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Journal Articles IEEE Robotics and Automation Letters Year : 2022

Efficient Industrial Solution for Robotic Task Sequencing Problem With Mutual Collision Avoidance & Cycle Time Optimization

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Abstract

In the automotive industry, several robots are required to simultaneously carry out welding sequences on the same vehicle. Coordinating and dispatching welding tasks between robots is a manual and difficult phase that needs to be optimized using automatic tools. The cycle time of the cell strongly depends on different robotic factors such as the task allocation among the robots, the configuration solutions and obstacle avoidance. Moreover, a key aspect, often neglected in the state of the art, is to define a strategy to solve the robotic task sequencing with an effective robot-robot avoidance integration. In this paper, we present an efficient iterative algorithm that generates a sequenced high-quality solution for Multi-Robot Task Sequencing Problem. This latter manages not only the robotic factors previously mentioned but also aspects related to accessibility constraints and mutual collision avoidance. In addition, a home-developed planner (RoboTSPlanner) handling 6 axis has been validated in a real case scenario. In order to ensure the completeness of the proposed methodology, we perform an optimization in the task, configuration and coordination space in a synergistic way. Comparing to the existing approaches, both simulation and real experiments reveal positive results in terms of cycle time and show the ability of this method to be interfaced with both industrial simulation software and ROS-I tools.
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Dates and versions

hal-03548468 , version 1 (21-03-2022)

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Hicham Touzani, Nicolas Seguy, Hicham Hadj-Abdelkader, Raul Suarez, Jan Rosell, et al.. Efficient Industrial Solution for Robotic Task Sequencing Problem With Mutual Collision Avoidance & Cycle Time Optimization. IEEE Robotics and Automation Letters, 2022, 7 (2), pp.2597--2604. ⟨10.1109/LRA.2022.3142919⟩. ⟨hal-03548468⟩
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