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These days, physical human-robot interaction (pHRI) of social robots has received a lot of attention. This paper proposes a method for variable admittance control (VAC) based pHRI to enable social robots to perform various social gestures. The proposed method includes several schemes. Firstly, a scheme to adjust the robot’s damping is proposed, which takes into account the external torques exerted by the user and the robot’s pose to control movement speed, reflecting the workspaces of both the human and the robot. Secondly, a scheme to change the joint stiffness is proposed, considering the reference angle and the external forces from the user to generate active motions for cooperative pHRI. Additionally, a scheme to change the robot reference point is proposed, which adjusts based on the user’s external forces and the robot’s posture for pHRI in various situations. The proposed methods are implemented with a dual-arm social robot. To realize pHRI based on the proposed schemes, a generalized momentum-based disturbance observer, one of the sensorless disturbance observers, is employed to estimate the exerted torque. To verify the performance of the proposed methods, experiments are carried out for handshaking and hugging.

Because the internal structure, degree of freedom, skin control position and range of the android face are different, it is very difficult to generate facial expressions by applying existing facial expression generation methods. In addition, facial expressions differ among robots because they are designed subjectively. To address these problems, we developed a system that can automatically generate robot facial expressions by combining an android, a recognizer capable of classifying facial expressions and a genetic algorithm. We have developed two types (older men and young women) of android face robots that can simulate human skin movements. We selected 16 control positions to generate the facial expressions of these robots. The expressions were generated by combining the displacements of 16 motors. A chromosome comprising 16 genes (motor displacements) was generated by applying real-coded genetic algorithms; subsequently, it was used to generate robot facial expressions. To determine the fitness of the generated facial expressions, expression intensity was evaluated through a facial expression recognizer. The proposed system was used to generate six facial expressions (angry, disgust, fear, happy, sad, surprised); the results confirmed that they were more appropriate than manually generated facial expressions.

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance. [ABSTRACT FROM AUTHOR]

Human-like appearance and movement of social robots is important in human-robot inter- action. This paper presents the hardware mechanism and software architecture of an incarnate announcing robot system called EveR-1. EveR-1 is a robot platform to implement and test emotional expressions and human-robot interactions. EveR-1 is not bipedal but sits on a chair and communicates information by moving its upper body. The skin of the head and upper body is made of silicon jelly to give a human-like texture. To express human-like emotion, it uses body gestures as well as facial expressions decided by a personality model. EveR-1 performs the role of guidance service in an exhibition and does the oral narration of fairy tales and simple conversation with humans. [ABSTRACT FROM AUTHOR]