Impact-Aware Bimanual Catching of Large-Momentum Objects
IEEE Transactions on Robotics(2024)
摘要
This paper investigates one of the most challenging tasks in dynamic
manipulation – catching large-momentum moving objects. Beyond the realm of
quasi-static manipulation, dealing with highly dynamic objects can
significantly improve the robot's capability of interacting with its
surrounding environment. Yet, the inevitable motion mismatch between the fast
moving object and the approaching robot will result in large impulsive forces,
which lead to the unstable contacts and irreversible damage to both the object
and the robot. To address the above problems, we propose an online optimization
framework to: 1) estimate and predict the linear and angular motion of the
object; 2) search and select the optimal contact locations across every surface
of the object to mitigate impact through sequential quadratic programming
(SQP); 3) simultaneously optimize the end-effector motion, stiffness, and
contact force for both robots using multi-mode trajectory optimization (MMTO);
and 4) realise the impact-aware catching motion on the compliant robotic system
based on indirect force controller. We validate the impulse distribution,
contact selection, and impact-aware MMTO algorithms in simulation and
demonstrate the benefits of the proposed framework in real-world experiments
including catching large-momentum moving objects with well-defined motion,
constrained motion and free-flying motion.
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关键词
Bimanual catching,dynamic manipulation,impact,large-momentum object,trajectory optimization
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