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Object handover is a common physical interaction between humans. It is thus also of significant interest for human–robot interaction. In this paper, we are focused on robot-to-human object handover. The main challenge in this case is how to reduce the failure rate, i.e., to ensure that the object does not fall (object safety), while at the same time allowing the human to easily acquire the object (smoothness). To endow the robot with a failure recovery mechanism, we investigated how humans detect failure during the transfer phase of the handover. We conducted a human study that showed that a human giver primarily relies on vision rather than haptic sensing to detect the fall of the object. Motivated by this study, a robotic handover system is proposed that consists of a motion sensor attached to the robot's gripper, a force sensor at the base of the gripper, and a controller that is capable of regrasping the object if it starts falling. The proposed system is implemented on a Baxter robot and is shown to achieve a smooth and safe handover.

In robot to human object handover, the configuration (position and orientation) in which the object is transferred should be selected so that the handover is safe and comfortable for the human. The trajectory on which the robot moves the object to the point of transfer should be also selected so that the robot intention is clear and the handover feels natural to the human. In this paper, we propose to select the configuration for the transfer and the trajectory to reach this configuration based on what humans do in human-human handovers. We describe a human study designed to investigate the human-human handover and propose an ergonomic model that can predict object transfer position observed in the study. A human-robot experiment is then conducted that shows that the proposed model generates transfer positions that match the preferred height and distance relative to the human.

Human grasp has been studied extensively and many taxonomies have been developed to classify different grasp types. In this work, we collect a comprehensive list of grasp types and investigate the force distribution patterns among them. We conduct a human study and collect force data in various grasp types. Data collection is performed by utilizing a data glove that has seventeen force sensors. We analyze the measured forces and identify different patterns in force distribution. Employing voting technique over various clustering methods, we propose a robust clustering for the force patterns, namely the grasp taxonomy in force domain. The proposed grasp taxonomy can be exploited in designing robotic prosthetic hands, designing grasp controllers and action recognition in human-robot interaction.

Humans rely on a multitude of senses to achieve a safe handover. The challenge for a robot-human handover is to both equip the robot with the appropriate sensors as well as devise handover controllers that effectively use them to prevent failures. In this paper we use object acceleration as an indicator of an impending handover failure and propose a handover controller that has re-grasping mechanism to prevent falling of the object in case of an imperfect handover. The work is motivated by our observation that humans primarily rely on vision to prevent handover failure. We also propose a novel acceleration sensing setup that can be integrated into a robot gripper. The handover controller is implemented on Baxter Research robot equipped with the proposed sensor and is shown to be effective in preventing object fall.

The purpose of this paper is proposing a comprehensive model that shows the effect of green supply chain management practices on sustainable performance focusing on environmental collaboration. 311 pieceworkers companies in the field of automotive, motorcycle and agricultural machinery were investigated. Questionnaire was used for collecting data. Structural equation model were used as a technique to analyzing the data. The results of analyzing data showed that green supply chain management practices have positive effect on sustainable performance and environmental collaboration. As mediating variable environmental collaboration has also positive effect on green supply chain management practices focusing on environmental collaboration on sustainable performance. Green supply chain can help increasing sustainable performance and environmental collaboration is also considered as an important capability for facilitating executing green supply chain management. Both positively impact society through improvements to the overall environment. This research is one of the few studies that explore the effect of green supply chain management practices on sustainable performance focusing on environmental collaboration. Green supply chain management practices plays an important role of each enterprise which is involved with supply chain activities and it will help increasing sustainable performance.

Output power fluctuations are very important for photovoltaic (PV) systems. Where PV is connected into the grid, the rapid fluctuations would be detrimental to the performance of the power system to which the PV system is connected. Furthermore, rapid fluctuations of the output power of the PV systems make difficult prediction of their output power. As a result, planning for other generating units of the grid will encounter with trouble. In this paper, energy storage systems including batteries, which are the most common options for PV systems, have been analyzed to manage and to control output power rate of such systems. As power generation by PV systems is not a cost effective process, battery performance should be optimized as far as possible to prevent imposition of further costs caused by using batteries. In this paper, four common batteries have been compared in terms of cost and capability to decline their fluctuations and the most optimum option, which brings about the least costs, has been selected.