A constrained instantaneous learning approach for aerial package delivery robots: onboard implementation and experimental results

TY - JOUR

T1 - A constrained instantaneous learning approach for aerial package delivery robots

T2 - onboard implementation and experimental results

AU - Mehndiratta, Mohit

AU - Kayacan, Erdal

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Rather than utilizing a sophisticated robot which is trained—and tuned—for a scenario in a specific environment perfectly, most people are interested in seeing robots operating in various conditions where they have never been trained before. In accordance with the goal of utilizing aerial robots for daily operations in real application scenarios, an aerial robot must learn from its own experience and its interactions with the environment. This paper presents an instantaneous learning-based control approach for the precise trajectory tracking of a 3D-printed aerial robot which can adapt itself to the changing working conditions. Considering the fact that model-based controllers suffer from lack of modeling, parameter variations and disturbances in their working environment, we observe that the presented learning-based control method has a compelling ability to significantly reduce the tracking error under aforementioned uncertainties throughout the operation. Three case scenarios are considered: payload mass variations on an aerial robot for a package delivery problem, ground effect when the aerial robot is hovering/flying close to the ground, and wind-gust disturbances encountered in the outdoor environment. In each case study, parameter variations are learned using nonlinear moving horizon estimation (NMHE) method, and the estimated parameters are fed to the nonlinear model predictive controller (NMPC). Thanks to learning capability of the presented framework, the aerial robot can learn from its own experience, and react promptly—unlike iterative learning control which allows the system to improve tracking accuracy from repetition to repetition—to reduce the tracking error. Additionally, the fast C++ execution of NMPC and NMHE codes facilitates a complete onboard implementation of the proposed framework on a low-cost embedded processor.

AB - Rather than utilizing a sophisticated robot which is trained—and tuned—for a scenario in a specific environment perfectly, most people are interested in seeing robots operating in various conditions where they have never been trained before. In accordance with the goal of utilizing aerial robots for daily operations in real application scenarios, an aerial robot must learn from its own experience and its interactions with the environment. This paper presents an instantaneous learning-based control approach for the precise trajectory tracking of a 3D-printed aerial robot which can adapt itself to the changing working conditions. Considering the fact that model-based controllers suffer from lack of modeling, parameter variations and disturbances in their working environment, we observe that the presented learning-based control method has a compelling ability to significantly reduce the tracking error under aforementioned uncertainties throughout the operation. Three case scenarios are considered: payload mass variations on an aerial robot for a package delivery problem, ground effect when the aerial robot is hovering/flying close to the ground, and wind-gust disturbances encountered in the outdoor environment. In each case study, parameter variations are learned using nonlinear moving horizon estimation (NMHE) method, and the estimated parameters are fed to the nonlinear model predictive controller (NMPC). Thanks to learning capability of the presented framework, the aerial robot can learn from its own experience, and react promptly—unlike iterative learning control which allows the system to improve tracking accuracy from repetition to repetition—to reduce the tracking error. Additionally, the fast C++ execution of NMPC and NMHE codes facilitates a complete onboard implementation of the proposed framework on a low-cost embedded processor.

KW - Ground effect

KW - Instantaneous learning

KW - Learning-based NMPC

KW - NMPC–NMHE framework

KW - Package delivery

KW - Tilt-rotor tricopter

KW - Unmanned aerial vehicle

KW - Wind-gust disturbance

UR - http://www.scopus.com/inward/record.url?scp=85068868548&partnerID=8YFLogxK

U2 - 10.1007/s10514-019-09875-y

DO - 10.1007/s10514-019-09875-y

M3 - Journal article

AN - SCOPUS:85068868548

SN - 0929-5593

VL - 43

SP - 2209

EP - 2228

JO - Autonomous Robots

JF - Autonomous Robots

IS - 8

ER -