Peripheral control enabled by distributed sensing in an octopus-inspired soft robotic arm for autonomous underwater grasping

IF 29.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Emanuela Del Dottore, Romina Adhami, Ebrahim Shahabi, Emanuele Solfiti, Michele Martini, Stefano Mariani, Alberto Parmiggiani, Alessio Mondini, Edoardo Sinibaldi, Barbara Mazzolai
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引用次数: 0

Abstract

Octopuses exhibit remarkable motor dexterity through distributed sensing and control in their flexible arms. Inspired by this biological model, we present a tendon-driven soft robotic arm featuring optoelectronic mechanosensors embedded in suction cups and a hierarchical behaviour-based control architecture. Each artificial sucker integrates light-emitting diodes and phototransistors to detect contact force and direction via light reflection, achieving high sensitivity (~400 mV N−1 in the 0–2-N range) and directional accuracy (error, <18°). The sensors operate reliably in dry and wet environments with minimal drift and hysteresis. Their compact, modular design facilitates system integration. The hierarchical control architecture enables local reflexes at the suction cup level and global coordination for autonomous grasping. The system reliably detects contact events, estimates force and direction and infers object position relative to the arm, enabling purposeful interaction. This work advances sensor-integrated soft robotics, demonstrating the potential of biologically inspired designs for adaptive grasping in unstructured environments. Del Dottore et al. present an octopus-inspired soft robotic arm that uses optoelectronic mechanosensors in its suction cups to detect contact forces and infer object positions.

Abstract Image

一种受章鱼启发的柔性机械臂,通过分布式传感实现自主水下抓取的外围控制
章鱼通过其灵活的手臂的分布式感知和控制表现出非凡的运动灵活性。受这种生物模型的启发,我们提出了一种肌腱驱动的软机械臂,其特征是嵌入在吸盘中的光电机械传感器和基于分层行为的控制体系结构。每个人工吸盘集成了发光二极管和光电晶体管,通过光反射来检测接触力和方向,实现了高灵敏度(0-2-N范围内~400 mV N−1)和方向精度(误差<;18°)。该传感器在干燥和潮湿环境下都能可靠地工作,具有最小的漂移和滞后。其紧凑的模块化设计便于系统集成。分层控制架构使吸盘级的局部反射和自主抓取的全局协调成为可能。该系统可靠地检测接触事件,估计力和方向,并推断物体相对于手臂的位置,从而实现有目的的交互。这项工作推进了集成传感器的软机器人技术,展示了生物启发设计在非结构化环境中自适应抓取的潜力。Del Dottore等人展示了一种受章鱼启发的柔性机械臂,该机械臂在吸盘中使用光电机械传感器来检测接触力并推断物体位置。
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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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