This paper proposes a system design for automation in multi-agent-based manufacturing systems to conduct a given complex task automatically by controlling multiple robotic manipulators in a systematic manner. To this end, the proposed system is designed with three-module configurations: environmental perception, task planning, and motion planning. The environmental perception module utilizes a vision sensor to recognize all objects placed on the workspace and extract their unique ID, size, and pose. The task planning module divides a given task into primitive skill levels and distributes each primitive skill to the associated robotic manipulator with the relevant object information in a systematic manner for robotic manipulators not to collide with each other. The motion planning module determines the motion of a robotic arm and a robotic hand by solving inverse kinematics for the robotic arm and by opening or closing two fingers. The proposed system has been tested and verified in real robot environments through a complex task "peg in hole"that requires at least two robotic manipulators.
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J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
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