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混合定向和定位协作运动生成方案,用于通过循环神经网络合成的多个移动冗余操纵系统
IEEE transactions on cybernetics
|August 6, 2024
概括
本研究介绍了多个移动操纵系统的新型运动生成方案,增强了协作任务的稳定性和安全性. 这种方法将方向和位置协调与实际应用的物理限制相结合.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 分布式多个移动操纵系统需要强大的协调,以实现安全和稳定的协作任务.
- 现有的方案往往忽视了导向协调和物理限制,影响了现实世界的适用性.
研究的目的:
- 为分布式多个移动操纵器系统开发运动生成方案,确保位置和方向协调.
- 将物理限制纳入运动生成过程中,以提高系统的实用性.
- 提高多个移动操纵器执行的协作任务的稳定性和安全性.
主要方法:
- 一个分布式循环神经网络被用来建模方向和位置协调约束.
- 使用二次编程来建立运动生成方案,并结合了合变量.
- 基于分布式线性变异不平等的原始双元神经网络被构建来解决方案并生成运动轨迹.
主要成果:
- 拟议的混合定向和位置协作运动生成方案有效解决了协调问题.
- 与其他方法相比,分布式计算结构显著提高了系统稳定性.
- 整合方向协调和物理边界增加了系统的整体实用性.
结论:
- 开发的运动生成方案为分布式多个移动操纵系统提供了稳定和安全的解决方案.
- 导向协调和物理边界的整合代表了实际机器人应用的重大进步.
- 分布式神经网络方法为复杂的机器人协调任务提供了有效的计算方法.
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