  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.
