SBIR Phase II: Rapidly Deployable Mobile Sensor Robots for Disaster Response and Monitoring

SBIR 第二阶段：用于灾难响应和监测的快速部署移动传感器机器人

• 批准号: 1927010
• 负责人: Douglas Hutchings
• 金额: $ 73.39万
• 依托单位: Squishy Robotics Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927010&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Rapidly; Deployable

The broader impact/commercial potential of this project is to commercialize previous research in tensegrity robots for new markets in disaster response. Future deaths and injuries of both victims and first responders during disaster rescues in unchartered, risky environments could be prevented by rapidly deploying sensor robots that use semi-autonomous technology to explore the regions of disasters, provide surveillance to inform first responders, and assist in the rescue of victims until human first responders can arrive. Current emergency response procedures are time-consuming, requiring first responders to don protective suits and hand place sensors to obtain air quality readings. Current disaster robots cannot be rapidly deployed and can be ineffective in navigating surface obstacles and climbing steep slopes to reach areas of interest. Instead, tensegrity sensor robots dropped from aerial vehicles, such as drones or helicopters, can land in dangerous, often difficult-to-reach areas and immediately transmit surveillance and environmental data. With this information, first responder teams can better understand the situational hazards and plan how best to ameliorate the emergency saving lives, while reducing costs and property damage. This proposed technology will also have broader impact in use for scientific and commercial monitoring and surveillance as well.This Small Business Innovation Research (SBIR) Phase II project will advance the development of tensegrity robots, their durability, and their propulsion, de-risking the technology for the commercial market. This work will focus on expanding the features and structures of two robotic platforms: (1) stationary robots that provide persistent monitoring in one location and (2) mobile robots that are capable of ground travel over rough terrain (rubble, rocks, slopes). Finite element analysis/computational fluid dynamics simulations will be used to reduce the robots' weight and improve impact-resilience and portability. Software improvements will focus on enhancing speed capabilities and energy efficiency for rough terrain locomotion. Control engineers will integrate robust Model Predictive Control to improve path planning and enhance the robots' locomotion in a wide range of topologies and environments. Improved software algorithms and user interfaces will provide first responders with summarized data analytics for real-time assessments in the field as well as deliver cloud-server support. Integrated playback functionality and data-driven learning will improve post-situation evaluations. With technologies that provide live 360-degree video feeds and greater incident intelligence, this project will improve rescue outcomes in future disaster recovery operations.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.

该项目更广泛的影响/商业潜力是将以前的张拉整体机器人研究商业化，用于灾害响应的新市场。未来在未经许可的危险环境中进行灾难救援时，受害者和第一响应者的死亡和受伤可以通过快速部署传感器机器人来防止，这些机器人使用半自主技术来探索灾难区域，提供监视以通知第一响应者，并协助救援受害者，直到人类第一响应者到达。目前的应急响应程序非常耗时，需要第一响应者穿上防护服并手持传感器来获取空气质量读数。目前的灾难机器人无法快速部署，并且在导航表面障碍物和爬陡坡以到达感兴趣的区域方面可能无效。相反，从无人机或直升机等飞行器上投放的张拉整体传感器机器人可以降落在危险的、通常难以到达的区域，并立即传输监视和环境数据。有了这些信息，第一响应团队可以更好地了解情况危险，并计划如何最好地改善紧急情况，挽救生命，同时减少成本和财产损失。这项技术也将在科学和商业监测和监视方面产生更广泛的影响。这个小型企业创新研究（SBIR）第二阶段项目将推动张拉整体机器人的发展，其耐用性和推进力，降低商业市场的技术风险。这项工作将侧重于扩大两个机器人平台的功能和结构：（1）在一个地点提供持续监测的固定机器人和（2）能够在粗糙地形（碎石、岩石、斜坡）上地面旅行的移动的机器人。 有限元分析/计算流体动力学模拟将用于减轻机器人的重量，提高抗冲击能力和便携性。软件改进将侧重于提高粗糙地形运动的速度能力和能源效率。控制工程师将集成强大的模型预测控制，以改善路径规划，并增强机器人在各种拓扑结构和环境中的运动。改进的软件算法和用户界面将为第一响应者提供汇总数据分析，用于现场实时评估，并提供云服务器支持。集成的回放功能和数据驱动的学习将改善事后评估。通过提供实时360度视频馈送和更大的事件情报的技术，该项目将在未来的灾难恢复操作中改善救援结果。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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

Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information

• DOI: 10.1115/imece2021-70759
• 发表时间: 2021-11
• 期刊: ArXiv
• 作者: A. Agogino;Hae Young Jang;V. Rao;Ritik Batra;Felicity Liao;R. Sood;Irving Fang;R. Hu;Emerson Shoichet-Bartus;John Matranga

Energy-Efficient Locomotion Strategies and Performance Benchmarks using Point Mass Tensegrity Dynamics

使用点质量张拉整体动力学的节能运动策略和性能基准

• 发表时间: 2019
• 期刊: IROS 2019
• 作者: Cera, B.;Thompson, A.A.;Agogino, A.M
• 通讯作者: Agogino, A.M