AI for Retail: Mobile Manipulation in Dynamic
The AI for Retail (AIR) Lab Delft is a joint TU Delft-Ahold
Delhaize industry lab.
As part of the project, we develop novel method for trajectory
generation for mobile manipulation. In the context of retail
environments, it is especially important to generate motions
that are safe in the proximity of humans. Special focus is on
the combined trajectory generation of the robotic platform and
Prof. Javier Alonso-Mora
This project is funded by Ahold Delhaize.
Ahold Delhaize, TU Delft.
J42 M. Spahn, M. Wisse and J. Alonso-Mora; Dynamic Optimization Fabrics for Motion Generation; Proceedings of IIEEE Transactions on Robotics (T-RO), Mar. 2023
Abstract: 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 de- grees 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
C62 M. Spahn and J. Alonso-Mora,
Autotuning Symbolic Optimization Fabrics for
Trajectory Generation, in IEEE Int. Conf. on
Robotics and Automation (ICRA), May. 2023.
Abstract: In this paper, we
present an automated parameter optimization method for
trajectory generation. We formulate parameter optimization
as a constrained optimization problem that can be
effectively solved using Bayesian optimization. While the
approach is generic to any trajectory generation method,
we showcase it using optimization fabrics. Optimiza- tion
fabrics are a geometric trajectory generation method based
on non-Riemannian geometry. By symbolically pre-solving
the structure of the tree of fabrics, we obtain a
parameterized trajectory generator, called symbolic
fabrics. We show that autotuned symbolic fabrics reach
expert-level performance in a few trials. Additionally, we
show that tuning transfers across different robots, motion
planning problems and between sim- ulation and real world.
Finally, we qualitatively showcase that the framework could
be used for coupled mobile manipulation.
C45 M. Spahn, B. Brito and J. Alonso-Mora, Coupled mobile manipulation via trajectory optimization with free space decomposition, in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), May 2021.
Abstract: This paper presents a real-time method for whole-body trajectory optimization of mobile manipulators in simplified dynamic and unstructured environments. Current trajectory optimization methods typically use decoupling of the mobile base and the robotic arm, which reduces flexibility in motion, does not scale to unstructured environments, and does not consider the future evolution of the environment, which is crucial to avoid dynamic obstacles. Given a goal configuration, such as waypoints generated by a global path planner, we formulate a receding horizon trajectory optimization minimizing the distance-to-target while avoiding collisions with static and dynamic obstacles. The presented method unifies the control of a robotic arm and a non-holonomic base to allow coupled trajectory planning. For collision avoidance, we propose to compute three convex regions englobing the robot's major body parts (i.e., base, shoulder-link and wrist-link) and thus reducing and limiting the number of inequality constraints, regardless of the number of obstacles in the environment. Moreover, our approach incorporates predicted trajectory information to smoothly, and in advance, avoid dynamic obstacles. The presented results show that trajectory optimization for the coupled system can reduce the total execution time by 48% and that applying the convex region generation for individual links allows keeping the computational costs low, even for complex scenarios, enabling onboard implementation.