calculate a smooth trajectory between two points. The method uses a triangular mesh instead of a square one because this kind of grid adapts better to 3D surfaces. The novelty of this approach is that, in the �rst step of the method, the algorithm calculates a weight matrix W that can represents di�culty, viscosity, refraction index or incertitude based on the informa- tion extracted from the 3D surface characteristics and the sensor data of the robot. Within the bestowed experiments these features are the height, the spherical variance, the gradient of the surface and the incer- titude in the position of other objects or robots and also the incertitude in the map because some portions of the map can't be measured directly by the robot. This matrix is used to limit the propagation speed of the Fast Marching wave in order to �nd the best path depending on the task requirements, e.g., the trajectory with least energy consumption, the fastest path, the most plain terrain or the safest path. The method also gives the robot's maximum admisible speed, which depends on the wave front propagation velocity. The results presented in this paper show that it is possible to model the path characteristics as desired, by varying this matrix W. Moreover, as it is shown in the experimental part, this method is also useful for calculating paths for climbing robots in com- plex purely 3D environments. At the end of the paper, it is shown that this method can also be used for robot avoidance when two robots with opposite trajectories approach each other, knowing each others position.
    Iberian Robotics Conference (ROBOT2013), Madrid, Spain; 11/2013
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    5th IEEE International Conference on Mechatronics (ICM 2009), Malaga, Spain; 04/2009


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May 26, 2014