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Double-robot obstacle avoidance path optimization for welding process

  • Received: 15 April 2019 Accepted: 16 May 2019 Published: 19 June 2019
  • For path planning of two welding robots, intelligent robot path optimization with obstacle avoidance is introduced first, where the optimization objective is the shortest time. In the optimization process, grid method is used for modeling. Then, ant colony algorithm is applied as search strategy to realize obstacle avoidance between welding gun and workpiece. For obstacle avoidance of robot joints, the robot is modeled using the sphere and the capsule. Besides, two-level collision detection and geometrical collision avoidance are used to obtain collision free robots' path. At last, an improved particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the proposed strategy could improve the effectiveness of the path planning. It can be used to shorten the teaching time and strengthen offline programming ability.

    Citation: Xuewu Wang, Bin Tang, Xin Zhou, Xingsheng Gu. Double-robot obstacle avoidance path optimization for welding process[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 5697-5708. doi: 10.3934/mbe.2019284

    Related Papers:

  • For path planning of two welding robots, intelligent robot path optimization with obstacle avoidance is introduced first, where the optimization objective is the shortest time. In the optimization process, grid method is used for modeling. Then, ant colony algorithm is applied as search strategy to realize obstacle avoidance between welding gun and workpiece. For obstacle avoidance of robot joints, the robot is modeled using the sphere and the capsule. Besides, two-level collision detection and geometrical collision avoidance are used to obtain collision free robots' path. At last, an improved particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the proposed strategy could improve the effectiveness of the path planning. It can be used to shorten the teaching time and strengthen offline programming ability.


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  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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