一个基于神经网络的加速度水平的关节无漂移的惩罚方案,用于解决冗余机器人操纵器的受限制运动规划问题
Zhijun Zhang1, Xitong Gao1, Jinjia Guo1
1South China University of Technology, Guangzhou, Guangdong, China.
概括
一个新的惩罚神经网络方案可以防止机器人手臂漂移和超越极限. 这种方法可以确保机器人准确安全地返回起始位置.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 冗余机器人操纵器中的重复运动可能导致关节漂移和超越物理极限.
- 传统的固定关节约束缺乏适应性和安全操作的空间.
- 确保机器人恢复到最初的姿势对于许多应用来说至关重要.
研究的目的:
- 开发一种新的方案,用于加速级联动无漂移控制冗余的机器人操纵器.
- 引入时间变化的约束,以防止联合州超越物理界限.
- 为了确保多余的操纵器定期恢复到最初的姿势.
主要方法:
- 设计了一个基于神经网络的加速度级联合漂移无惩罚 (PNN-ALJDF) 方案.
- 一个新的联合加速度时间变化的约束被纳入.
- 一个共同的无漂移标准被开发和制定成一个加速级联合无漂移 (ALJDF) 方案,使用时间变化的二次编程.
- 通过使用设计的惩罚神经网络来解决ALJDF方案.
主要成果:
- 在模拟中,PNN-ALJDF计划有效地防止了联合漂移.
- 所有联合州都保持在定义的加速时间变化的约束范围内.
- 拟议的PNN-ALJDF与基于线性变异不平等的初级-双元神经网络相比,显示出更高的解决方案准确性.
结论:
- 该PNN-ALJDF方案成功地解决了冗余机器人操纵器中的重复运动问题.
- 使用时间变化的约束和共同的无漂移标准可确保机器人的安全和准确的操作.
- 拟议的方法比现有方法提供了更好的准确性和稳定性.
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