협업 로봇을 위한 연속적 검증 환경 및 동적 돌발 대응 기법
Continuous Verification Environment and Dynamic Incident Response Techniques for Collaborative Robots
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Recent advancements in unmanned robot technology have significantly enhanced individual robot performance, expanded their mission capabilities and drawing attention to collaborative potential. Previous research lacks sophisticated environments for handling interrupted missions and complex collaboration scenarios when unexpected tasks arise. Therefore, we propose an advanced mission allocation environment to fully utilize highly intelligent unmanned robots. We also propose a greedy algorithm-based mission allocation and replanning algorithm as a baseline performance metric in this environment. We conducted 1000 experiments for each combination of robot and task quantities to compare the global optimum solution with our proposed greedy algorithm. Our results demonstrate that when dealing with a large number of tasks, the proposed algorithm achieved approximately 90% reduction in computation time with 3.6% to 21% performance gap compared to the optimal solution.
Abstract
Recent advancements in unmanned robot technology have significantly enhanced individual robot performance, expanded their mission capabilities and drawing attention to collaborative potential. Previous research lacks sophisticated environments for handling interrupted missions and complex collaboration scenarios when unexpected tasks arise. Therefore, we propose an advanced mission allocation environment to fully utilize highly intelligent unmanned robots. We also propose a greedy algorithm-based mission allocation and replanning algorithm as a baseline performance metric in this environment. We conducted 1000 experiments for each combination of robot and task quantities to compare the global optimum solution with our proposed greedy algorithm. Our results demonstrate that when dealing with a large number of tasks, the proposed algorithm achieved approximately 90% reduction in computation time with 3.6% to 21% performance gap compared to the optimal solution.