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.
翻译:在动态不确定的环境中应用智能机器人武器(即弹性生产线)仍然具有挑战性,这需要实时轨迹生成的有效算法。机器人轨迹生成的动作规划问题是高度非线性和非线性和非线性,通常与避免碰撞的限制、机器人运动动力学和动态限制以及任务限制有关(例如,根据在表面界定的笛卡尔的轨迹并保持接触),在动态不确定的环境中应用智能机器人武器。非线性和非链性规划问题在计算上昂贵难以解决,从而限制了机器人手臂在现实世界中的应用。在本文中,对于具有复杂限制的冗余机器人臂臂规划问题,我们提出一种运动规划方法,使用迭代式锥体优化,能够有效处理各种限制,实时产生最佳轨迹。拟议的规划员保证满足接触丰富的任务限制,避免在封闭的环境中发生碰撞。对焊接的轨迹生成进行了广泛的实验,以证明拟议方法的有效性及其在先进机器人制造中的适用性。