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The development of “large display, high performance and low cost” in the FPD industry demands glass substrates to be “larger and thinner”. Therefore, the requirements of handling robots are developing in the direction of large scale, high speed, and high precision. This paper presents a novel construction of a glass substrate handling robot, which has a 2.5 m/s travelling speed. It innovatively adopts bionic end-suction technology to grasp the glass substrate more firmly. The structure design is divided into the following three parts: a travel track, a robot body, and an end-effector. The manipulator can be smoothly and rapidly extended by adjusting the transmission ratio of the reducer to 1:2:1, using only one motor to drive two sections of the arm. This robot can transfer two pieces of glass substrate at one time, and improves the working efficiency. The kinematic and dynamic models of the robot are built based on the DH coordinate. Through the positioning accuracy experiment and vibration experiment of the end-effector, it is found that the robot has high precision during handling. The robots developed in this study can be used in large-scale glass substrate handling.

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Many heat transfer tubes are distributed on the tube plates of a steam generator that requires periodic inspection by robots. Existing inspection robots are usually involved in issues: Robots with manipulators need complicated installation due to their fixed base; tube mobile robots suffer from low running efficiency because of their structural restricts. Since there are thousands of tubes to be checked, task planning is essential to guarantee the precise, orderly, and efficient inspection process. Most in-service robots check the task tubes using row-by-row and column-by-column planning. This leads to unnecessary inspections, resulting in a long shutdown and affecting the regular operation of a nuclear power plant. Therefore, this paper introduces the structure and control system of a dexterous robot and proposes a task planning method. This method proceeds into three steps: task allocation, base position search, and sequence planning. To allocate the task regions, this method calculates the tool work matrix and proposes a criterion to evaluate a sub-region. And then all tasks contained in the sub-region are considered globally to search the base positions. Lastly, we apply an improved ant colony algorithm for base sequence planning and determine the inspection orders according to the planned path. We validated the optimized algorithm by conducting task planning experiments using our robot on a tube sheet. The results show that the proposed method can accomplish full task coverage with few repetitive or redundant inspections and it increases the efficiency by 33.31% compared to the traditional planning algorithms.