NRI: INT: SMART: Soft Multi-Arm RoboT for Synergistic Collaboration with Humans

NRI：INT：SMART：用于与人类协同协作的软多臂机器人

• 批准号: 2024649
• 负责人: Zhaojian Li
• 金额: $ 149.93万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024649&HistoricalAwards=false
• 关键词: NRI; INT; SMART; Soft; Multi

AbstractThis grant will support research that will contribute novel methodologies related to multi-arm soft robots, advancing its practical use in applications involving close collaboration with humans. Soft robots have great promise in versatile applications involving interactions with humans, such as elder care, collaborative surgery, work/life assistance, and collaborative fruit harvesting. Taking apple-picking as a motivating case study, the goal of this project is to develop a novel soft robot system equipped with multiple soft arms, termed soft multi-arm robot or SMART, and to advance its practical use in applications involving close collaboration with humans. This award supports fundamental research that addresses the major challenges in soft robot design and fabrication, motion planning and control, environment and human perception, and human-robot interaction. The new designs and methodologies will enable safe, efficient, and robust cooperation between multiple soft robot arms and humans. The soft multi-arm robot system can not only improve production efficiency (for example, in assisting fruit harvesting), but also contribute to meeting the nation’s urgent need to take care of the elderly population (for example, in elder care and assisted living). Therefore, results from this research will benefit the U.S. economy and life quality. This research involves several disciplines including soft robotics, control, human-robot interaction, perception and learning, and agriculture automation. The multi-disciplinary approach also facilitates the participation of underrepresented groups in research and positively impacts engineering education.The soft multi-arm robot system is expected to offer dexterity, efficiency, and intrinsic safety, and achieve productive collaboration with humans with an array of exciting potential applications. To achieve this goal, five synergistic research thrusts are pursued to overcome key scientific challenges: 1) designing and fabricating soft multi-arm robots to realize simultaneous actuation and stiffness-turning and enable dexterous manipulation, 2) advancing motion planning and control approaches for these soft robots to achieve robust manipulation in 3D space in the presence of stationary and dynamic obstacles, 3) formalizing trust-based human-robot interaction to realize efficient human-robot collaboration by explicitly accommodating the dynamics of human trust in the soft multi-arm robot policy, 4) developing orchard and human motion perception algorithms to robustly obtain 3D tree and human position/pose information to support the fruit harvesting application, and 5) evaluating the soft multi-arm robot system via extensive lab and field experiments in the context of collaborative apple harvesting. Collectively, advances from these research endeavors are expected to make soft multi-arm robots practically viable, especially for applications involving close collaboration with humans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这笔赠款将支持与多臂软机器人相关的新方法的研究，促进其在涉及与人类密切合作的应用中的实际应用。软体机器人在涉及与人类互动的多种应用中有着巨大的前景，如老年护理、协作手术、工作/生活辅助和协作水果收获。以苹果采摘为激励案例，本项目的目标是开发一种新型的配备多个软臂的软机器人系统，称为软多臂机器人或SMART，并推动其在与人类密切合作的应用中的实际应用。该奖项支持基础研究，以解决软机器人设计和制造、运动规划和控制、环境和人类感知以及人与机器人交互方面的主要挑战。新的设计和方法将使多个软机器人手臂和人类之间实现安全、高效和强大的合作。柔性多臂机器人系统不仅可以提高生产效率(例如，在辅助水果收获方面)，而且还有助于满足国家照顾老年人口的迫切需求(例如，在养老和辅助生活方面)。因此，这项研究的结果将有利于美国的经济和生活质量。本研究涉及软机器人、控制、人机交互、感知与学习、农业自动化等多个学科。这种多学科的方法也促进了未被充分代表的群体参与研究，并对工程教育产生了积极的影响。软多臂机器人系统有望提供灵活、高效和固有的安全性，并实现与人类的富有成效的协作，具有一系列令人兴奋的潜在应用。为实现这一目标，我们进行了五个协同研究，以克服关键的科学挑战：1)设计和制造软多臂机器人，以实现同时驱动和刚度转动并实现灵活操作；2)提出这些软机器人的运动规划和控制方法，以在存在静止和动态障碍物的情况下实现在3D空间中的稳健操作；3)形式化基于信任的人-机器人交互，通过在软多臂机器人策略中显式地适应人类信任的动态来实现高效的人-机器人协作；4)开发果园和人体运动感知算法，以鲁棒地获得3D树和人类位置/姿势信息，以支持水果收获应用，5)以苹果采摘为背景，通过大量的实验室和田间实验对软多臂机器人系统进行了评估。总而言之，这些研究工作取得的进展有望使软多臂机器人在实践中可行，特别是在与人类密切合作的应用中。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Bioinspired, Multifunctional, Active Whisker Sensors for Tactile Sensing of Mobile Robots

用于移动机器人触觉感知的仿生多功能主动晶须传感器

• DOI: 10.1109/lra.2022.3191172
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Yu, Zhiqiang;Guo, Yue;Su, Jiaji;Huang, Qiang;Fukuda, Toshio;Cao, Changyong;Shi, Qing
• 通讯作者: Shi, Qing

O2RNet: Occluder-occludee relational network for robust apple detection in clustered orchard environments

O2RNet：遮挡物-被遮挡物关系网络，用于在集群果园环境中进行稳健的苹果检测

• DOI: 10.1016/j.atech.2023.100284
• 发表时间: 2023
• 期刊: Smart Agricultural Technology
• 作者: Chu, Pengyu;Li, Zhaojian;Zhang, Kaixiang;Chen, Dong;Lammers, Kyle;Lu, Renfu
• 通讯作者: Lu, Renfu

Self‐Powered Multifunctional Human–Machine Interfaces for Respiratory Monitoring and Smart System Control

• DOI: 10.1002/admi.202201202
• 发表时间: 2022-07
• 期刊: Advanced Materials Interfaces
• 影响因子: 5.4
• 作者: Y. Pang;Shoue Chen;Yunteng Cao;Zhida Huang;Xianchen Xu;Yuhui Fang;Changyong (Chase) Cao

Efficient Path Planning of Soft Robotic Arms in the Presence of Obstacles

存在障碍物时软体机械臂的高效路径规划

• DOI: 10.1016/j.ifacol.2021.11.235
• 发表时间: 2021
• 期刊: IFAC-PapersOnLine
• 作者: Fairchild, Preston R.;Srivastava, Vaibhav;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo

On Trust-aware Assistance-seeking in Human-Supervised Autonomy

关于人类监督自治中的信任意识寻求援助

• DOI: 10.23919/acc55779.2023.10156103
• 发表时间: 2023
• 期刊: American Control Conference
• 作者: Mangalindan, Dong Hae;Rovira, Ericka;Srivastava, Vaibhav
• 通讯作者: Srivastava, Vaibhav