Do speed and proximity affect human-robot collaboration with an industrial robot arm?

Current guidelines for Human-Robot Collaboration (HRC) allow a person to be within the working area of an industrial robot arm whilst maintaining their physical safety. However, research into increasing automation and social robotics have shown that attributes in the robot, such as speed and proximity setting, can influence a person’s workload and trust. Despite this, studies into how an industrial robot arm’s attributes affect a person during HRC are limited and require further development. Therefore, a study was proposed to assess the impact of robot’s speed and proximity setting on a person’s workload and trust during an HRC task. Eighty-three participants from Cranfield University and the ASK Centre, BAE Systems Samlesbury, completed a task in collaboration with a UR5 industrial robot arm running at different speeds and proximity settings, workload and trust were measured after each run. Workload was found to be positively related to speed but not significantly related to proximity setting. Significant interaction was not found for trust with speed or proximity setting. This study showed that even when operating within current safety guidelines, an industrial robot can affect a person’s workload. The lack of significant interaction with trust was attributed to the robot’s relatively small size and high success rate, and therefore may have an influence in larger industrial robots. As workload and trust can have a significant impact on a person’s performance and satisfaction, it is key to understand this relationship early in the development and design of collaborative work cells to ensure safe and high productivity

Review of the environmental prenatal exposome and its relationship to maternal and fetal health

Environmental chemicals comprise a major portion of the human exposome, with some shown to impact the health of susceptible populations, including pregnant women and developing fetuses. The placenta and cord blood serve as important biological windows into the maternal and fetal environments. In this article we review how environmental chemicals (defined here to include man-made chemicals [e.g., flame retardants, pesticides/ herbicides, per- and polyfluoroalkyl substances], toxins, metals, and other xenobiotic compounds) contribute to the prenatal exposome and highlight future directions to advance this research field. Our findings from a survey of recent literature indicate the need to better understand the breadth of environmental chemicals that reach the placenta and cord blood, as well as the linkages between prenatal exposures, mechanisms of toxicity, and subsequent health outcomes. Research efforts tailored towards addressing these needs will provide a more comprehensive understanding of how environmental chemicals impact maternal and fetal health

Environmental and financial performance of mechanical recycling of carbon fibre reinforced polymers and comparison with conventional disposal routes

Recovering value from carbon fibre reinforced polymers waste can help to address the high cost and environmental burden of producing carbon fibres, but there is limited understanding of the cost and environmental implications of potential recycling technologies. The objective of this study is to assess the environmental and financial viability of mechanical recycling of carbon fibre composite waste. Life cycle costing and environmental assessment models are developed to quantify the financial and environmental impacts of alternative composite waste treatment routes, comparing landfilling, incineration with energy recovery, and mechanical recycling in a UK context. Current Landfill Tax results in incineration becoming the lowest cost composite waste treatment option; however, incineration is associated with high greenhouse gas emissions as carbon released from composite waste during combustion exceeds CO2 emissions savings from displacing UK electricity and/or heat generation, resulting in a net greenhouse gas emissions source. Mechanical recycling and fibre reuse to displace virgin glass fibre can provide the greatest greenhouse gas emissions reductions of the treatment routes considered (−378 kg CO2 eq./t composite waste), provided residual recyclates are landfilled rather than incinerated. However, this pathway is found to be unfeasible due to its high cost, which exceeds £2500/t composite waste ($3750/t composite waste). The financial performance of mechanical recycling is impaired by the high costs of dismantling and recycling processes; low carbon fibre recovery rate; and low value of likely markets. To be viable, carbon fibre recycling processes must achieve near-100% fibre recover rates and minimise the degradation of fibre mechanical properties to enable higher value applications (e.g., virgin carbon fibre displacement). On-going development of carbon fibre recovery technologies and composite manufacturing techniques using recycled carbon fibres leading to improved material properties is therefore critical to ensuring financial viability and environmental benefit of carbon fibre reinforced polymer recycling

Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications

Human-robot interaction (HRI) research is progressively addressing multi-party scenarios, where a robot interacts with more than one human user at the same time. Conversely, research is still at an early stage for human-robot collaboration. The use of machine learning techniques to handle such type of collaboration requires data that are less feasible to produce than in a typical HRC setup. This work outlines scenarios of concurrent tasks for non-dyadic HRC applications. Based upon these concepts, this study also proposes an alternative way of gathering data regarding multi-user activity, by collecting data related to single users and merging them in post-processing, to reduce the effort involved in producing recordings of pair settings. To validate this statement, 3D skeleton poses of activity of single users were collected and merged in pairs. After this, such datapoints were used to separately train a long short-term memory (LSTM) network and a variational autoencoder (VAE) composed of spatio-temporal graph convolutional networks (STGCN) to recognise the joint activities of the pairs of people. The results showed that it is possible to make use of data collected in this way for pair HRC settings and get similar performances compared to using training data regarding groups of users recorded under the same settings, relieving from the technical difficulties involved in producing these data. The related code and collected data are publicly available