Soft Multicopter Control Using Neural Dynamics Identification
Authors
Yitong Deng (Dartmouth College)*; Yaorui Zhang (Dartmouth College); Xingzhe He (University of British Columbia); Shuqi Yang (Dartmouth College); Yunjin Tong (Dartmouth College); Michael Zhang (Dartmouth College); Daniel DiPietro (Dartmouth College); Bo Zhu (Dartmouth College)
Interactive Session
2020-11-16, 11:50 - 12:20 PST | PheedLoop Session
Abstract
We propose a data-driven method to automatically generate feedback controllers for soft multicopters featuring deformable materials, non-conventional geometries, and asymmetric rotor layouts, to deliver compliant deformation and agile locomotion. Our approach coordinates two sub-systems: a physics-inspired network ensemble that simulates the soft drone dynamics and a custom LQR control loop enhanced by a novel online-relinearization scheme to control the neural dynamics. Harnessing the insights from deformation mechanics, we design a decomposed state formulation whose modularity and compactness facilitate the dynamics learning while its measurability readies it for real-world adaptation. Our method is painless to implement, and requires only conventional, low-cost gadgets for fabrication. In a high-fidelity simulation environment, we demonstrate the efficacy of our approach by controlling a variety of customized soft multicopters to perform hovering, target reaching, velocity tracking, and active deformation.