AI for Retail: Mobile Manipulation in Dynamic Environments

People

Max Spahn

Prof. Javier Alonso-Mora - Autonomous Multi-Robot Lab (AMR) TU Delft

Funding

This project is funded by Ahold Delhaize.

About the Project

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 the manipulator.

Related Publications

Dynamic Optimization Fabrics for Motion Generation
M. Spahn, M. Wisse, J. Alonso-Mora. In IIEEE Transactions on Robotics (T-RO), 2023.

abstract pdf code

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 theoretical findings.

Autotuning Symbolic Optimization Fabrics for Trajectory Generation
M. Spahn, J. Alonso-Mora. In IEEE Int. Conf. on Robotics and Automation (ICRA), 2023.

abstract pdf code

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.

Coupled mobile manipulation via trajectory optimization with free space decomposition
M. Spahn, B. Brito, J. Alonso-Mora. In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), 2021.

abstract web pdf video

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.