Industrial use of automated guided vehicles (AGVs) requires execution of mobile tasks in populated work areas. How these robots can arrive at their goals without collision with potential obstacles and how the knowledge acquired about obstacles in earlier movements may be used in later navigation are the key issues in this paper. A collision avoidance algorithm integrating use of proximity sensors is developed which can be adapted to any two-dimensional work area. The algorithm also involves construction and continual update of an obstacle matrix using information acquiring during earlier movements. Once the obstacle matrix for the work area is identified following navigation can be executed with ease using an off-line collision avoidance algorithm. The algorithm developed here is an attempt to combine the merits of both on-line and off-line collision avoidance strategies. Before commanding the AGV to move, the algorithm will compute an off-line collision-free trajectory based on current knowledge regarding obstacles in the work area. Since knowledge of the current rate of obstacle topology may be incomplete, it is entirely possible that the AGV may detect a potential candidate for collision while travelling along its precomputed trajectory. Should such a situation occur, the AGV will immediately invoke an on-line strategy which will enable the AGV to avoid collision. Upon successfully avoiding the immediate candidate for collision, the AGV will again recompute an off-line path and the process will continue. A stationary robot is used to test the feasibility of the on-line component of the algorithm by guiding its end effector through an obstacle maze. The off-line algorithm Jim has been simulated on a 80386-based personal computer. Examples are included to demonstrate the capability of the algorithm.


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    Title :

    A real time collision avoidance algorithm


    Additional title:

    Ein Echtzeit-Algorithmus zur Kollisionsverhinderung


    Contributors:

    Published in:

    Publication date :

    2000


    Size :

    19 Seiten, 11 Bilder, 3 Tabellen, 21 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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