Abstract
This article presents a tracking control approach with obstacle avoidance for a mobile robot. The control law is composed of two parts. The first is a discrete-time model predictive method-based trajectory tracking control law that is derived using an optimal quadratic algorithm. The second part is the obstacle avoidance strategies that switch according to two different designed obstacle avoidance regions. The controllability of the avoidance control law is analyzed. The simulation results validate the effectiveness of the proposed control law considering both trajectory tracking and obstacle avoidance.
Highlights
The motion control of robots has been an important research topic that has many practical applications in various industries
We proposed a new collision avoidance strategy for predictive trajectory tracking control
The linear velocity selection in the process of obstacle avoidance is obtained by the algorithm of model predictive control (MPC) trajectory tracking and calculated in real time according to the location information in the whole motion control process
Summary
The motion control of robots has been an important research topic that has many practical applications in various industries. The motion problem of the nonholonomic mobile robots can be analyzed with respect to in aspects as point stabilization,[2] trajectory tracking,[3,4] and path following.[5,6]. Considering the practical implementation, the discrete MPC is adopted for trajectory tracking problems in this article, and the sampling-time instants are deterministically periodic. The main contributions of this work are to use the discrete-time system model based on MPC to achieve the trajectory tracking control and to avoid obstacle collisions. We consider static obstacle ranges as the control excitation condition that divides avoidance region into two parts, named as conservative and regulated region, respectively The former is the essential setup for avoiding obstacles control and the latter can ensure obstacle avoidance and tracking performance of mobile robots simultaneously. The performance of simulation experiment is presented in the fourth section, and conclusions are given at the end
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