Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning-猫眼课题宝

DOI：

10.48550/arxiv.2308.12843

发表时间：

2023-08

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Hazim Alzorgan;Abolfazl Razi;A. Moshayedi

本文研究了一种空中机械臂系统的操作，即一种配备有两个自由度的可控臂的无人机（UAV），用于在飞行中执行驱动任务。我们的解决方案是基于采用Q-learning方法来控制手臂尖端的轨迹，也称为末端执行器。更具体地说，我们开发了一个基于碰撞时间（TTC）的运动规划模型，该模型使四旋翼无人机能够在确保机械手可达性的同时绕过障碍物。此外，我们利用基于模型的q -学习模型来独立跟踪和控制机械臂末端执行器的期望轨迹，给定无人机平台的任意基线轨迹。这样的组合可以实现各种驱动任务，例如高空焊接，结构监测和维修，电池更换，排水沟清洁，摩天大楼清洁以及在难以到达和危险环境中的电源线维护，同时保持与飞行控制固件的兼容性。我们基于rl的控制机制产生了一种鲁棒控制策略，可以处理无人机运动中的不确定性，提供了很好的性能。具体来说，我们的方法在平均位移误差（即目标与获得的轨迹点之间的平均距离）方面实现了92%的准确率，使用了15,000集的Q-learning

In this paper, we investigate the operation of an aerial manipulator system, namely an Unmanned Aerial Vehicle (UAV) equipped with a controllable arm with two degrees of freedom to carry out actuation tasks on the fly. Our solution is based on employing a Q-learning method to control the trajectory of the tip of the arm, also called end-effector. More specifically, we develop a motion planning model based on Time To Collision (TTC), which enables a quadrotor UAV to navigate around obstacles while ensuring the manipulator's reachability. Additionally, we utilize a model-based Q-learning model to independently track and control the desired trajectory of the manipulator's end-effector, given an arbitrary baseline trajectory for the UAV platform. Such a combination enables a variety of actuation tasks such as high-altitude welding, structural monitoring and repair, battery replacement, gutter cleaning, skyscrapper cleaning, and power line maintenance in hard-to-reach and risky environments while retaining compatibility with flight control firmware. Our RL-based control mechanism results in a robust control strategy that can handle uncertainties in the motion of the UAV, offering promising performance. Specifically, our method achieves 92% accuracy in terms of average displacement error (i.e. the mean distance between the target and obtained trajectory points) using Q-learning with 15,000 episodes

Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning

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