Efficient Model Learning and Adaptive Tracking Control of Magnetic Micro-Robots for Non-Contact Manipulation
arxiv(2024)
摘要
Magnetic microrobots can be navigated by an external magnetic field to
autonomously move within living organisms with complex and unstructured
environments. Potential applications include drug delivery, diagnostics, and
therapeutic interventions. Existing techniques commonly impart magnetic
properties to the target object,or drive the robot to contact and then
manipulate the object, both probably inducing physical damage. This paper
considers a non-contact formulation, where the robot spins to generate a
repulsive field to push the object without physical contact. Under such a
formulation, the main challenge is that the motion model between the input of
the magnetic field and the output velocity of the target object is commonly
unknown and difficult to analyze. To deal with it, this paper proposes a
data-driven-based solution. A neural network is constructed to efficiently
estimate the motion model. Then, an approximate model-based optimal control
scheme is developed to push the object to track a time-varying trajectory,
maintaining the non-contact with distance constraints. Furthermore, a
straightforward planner is introduced to assess the adaptability of non-contact
manipulation in a cluttered unstructured environment. Experimental results are
presented to show the tracking and navigation performance of the proposed
scheme.
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