Advantages of using flexible robot grippers

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http://blog.robotiq.com/advantages-of-using-flexible-robot-grippers-for-industrial-robotics - In keeping with the idea of giving manufacturers the flexibility needed to automate high-mix processes, Robotiq has designed the 2-Finger Adaptive Robot Gripper to provide unmatched versatility in your robotic applications. Its capabilities make the flexible Robot Gripper a perfect fit for high-mix processes such as: light assembly, sorting and kitting, packaging, machine tending, bin picking and part transfer. Visit the product page of our website at: http://robotiq.com/en/products/industrial-robot-grippers/ to get more technical information and see videos on the main features of our Grippers. Download our free eBook to learn how to: Control the Gripper Create a faster cycle time Use the feedback of the Gripper Verify the Gripper contact during a process

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Advantages of using flexible robot grippers

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In this study, we plan a sequence of the base position for dual-arm mobile manipulators performing multiple pick-and-place tasks. By using our proposed method, the robot can selectively use either the right or the left hand to pick up an object and can minimize the sequence size of the base position needed to performed the given task. We first formulate a quadratic programming (QP) problem to solve the inverse kinematics by considering robot collision. We then formulate a region on the base position where a robot can pick up an object placed anywhere in a box. A sequence of base positions is then obtained by combining the branch and bound (BB) and simulated annealing (SA) methods. We present the numerical calculation results to demonstrate the effectiveness of our proposed method.

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This paper proposes a iterative visual recognition system for learning based randomized bin-picking. Since the configuration on randomly stacked objects while executing the current picking trial is just partially different from the configuration while executing the previous picking trial, we consider detecting the poses of objects just by using a part of visual image taken at the current picking trial where it is different from the visual image taken at the previous picking trial. By using this method, we do not need to try to detect the poses of all objects included in the pile at every picking trial. Assuming the 3D vision sensor attached at the wrist of a manipulator, we first explain a method to determine the pose of a 3D vision sensor maximizing the visibility of randomly stacked objects. Then, we explain a method for detecting the poses of randomly stacked objects. Effectiveness of our proposed approach is confirmed by experiments using a dual-arm manipulator where a 3D vision sensor and the two-fingered hand attached at the right and the left wrists, respectively.

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This paper discusses the vision/manipulation technology where a dual-arm manipulator first picks up an object from the pile, then regrasps it from the right hand to the left hand, and finally places it to the fixture. We especially focus on the grasp/manipulation planner for a dual-arm manipulator. Here, our grasp planner is well applied for objects which can be approximated by multiple cylinders. Our pick-and-place planner can quickly find a robot motion with regrasp. We also explain our vision technology to measure the position/orientation of an object. To show the effectiveness of our proposed approach, we show an experimental result of a dual-arm manipulator.

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