ORCA Stream B - Towards Resident Robots

ORCA Stream B - 迈向常驻机器人

• 批准号: EP/W001136/1
• 负责人: David Lane
• 金额: $ 244.06万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW001136%2F1
• 关键词: ORCA; Stream; Towards; Resident; Robots

The international offshore energy industry is undergoing as revolution, adopting aggressive net-zero objectives and shifting rapidly towards large scale offshore wind energy production. This revolution cannot be done using 'business as usual' approaches in a competitive market with low margins. Further, the offshore workforce is ageing as new generations of suitable graduates prefer not to work in hazardous places offshore. Operators therefore seek more cost effective, safe methods and business models for inspection, repair and maintenance of their topside and marine offshore infrastructure. Robotics and artificial intelligence are seen as key enablers in this regard as fewer staff offshore reduces cost, increases safety and workplace appeal. The long-term industry vision is thus for a digitised offshore energy field, operated, inspected and maintained from the shore using robots, digital architectures and cloud based processes to realise this vision. In the last 3 years, we has made significant advances to bring robots closer to widespread adoption in the offshore domain, developing close ties with industrial actors across the sector. The recent pandemic has highlighted a widespread need for remote operations in many other industrial sectors. The ORCA Hub extension is a one year project from 5 UK leading universities with over 20 industry partners (>£2.6M investment) which aims at translating the research done into the first phase of the Hub into industry led use cases. Led by the Edinburgh Centre of Robotics (HWU/UoE), in collaboration with Imperial College, Oxford and Liverpool Universities, this multi-disciplinary consortium brings its unique expertise in: Subsea (HWU), Ground (UoE, Oxf) and Aerial robotics (ICL); as well as human-machine interaction (HWU, UoE), innovative sensors for Non Destructive Evaluation and low-cost sensor networks (ICL, UoE); and asset management and certification (HWU, UoE, LIV).The Hub will provide remote solutions using robotics and AI that are applicable across a wide range of industrial sectors and that can operate and interact safely in autonomous or semi-autonomous modes in complex and cluttered environments. We will develop robotics solutions enabling accurate mapping , navigation around and interaction with assets in the marine, aerial and ground environments that support the deployment of sensors for asset monitoring. This will be demonstrated using 4 industry led use cases developed in close collaboration with our industry partners and feeding directly into their technology roadmaps: Offshore Renewable Energy Subsea Inspection in collaboration with EDF, Wood, Fugro, OREC, Seebyte Ltd and Rovco; Aerial Inspection of Large Infrastructures in Challenging Conditions in collaboration with Barrnon, BP, Flyability, SLAMCore, Voliro and Helvetis; Robust Inspection and Manipulation in Hazardous Environments in collaboration with ARUP, Babcock, Chevron, EMR, Lafarge, Createc, Ross Robotics; Symbiotic Systems for Resilient Autonomous Missions in collaboration with TLB, Total Wood and the Lloyds Register. This will see the Hub breach into new sectors and demonstrate the potential of our technology on a wider scale.

国际海上能源行业正在经历一场革命，采用积极的净零目标，并迅速转向大规模海上风能生产。在一个竞争激烈、利润率低的市场上，这种革命不可能用“一切照旧”的方法来完成。此外，离岸劳动力正在老龄化，因为新一代合适的毕业生不愿意在离岸危险的地方工作。因此，运营商寻求更具成本效益，更安全的方法和商业模式来检查，维修和维护其上部和海上离岸基础设施。机器人技术和人工智能被视为这方面的关键推动因素，因为离岸员工的减少降低了成本，提高了安全性和工作场所的吸引力。因此，长期的行业愿景是建立一个数字化的海上能源领域，使用机器人、数字架构和基于云的流程从海岸进行操作、检查和维护，以实现这一愿景。在过去的3年里，我们取得了重大进展，使机器人更接近于在离岸领域的广泛采用，与整个行业的工业参与者建立了密切的联系。最近的大流行突显了许多其他工业部门对远程操作的广泛需求。ORCA Hub扩展是一个为期一年的项目，由5所英国领先大学与20多个行业合作伙伴（投资超过260万英镑）合作，旨在将Hub第一阶段的研究成果转化为行业主导的用例。由爱丁堡机器人中心（HWU/UoE）牵头，与帝国理工学院、牛津大学和利物浦大学合作，这个多学科联盟带来了其独特的专业知识：水下（HWU），地面（UoE，Oxf）和空中机器人（ICL）;以及人机交互（HWU，UoE），用于非破坏性评估和低成本传感器网络的创新型传感器（ICL，UoE）;资产管理和认证（HWU，UoE，该中心将提供使用机器人和人工智能的远程解决方案，适用于广泛的工业部门，并且可以在自主或半自主状态下安全地操作和交互。在复杂和混乱的环境中的自主模式。我们将开发机器人解决方案，实现精确的地图绘制，导航以及与海洋，空中和地面环境中的资产互动，支持部署传感器进行资产监控。这将通过与我们的行业合作伙伴密切合作开发的4个行业主导的用例进行演示，并直接纳入其技术路线图：与EDF，Wood，Fugro，OREC，Seebyte Ltd和Roches合作的海上可再生能源海底检查;与Barrnon，BP，Flyability，SLAMCore，Voliro和Helvetis合作的大型水下结构物的高空检查;与ARUP、Babcock、Chevron、EMR、Lafarge、Createc、Ross Robotics合作，在危险环境中进行稳健的检查和操作;与TLB、Total Wood和劳埃德注册中心合作，在弹性自主任务中使用共生系统。这将使该中心进入新的领域，并在更大范围内展示我们技术的潜力。

项目成果

Sparsity-Inducing Optimal Control via Differential Dynamic Programming

通过微分动态规划的稀疏性最优控制

• 发表时间: 2021
• 作者: Dinev T

A Robotic Experimental Setup with a Stewart Platform to Emulate Underwater Vehicle-Manipulator Systems.

• DOI: 10.3390/s22155827
• 发表时间: 2022-08-04
• 期刊: Sensors (Basel, Switzerland)

Where Should I Look? Optimised Gaze Control for Whole-Body Collision Avoidance in Dynamic Environments

• DOI: 10.1109/lra.2021.3137545
• 发表时间: 2022-04-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Finean, Mark Nicholas;Merkt, Wolfgang;Havoutis, Ioannis
• 通讯作者: Havoutis, Ioannis

Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance

• DOI: 10.1145/3570918
• 发表时间: 2021-11
• 期刊: ACM Transactions on Embedded Computing Systems
• 影响因子: 2
• 作者: Yizhen Dong;Wei Huang;Vibhav Bharti;V. Cox;Alec Banks;Sen Wang;Xingyu Zhao;S. Schewe;Xiaowei Huang

Simultaneous Scene Reconstruction and Whole-Body Motion Planning for Safe Operation in Dynamic Environments

同步场景重建和全身运动规划，确保动态环境中的安全操作

• 发表时间: 2021
• 作者: Finean M