In recent years, intelligent robot arms play an increasingly important role in both industry and people's daily life. The application of intelligent robot arms in the real world (i.e., production lines) remains challenging, where real-time trajectory generation is critical. 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 highly nonlinear and nonconvex problem is computationally expensive to solve, which limits the application of robot arms in the real world. In this paper, for redundant robot arms with complex constraints, we present a motion planning method using iterative convex optimization that can efficiently handle the constraints and generate trajectories in real time. The planner guarantees the continuous 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 .


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

Division / Sponsors: 
Dynamic Systems & Control (DSCD)

Paper Proceedings: https://asmedigitalcollection.asme.org/DSCC/DSCC2020/volume/84270

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