BoundMPC: Cartesian Trajectory Planning with Error Bounds based on Model Predictive Control in the Joint Space

• Main Authors: Oelerich, Thies, Beck, Florian, Hartl-Nesic, Christian, Kugi, Andreas
• Format: Journal Article
• Language: English
• Published: 10-01-2024
• Subjects: Computer Science - Robotics
• DOI: 10.48550/arxiv.2401.05057

This work presents a novel online model-predictive trajectory planner for
robotic manipulators called BoundMPC. This planner allows the collision-free
following of Cartesian reference paths in the end-effector's position and
orientation, including via-points, within desired asymmetric bounds of the
orthogonal path error. The path parameter synchronizes the position and
orientation reference paths. The decomposition of the path error into the
tangential direction, describing the path progress, and the orthogonal
direction, which represents the deviation from the path, is well known for the
position from the path-following control in the literature. This paper extends
this idea to the orientation by utilizing the Lie theory of rotations.
Moreover, the orthogonal error plane is further decomposed into basis
directions to define asymmetric Cartesian error bounds easily. Using piecewise
linear position and orientation reference paths with via-points is
computationally very efficient and allows replanning the pose trajectories
during the robot's motion. This feature makes it possible to use this planner
for dynamically changing environments and varying goals. The flexibility and
performance of BoundMPC are experimentally demonstrated by two scenarios on a
7-DoF Kuka LBR iiwa 14 R820 robot. The first scenario shows the transfer of a
larger object from a start to a goal pose through a confined space where the
object must be tilted. The second scenario deals with grasping an object from a
table where the grasping point changes during the robot's motion, and
collisions with other obstacles in the scene must be avoided.