论文标题

使用迭代凸优化在限制环境中富含接触的轨迹生成

Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization

论文作者

Zhao, Weiye, He, Suqin, Wen, Chengtao, Liu, Changliu

论文摘要

在动态不确定的环境(即灵活的生产线)中应用智能机器人臂仍然具有挑战性,这需要有效的实时轨迹生成算法。机器人轨迹产生的运动计划问题是高度非线性和非凸,通常伴随着避免碰撞约束,机器人运动学和动力学约束以及任务约束(例如,遵循在表面上定义的笛卡尔轨迹并维护联系人)。非线性和非凸准计划问题在计算上的求解价格很高,这限制了机器人在现实世界中的应用。在本文中,对于具有复杂约束的冗余机器人臂规划问题,我们使用迭代凸优化提出了一种运动计划方法,该方法可以有效地处理约束并实时生成最佳轨迹。拟议的计划者保证了接触量丰富的任务限制的满意度,并避免在狭窄的环境中发生碰撞。进行了有关焊接研磨轨迹产生的广泛实验,以证明该方法的有效性及其在晚期机器人制造中的适用性。

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.

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