Impact-Aware Bimanual Catching of Large-Momentum Objects

Kernkonzepte

Optimizing impact-aware catching of large-momentum objects through online optimization and multi-mode trajectory planning.

Zusammenfassung

This paper investigates the challenging task of catching large-momentum moving objects through impact-aware bimanual manipulation. The proposed framework optimizes contact locations, motion, stiffness, and force profiles to mitigate impact and enable dynamic manipulation tasks. The content is structured into sections focusing on object motion estimation, impact mechanics, contact selection, hybrid motion planning, and control mechanisms.

Statistiken

"Two KUKA-iiwa robots catching a flying large-momentum box that weighs 4.2 kg and travels with speed larger than 3.5 m/s."
"The impulse distribution, contact selection, and impact-aware MMTO algorithms are validated in simulation."
"The proposed framework is experimentally validated on hardware using two KUKA LBR iiwa robots."

Zitate

"To address the above problems, we propose an online optimization framework to estimate and predict the linear and angular motion of the object."
"The aim of this paper is to decrease impulsive forces when making contact to enable impact-aware dynamic manipulation tasks."

Wichtige Erkenntnisse aus

Impact-Aware Bimanual Catching of Large-Momentum Objects

by Lei ... um arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17249.pdf

Tiefere Fragen

How can the proposed framework be adapted for different types of large-momentum objects

The proposed framework for impact-aware catching of large-momentum objects can be adapted for different types of objects by adjusting the parameters and models used in the optimization process. For instance, the shape and size of the object can be taken into account when selecting optimal contact locations and directions. Additionally, the mass and inertia properties of the object can be incorporated into the impact models to better predict and handle the impulses during catching. By customizing the cost functions and constraints in the optimization process, the framework can be tailored to suit the specific characteristics of different types of large-momentum objects, such as varying shapes, weights, and velocities.

What are the potential limitations of the impact-aware catching approach in real-world scenarios

While the impact-aware catching approach proposed in the study shows promise for dynamic manipulation tasks with large-momentum objects, there are potential limitations that may arise in real-world scenarios. One limitation could be the accuracy of the estimation and prediction models used to anticipate the object's motion, as uncertainties or disturbances in the environment could affect the effectiveness of the catching strategy. Additionally, the physical constraints of the robotic system, such as payload capacity and speed limitations, could impact the ability to successfully catch and manipulate certain objects. Moreover, the complexity of real-world scenarios, including unpredictable object behaviors and environmental factors, may pose challenges for the framework to adapt and respond effectively in dynamic situations.

How can the findings of this study be applied to other fields beyond robotics

The findings of this study on impact-aware catching of large-momentum objects in robotics can have applications beyond the field of robotics. The optimization framework and strategies developed for handling impacts and catching fast-moving objects can be valuable in areas such as sports biomechanics, where athletes need to interact with dynamic objects during activities like catching balls or projectiles. Furthermore, the principles of impact analysis and contact selection could be applied in industrial settings for handling heavy or fast-moving objects in manufacturing processes. The insights gained from this research could also be relevant in fields like physics and material science, where understanding and managing impacts are essential for various experiments and applications.