首页|Reports Summarize Robotics Research from Feng Chia University (Multiobjective op timization of collaborative robotic task sequence assignment problems under coll ision-free constraints)
Reports Summarize Robotics Research from Feng Chia University (Multiobjective op timization of collaborative robotic task sequence assignment problems under coll ision-free constraints)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on robotics have been published. According to news reporting originating from Taichung, Taiwan, by NewsRx correspondents, research stated, “This paper proposes a multiobjective optimization approach to address the challenge of collaborative manufacturing w ith multiple robot arms.” Funders for this research include National Science And Technology Council. Our news journalists obtained a quote from the research from Feng Chia Universit y: “Given the necessity for multiple robot arms to work together, the potential for collisions between robotic motions is a significant concern, and the automat ed task sequence assignment for robots becomes increasingly complex. Previous re search has either simplified the collision-free conditions in a limited working area, or employed a master-slave approach to obtain only a local solution. Conse quently, we propose a unified global optimization approach for simultaneously ad dressing various collaborative manufacturing issues, including robotic task sequ ence assignment (RTSA), multiple inverse kinematics (IK) selection, joint-space collisionfree operations and multiple manufacturing objectives. As the optimal collaborative RTSA problem is a combinatorial optimization problem with non-dete rministic polynomial-time hard (NP-hard) complexity, this paper presents a hybri d nondominated sorting genetic algorithm III (NSGA-III) method that integrates a Hamming-distance-based method and a greedy strategy within NSGA-III to improve population diversity and solution quality. To validate the efficacy of the propo sed approach, simulation experiments were conducted on cooperative manufacturing scenarios, with two objectives: task completion time and task load balancing. T he experimental results demonstrate that the proposed approach is effective in o btaining collision-free Pareto solutions.”
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