反復凸最適化による狭隘環境での接触を伴う軌道生成
Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization
冗長ロボットアームの複雑な制約下での軌道計画を反復凸最適化で解き、狭隘環境での衝突回避と接触維持を両立させてリアルタイムに最適軌道を生成する手法を提案し、溶接研削で有効性を示した。
著者: Weiye Zhao, Suqin He, Chengtao Wen, Changliu Liu
分類: cs.RO
原文アブストラクト
Applying intelligent robot arms in dynamic uncertain environments (i.e., flexible production lines) remains challenging, which requires efficient algorithms for real time trajectory generation. The motion planning problem for robot trajectory generation is highly nonlinear and nonconvex, which usually comes with collision avoidance constraints, robot kinematics and dynamics constraints, and task constraints (e.g., following a Cartesian trajectory defined on a surface and maintain the contact). The nonlinear and nonconvex planning problem is computationally expensive to solve, which limits the application of robot arms in the real world. In this paper, for redundant robot arm planning problems with complex constraints, we present a motion planning method using iterative convex optimization that can efficiently handle the constraints and generate optimal trajectories in real time. The proposed planner guarantees the satisfaction of the contact-rich task constraints and avoids collision in confined environments. Extensive experiments on trajectory generation for weld grinding are performed to demonstrate the effectiveness of the proposed method and its applicability in advanced robotic manufacturing.
