LARG: A Lightweight Robotic Gripper With 3-D Topology Optimized Adaptive Fingers

• Published in: IEEE/ASME transactions on mechatronics Vol. 27; no. 4; pp. 2026 - 2034
• Main Authors: Sun, Yilun, Liu, Yuqing, Pancheri, Felix, Lueth, Tim C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3-D printing; 3-D topology optimization; Adaptation models; adaptive robotic gripper; compliant mechanism; Design improvements; Design optimization; Electric motors; End effectors; Fingers; Grasping; Grasping (robotics); Grippers; lightweight design; Polyamide resins; Robotics; Robots; Shape; Surface treatment; Topology; Topology optimization
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2022.3170800

Adaptive grasping is an important approach for robotic grippers to handle objects with irregular shapes. Compared to rigid-link-based adaptive grippers, the continuum-structure grippers benefit from their structural compliance and have thus a higher degree of adaptive grasping freedom. Based on this advantage, we have developed a continuum-structure-based double-finger gripper in this article to achieve the adaptive grasping. To improve the design efficiency, a 3-D-topology-optimization-based design method was adopted in this article, which realized the adaptive-grasping function of the robotic finger by introducing an additional spring into the design problem. The proposed robotic gripper was selective-laser-sintered with the material polyamide (PA2200) and was actuated by a linear motor. Experiments were also conducted to evaluate the grasping performance and load capacity of the developed gripper. Results have shown that the gripper could successfully grasp objects of different shapes and materials. In addition, with a total weight of only 180 g, the developed gripper can achieve a maximum grasping payload of 8.8 kg, which is about 49 times of its self-weight. From the methodological point of view, this work has successfully demonstrated the feasibility of optimization-based automatic design of robotic grippers.