GTO-MPC-Based Target Chasing Using a Quadrotor in Cluttered Environments

Autor:	Lei Jiao, Xinyi Wang, Zhihong Peng, Lele Xi, Shupeng Lai, Ben M. Chen
Rok vydání:	2022
Předmět:	Jerk Smoothness Model predictive control Artificial neural network Control and Systems Engineering Control theory Computer science Trajectory Flight safety Electrical and Electronic Engineering Deadlock Track (rail transport)
Zdroj:	IEEE Transactions on Industrial Electronics. 69:6026-6035
ISSN:	1557-9948 0278-0046
DOI:	10.1109/tie.2021.3090700
Popis:	This paper addresses the challenging problem of chasing an escaping target using a quadrotor in cluttered environments. To tackle these challenges, we propose a guided time-optimal model predictive control (GTO-MPC) based practical framework to generate chasing trajectories for the quadrotor. A jerk limited approach is first adopted to find a time-optimal jerk limited trajectory (JLT), an initial reference for the quadrotor to track, without taking into account surrounding obstacles and potential threats. An MPC based replanning framework is then applied to approximate the JLT together with the consideration of other issues such as flight safety, line-of-sight maintenance, and deadlock avoidance. Combined with a neural network, the proposed GTO-MPC framework can efficiently generate chasing trajectories that guarantee flight smoothness and kinodynamic feasibility. Our simulation and actual experimental results show that the proposed technique is highly effective.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::962d1335156af90b66de8f04fb0f32a0 https://doi.org/10.1109/tie.2021.3090700 Zobrazit plný text záznamu