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: 1.26). 12/2008; 56(12):1069-1081. DOI: 10.1016/j.robot.2008.02.003
Source: DBLP


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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Available from: Santiago Garrido
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    • "This is due to the complexity of computing the Generalized Voronoi Diagrams. The proposed method in this paper is a non-holonomic extension of the Voronoi Fast Marching Method (VFM)[6], [7]. This method has strong physics contents: it consists in the propagation of a wave (like that used in Geometrical Optics, Geometrical Acoustics etc.) from the current position of the robot to the goal, using a map of slowness (or refractive indexes, or the inverse of velocities) similar to the repulsive electrical potential of walls and obstacles, and the calculation of the path (or light ray in Geometrical Optics) using the gradient method from the goal to the current position point. "

    Full-text · Dataset · May 2015
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    • "The FM 2 method is very versatile when applied to motion planning problems. It has been successfully applied to many different problems such as autonomous exploration [8], outdoor motion planning [9] or even robot formation motion planning [10]. "
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    ABSTRACT: Rhombic like vehicles are characterized for high maneuverability in cluttered environments. This type of vehicles will be used on remote handling operations of maintenance in the International Thermonuclear Experimental Reactor (ITER). Previous work was done in motion planning using Constrained Delaunay Triangulation for rhombic like vehicles operating in ITER. This paper shows that the integration of Fast Marching Square improves the motion planning methodology, decreasing also the computational effort, which can be applied not only in ITER but also in other complex and cluttered environments. Simulated results are presented comparing the initial and the improved motion planning.
    Full-text · Conference Paper · May 2013
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    • "However, this method has not been proven to work for all possible situations. We have also introduced a method to avoid potential fields local minima for a single vehicle, using the Voronoi Fast Marching method (VFM) and the Fast Marching Squared method (‫ܯܨ‬ ଶ ) [11],[12],[13]. "
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    ABSTRACT: This paper describes a robust algorithm for mobile robot formations based on the Voronoi Fast Marching path planning method. This is based on the propagation of a wave throughout the model of the environment, the wave expanding faster as the wave’s distance from obstacles increases. This method provides smooth and safe trajectories and its computational efficiency allows us to maintain a good response time. The proposed method is based on a local‐minima‐free planner; it is complete and has an O(n) complexity order where n is the number of cells of the map. Simulation results show that the proposed algorithm generates good trajectories.
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