Tool Exchangeable Grasp/Assembly Planner

Abstract

This paper proposes a novel assembly planner for a manipulator which can simultaneously plan assembly sequence, robot motion, grasping configuration, and exchange of grippers. Our assembly planner assumes multiple grippers and can automatically selects a feasible one to assemble a part. For a given AND/OR graph of an assembly task, we consider generating the assembly graph from which assembly motion of a robot can be planned. The edges of the assembly graph are composed of three kinds of paths, i.e., transfer/assembly paths, transit paths and tool exchange paths. In this paper, we first explain the proposed method for planning assembly motion sequence including the function of gripper exchange. Finally, the effectiveness of the proposed method is confirmed through some numerical examples and a physical experiment.

Motion-Data Driven Grasp/Assembly Planner

“…This section shows the overview of the grasp/assembly planner which has proposed in Harada et al [2] and Wan and Harada [3]. Let us consider a product composed of m parts P = (P 1 , • • • , P m ).…”

“…By solving the manipulation graph, we simultaneously plan grasping posture and robot motion. The method is detailed in our previous papers [2,3].…”

“…On the other hand, this research aims to fully automate the product assembly process and proposes a novel robotic grasp/ assembly planner. In conventional grasp/assembly planners [2,3,6,7], we simultaneously planned the grasping posture of a part, the assembly sequence and the motion of a robot. However, conventional grasp/assembly planners can just realize a series of pick-and-place motion of a robot.…”

User Interface and Motion Planner for Task Database

“…The authors have constructed a cloud database [1][2][3][4] that accumulates various information related to motions and shares among robots for motion generation. The database accumulates human work motions and teaching data for robots, work procedure, and object information such as the shape of the object, motion trajectories during work.…”

Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep-Reinforcement-Learning Approach

“…The high accuracy of the predictions is in many cases enough for robot manipulation. Moreover, this second assumption included the specific case of using an assembly planner [32,33], where even if the initial position of the objects is known, the inevitable error throughout the manipulation (e.g. pick-and-place, grasping, and regrasping) that makes the positions/orientations of the manipulated objects uncertain during the insertion phase.…”