VIP内容
这本开放存取书主要关注机器人的安全控制。控制方案主要基于动态神经网络，它是深度强化学习的一个重要理论分支。为提高机器人系统的安全性能，控制策略包括模型不确定性机器人的自适应跟踪控制、不确定性环境的顺应性控制、动态工作空间的避障控制。这本书关于解决机器手臂安全控制的想法是在工业应用和实验室的研究讨论中构想出来的。这本书中的大部分材料来源于作者在期刊上发表的论文，如IEEE工业电子学报、神经计算等。
本书可以作为机器人系统和人工智能控制器的研究者和设计者的参考书，也可以作为高校本科高年级和研究生的参考书。
成为VIP会员查看完整内容
【138页pdf】AI based Robot Safe Learning and Control
热门内容
最新内容
最新论文
One aspect of the ever-growing need for long term autonomy of multi-robot systems, is ensuring energy sufficiency. In particular, in scenarios where charging facilities are limited, battery-powered robots need to coordinate to share access. In this work we extend previous results by considering robots that carry out a generic mission while sharing a single charging station, while being affected by air drag and wind fields. Our mission-agnostic framework based on control barrier functions (CBFs) ensures energy sufficiency (i.e., maintaining all robots above a certain voltage threshold) and proper coordination (i.e., ensuring mutually exclusive use of the available charging station). Moreover, we investigate the feasibility requirements of the system in relation to individual robots' properties, as well as air drag and wind effects. We show simulation results that demonstrate the effectiveness of the proposed framework.
