Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.University of Michigan Mcubed Grant: Virtual Prototyping of Human-Robot Collaboration in Unstructured Construction EnvironmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/1/You et al. forthcoming in AutCon.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/4/You et al. 2018.pdfDescription of You et al. 2018.pdf : Published Versio

The different effector function capabilities of the seven equine IgG subclasses have implications for vaccine strategies

Recombinant versions of the seven equine IgG subclasses were expressed in CHO cells. All assembled into intact immunoglobulins stabilised by disulphide bridges, although, reminiscent of human IgG4, a small proportion of equine IgG4 and IgG7 were held together by non-covalent bonds alone. All seven IgGs were N-glycosylated. In addition IgG3 appeared to be O-glycosylated and could bind the lectin jacalin. Staphylococcal protein A displayed weak binding for the equine IgGs in the order: IgG1 > IgG3 > IgG4 > IgG7 > IgG2 = IgG5 > IgG6. Streptococcal protein G bound strongly to IgG1, IgG4 and IgG7, moderately to IgG3, weakly to IgG2 and IgG6, and not at all to IgG5. Analysis of antibody effector functions revealed that IgG1, IgG3, IgG4, IgG5 and IgG7, but not IgG2 and IgG6, were able to elicit a strong respiratory burst from equine peripheral blood leukocytes, predicting that the former five IgG subclasses are able to interact with Fc receptors on effector cells. IgG1, IgG3, IgG4 and IgG7, but not IgG2, IgG5 and IgG6, were able to bind complement C1q and activate complement via the classical pathway. The differential effector function capabilities of the subclasses suggest that, for maximum efficacy, equine vaccine strategies should seek to elicit antibody responses of the IgG1, IgG3, IgG4, and IgG7 subclasses

High-performance parallel hexapod-robotic light abrasive grinding using real-time tool deflection compensation and constant resultant force control

In robotic grinding, significant tool deflection occurs due to the lower stiffness of the manipulator and tool, compared with operation by universal grinding machines. Tool deflection during robotic grinding operation causes geometrical errors in the workpiece cross section. Also, it makes difficult to control the grinding cutting depth. In this study, a method is proposed for calculation of the tool deflection in normal and tangential directions based on grinding force feedback in these directions. Based on calculated values, a real-time tool deflection compensation (TDC) algorithm is developed and implemented. Force interaction between the tool and workpiece is significant for grinding operation. Implementing grinding with constant normal force is a well-known approach for improving surface quality. Tool deflection in the robotic grinding causes orientation between the force sensor reference frame and tool reference frame. This means that the measured normal and tangential forces by the sensor are not actual normal and tangential interaction forces between the tool and workpiece. In order to eliminate this problem, a resultant grinding force control strategy is designed and implemented for a parallel hexapod-robotic light abrasive surface grinding operation. Due to the nonlinear nature of the grinding operation, a supervised fuzzy controller is designed where the reference input is identified by the developed grinding force model. This grinding model is optimized for the robotic grinding operation considering setup stiffness. Evaluation of the experimental results demonstrates significant improvement in grinding operation accuracy using the proposed resultant force control strategy in parallel with a real-time TDC algorithm