Whilst industrial robots have been widely used in many industrial sectors, they are predominantly used in a structured factory environment. In recent years, off-site robotics have been investigated extensively and the...Whilst industrial robots have been widely used in many industrial sectors, they are predominantly used in a structured factory environment. In recent years, off-site robotics have been investigated extensively and there are some promising candidates emerging. One such category of robots is exoskeleton robots and this paper provides an in-depth assessment of their suitability in assisting human operators in undertaking manual operations typically found in the construction industry. This work aims to objectively assess the advantages and disadvantages of these two suits and provide recommendations for further improvements of similar system designs. The paper focuses on the passive exoskeleton robotic suits which are commercially available. Three types of activities are designed and a mechatronic methodology has been designed and implemented to capture visual data in order to assess these systems in comparison with normal human operations. The study suggests that these passive suits do reduce the effort required by human operators to undertake the same construction tasks as evidenced by the results from one focused study, though a number of improvements could be made to improve their performance for wider adoption.展开更多
The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle ser...The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.展开更多
Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as cur...Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature.It also aims to use an extended Kalman filter(EKF)to estimate the fused position of the UAVs and to apply the 2-D splinegon technique to build the map of the complex shaped obstacles.The path of the UAVs are dictated by the Dubins path planning algorithm.The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.Design/methodology/approach–An extended Kalman filter is used to estimate the position of the UAVs,and the 2-D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.Findings-The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2-D splinegon technique.This is a newly proposed algorithm,the most efficient and a robust way in terrain based mapping of the complex obstacles.The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.Originality/value-The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2-D splinegon technique.This is a new approach where the fused EKF estimated positions are used with the limited number of sensors’measurements in building the map of the complex obstacles.展开更多
Magnetic resonance imaging(MRI)is used to image root systems grown in opaque soil.However,reconstruction of root system architecture(RSA)from 3-dimensional(3D)MRI images is challenging.Low resolution and poor contrast...Magnetic resonance imaging(MRI)is used to image root systems grown in opaque soil.However,reconstruction of root system architecture(RSA)from 3-dimensional(3D)MRI images is challenging.Low resolution and poor contrast-to-noise ratios(CNRs)hinder automated reconstruction.Hence,manual reconstruction is still widely used.Here,we evaluate a novel 2-step work flow for automated RSA reconstruction.In the first step,a 3D U-Net segments MRI images into root and soil in super-resolution.In the second step,an automated tracing algorithm reconstructs the root systems from the segmented images.We evaluated the merits of both steps for an MRI dataset of 8 lupine root systems,by comparing the automated reconstructions to manual reconstructions of unaltered and segmented MRI images derived with a novel virtual reality system.We found that the U-Net segmentation offers profound benefits in manual reconstruction:reconstruction speed was doubled(+97%)for images with low CNR and increased by 27%for images with high CNR.Reconstructed root lengths were increased by 20%and 3%,respectively.Therefore,we propose to use U-Net segmentation as a principal image preprocessing step in manual work flows.The root length derived by the tracing algorithm was lower than in both manual reconstruction methods,but segmentation allowed automated processing of otherwise not readily usable MRI images.Nonetheless,model-based functional root traits revealed similar hydraulic behavior of automated and manual reconstructions.Future studies will aim to establish a hybrid work flow that utilizes automated reconstructions as scaffolds that can be manually corrected.展开更多
文摘Whilst industrial robots have been widely used in many industrial sectors, they are predominantly used in a structured factory environment. In recent years, off-site robotics have been investigated extensively and there are some promising candidates emerging. One such category of robots is exoskeleton robots and this paper provides an in-depth assessment of their suitability in assisting human operators in undertaking manual operations typically found in the construction industry. This work aims to objectively assess the advantages and disadvantages of these two suits and provide recommendations for further improvements of similar system designs. The paper focuses on the passive exoskeleton robotic suits which are commercially available. Three types of activities are designed and a mechatronic methodology has been designed and implemented to capture visual data in order to assess these systems in comparison with normal human operations. The study suggests that these passive suits do reduce the effort required by human operators to undertake the same construction tasks as evidenced by the results from one focused study, though a number of improvements could be made to improve their performance for wider adoption.
文摘The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.
文摘Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature.It also aims to use an extended Kalman filter(EKF)to estimate the fused position of the UAVs and to apply the 2-D splinegon technique to build the map of the complex shaped obstacles.The path of the UAVs are dictated by the Dubins path planning algorithm.The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.Design/methodology/approach–An extended Kalman filter is used to estimate the position of the UAVs,and the 2-D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.Findings-The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2-D splinegon technique.This is a newly proposed algorithm,the most efficient and a robust way in terrain based mapping of the complex obstacles.The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.Originality/value-The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2-D splinegon technique.This is a new approach where the fused EKF estimated positions are used with the limited number of sensors’measurements in building the map of the complex obstacles.
基金supported by the German Research Foundation(DFG)(grants BE 2556/15-1 and SCHN 1361/3-1)partially funded by the DFG under Germany’s Excellence Strategy,EXC-2070-390732324-PhenoRob+1 种基金the German Federal Ministry of Education and Research(BMBF)in the framework of the funding initiative Soil as a Sustainable Resource for the Bioeconomy BonaRes,the project BonaRes(Module A):Sustainable Subsoil Management-Soil3subproject 3(grant 031B1066C).
文摘Magnetic resonance imaging(MRI)is used to image root systems grown in opaque soil.However,reconstruction of root system architecture(RSA)from 3-dimensional(3D)MRI images is challenging.Low resolution and poor contrast-to-noise ratios(CNRs)hinder automated reconstruction.Hence,manual reconstruction is still widely used.Here,we evaluate a novel 2-step work flow for automated RSA reconstruction.In the first step,a 3D U-Net segments MRI images into root and soil in super-resolution.In the second step,an automated tracing algorithm reconstructs the root systems from the segmented images.We evaluated the merits of both steps for an MRI dataset of 8 lupine root systems,by comparing the automated reconstructions to manual reconstructions of unaltered and segmented MRI images derived with a novel virtual reality system.We found that the U-Net segmentation offers profound benefits in manual reconstruction:reconstruction speed was doubled(+97%)for images with low CNR and increased by 27%for images with high CNR.Reconstructed root lengths were increased by 20%and 3%,respectively.Therefore,we propose to use U-Net segmentation as a principal image preprocessing step in manual work flows.The root length derived by the tracing algorithm was lower than in both manual reconstruction methods,but segmentation allowed automated processing of otherwise not readily usable MRI images.Nonetheless,model-based functional root traits revealed similar hydraulic behavior of automated and manual reconstructions.Future studies will aim to establish a hybrid work flow that utilizes automated reconstructions as scaffolds that can be manually corrected.
