A 6-DOF robot arm uses a deep Q-network with a solar-objectness loss to track the sun, reporting 81% training and 58% real-world success, but the method and evidence are under-specified.
Adaptive Robot Detumbling of a Non-Rigid Satellite
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
The challenge of satellite stabilization, particularly those with uncertain flexible dynamics, has become a pressing concern in control and robotics. These uncertainties, especially the dynamics of a third-party client satellite, significantly complicate the stabilization task. This paper introduces a novel adaptive detumbling method to handle non-rigid satellites with unknown motion dynamics (translation and rotation). The distinctive feature of our approach is that we model the non-rigid tumbling satellite as a two-link serial chain with unknown stiffness and damping in contrast to previous detumbling research works which consider the satellite a rigid body. We develop a novel adaptive robotics approach to detumble the satellite by using two space tugs as servicer despite the uncertain dynamics in the post-capture case. Notably, the stiffness properties and other physical parameters, including the mass and inertia of the two links, remain unknown to the servicer. Our proposed method addresses the challenges in detumbling tasks and paves the way for advanced manipulation of non-rigid satellites with uncertain dynamics.
fields
cs.RO 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
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Maximum Solar Energy Tracking Leverage High-DoF Robotics System with Deep Reinforcement Learning
A 6-DOF robot arm uses a deep Q-network with a solar-objectness loss to track the sun, reporting 81% training and 58% real-world success, but the method and evidence are under-specified.