Dynamic Optimization Fabrics for Motion Generation

Dynamic Optimization Fabrics for Motion Generation

Spahn, M. (TU Delft Learning & Autonomous Control)
Wisse, M. (TU Delft Robot Dynamics)
Alonso-Mora, J. (TU Delft Learning & Autonomous Control)

2023

Optimization fabrics are a geometric approach to real-time local motion generation, where motions are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and nonholonomic robots and prove that fundamental properties can be conserved. We show that convergence to desired trajectories and avoidance of moving obstacles can be guaranteed using simple construction rules of the components. In addition, we present the first quantitative comparisons between optimization fabrics and model predictive control and show that optimization fabrics can generate similar trajectories with better scalability, and thus, much higher replanning frequency (up to 500 Hz with a 7 degrees of freedom robotic arm). Finally, we present empirical results on several robots, including a nonholonomic mobile manipulator with 10 degrees of freedom and avoidance of a moving human, supporting the theoretical findings.

Dynamics
Fabrics
Geometric control
mobile manipulation
motion control of manipulators
nonholonomic motion planning
Optimization
Planning
Robot kinematics
Robots
Task analysis

http://resolver.tudelft.nl/uuid:9e1f41b9-d087-492e-8a86-d6abd68a9e7a

https://doi.org/10.1109/TRO.2023.3255587

2023-09-29

1552-3098

IEEE Transactions on Robotics, 39 (4), 2684-2699

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 M. Spahn, M. Wisse, J. Alonso-Mora