论文标题
NMPC-LBF：具有学习屏障功能的非线性MPC，用于在未知环境中分散的多个机器人的安全导航
NMPC-LBF: Nonlinear MPC with Learned Barrier Function for Decentralized Safe Navigation of Multiple Robots in Unknown Environments
论文作者
论文摘要
在本文中，我们基于非线性模型预测控制（NMPC）方法提出了一种分散的控制方法，该方法采用屏障证书在具有静态和/或动态障碍的未知环境中安全导航的多个非独立轮式移动机器人。该方法将学习的屏障功能（LBF）纳入NMPC设计中，以确保安全机器人导航，即防止机器人与其他机器人和障碍物的碰撞。我们将我们提出的控制方法称为NMPC-LBF。由于每个机器人都没有关于障碍物和其他机器人的先验知识，因此我们仅在每个机器人上实时运行的深神经网络（DEEPNN），仅从机器人的凸起和探针测量中学习屏障功能（BF）。深文经过训练，可以学习分离安全和不安全地区的BF。在不同情况下，我们对模拟和实际Turtlebot3汉堡机器人实施了建议的方法。实施结果显示了NMPC-LBF方法在确保机器人安全导航方面的有效性。
In this paper, we present a decentralized control approach based on a Nonlinear Model Predictive Control (NMPC) method that employs barrier certificates for safe navigation of multiple nonholonomic wheeled mobile robots in unknown environments with static and/or dynamic obstacles. This method incorporates a Learned Barrier Function (LBF) into the NMPC design in order to guarantee safe robot navigation, i.e., prevent robot collisions with other robots and the obstacles. We refer to our proposed control approach as NMPC-LBF. Since each robot does not have a priori knowledge about the obstacles and other robots, we use a Deep Neural Network (DeepNN) running in real-time on each robot to learn the Barrier Function (BF) only from the robot's LiDAR and odometry measurements. The DeepNN is trained to learn the BF that separates safe and unsafe regions. We implemented our proposed method on simulated and actual Turtlebot3 Burger robot(s) in different scenarios. The implementation results show the effectiveness of the NMPC-LBF method at ensuring safe navigation of the robots.
