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

Abstract

This article 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.