有效载荷(计算)
机器人
变硬
机械臂
Lift(数据挖掘)
机器人末端执行器
爬行
适应性
计算机科学
模拟
刚度
工程类
人工智能
结构工程
医学
计算机网络
生态学
解剖
网络数据包
生物
数据挖掘
作者
Daniel Bruder,Moritz A. Graule,Clark B. Teeple,Robert J. Wood
出处
期刊:Science robotics
[American Association for the Advancement of Science (AAAS)]
日期:2023-08-30
卷期号:8 (81)
被引量:2
标识
DOI:10.1126/scirobotics.adf9001
摘要
Soft robot arms offer safety and adaptability due to their passive compliance, but this compliance typically limits their payload capacity and prevents them from performing many tasks. This paper presents a model-based design approach to effectively increase the payload capacity of soft robot arms. The proposed approach uses localized body stiffening to decrease the compliance at the end effector without sacrificing the robot's range of motion. This approach is validated on both a simulated and a real soft robot arm, where experiments show that increasing the stiffness of localized regions of their bodies reduces the compliance at the end effector and increases the height to which the arm can lift a payload. By increasing the payload capacity of soft robot arms, this approach has the potential to improve their efficacy in a variety of tasks including object manipulation and exploration of cluttered environments.
科研通智能强力驱动
Strongly Powered by AbleSci AI