论文标题
强化学习增强机器人操纵器的控制障碍功能
Reinforcement Learning-Enhanced Control Barrier Functions for Robot Manipulators
论文作者
论文摘要
在本文中，我们使用二次程序（QP）公式介绍了控制屏障功能（CBF），该公式为机器人操纵器臂系统提供避免障碍物。 CBF是一种控制技术，在过去的十年中已经出现和开发，并且在文献中广泛探讨了其数学基础，设置不变性证明以及各种安全关键控制系统的潜在应用。在这项工作中，我们将研究为避免机器人操纵器障碍物的CBF设计，讨论CBF参数的选择，并提出强化学习（RL）方案，以协助寻找最有效的轨迹，以成功避免使用不同尺寸的障碍。然后，我们在用于训练神经网络（NN）模型的一系列方案上创建一个数据集，该模型可在控制方案中使用，以使系统有效地适应不同的障碍场景。计算机模拟（基于MATLAB/SIMULINK）证明了所提出的算法的有效性。
In this paper we present the implementation of a Control Barrier Function (CBF) using a quadratic program (QP) formulation that provides obstacle avoidance for a robotic manipulator arm system. CBF is a control technique that has emerged and developed over the past decade and has been extensively explored in the literature on its mathematical foundations, proof of set invariance and potential applications for a variety of safety-critical control systems. In this work we will look at the design of CBF for the robotic manipulator obstacle avoidance, discuss the selection of the CBF parameters and present a Reinforcement Learning (RL) scheme to assist with finding parameters values that provide the most efficient trajectory to successfully avoid different sized obstacles. We then create a data-set across a range of scenarios used to train a Neural-Network (NN) model that can be used within the control scheme to allow the system to efficiently adapt to different obstacle scenarios. Computer simulations (based on Matlab/Simulink) demonstrate the effectiveness of the proposed algorithm.
