Increasing demand for automation and recent intact trends have accelerated the employment of robots in manufacturing and service areas. Thus, there is a growing demand for easier and safer operation of robots, both by experts and non-professionals. This paper proposes a detachable smart robot teaching device with collision prediction to meet such demands. The proposed device is composed of two modules. One is for easy teaching based on a 6D mouse, and the other is for safe operation by measuring the distance to the surrounding obstacles and predicting the collision situation. The teaching module is used only in a teaching phase and can be separated afterward. A prototype is implemented, and the performance is verified through experiments with a collaborative robot.

Robotic manipulators require contact force sensing capabilities to sense the contact force between the manipulator and an object. Specifically, for humans and robots to coexist in the work environment, the robot must be able to detect an external force applied by a human. This study presents a new intrinsic force sensing method for robot manipulators that can obtain accurate information of the external force applied by a human during human–robot interaction. The method employs a robot cover, which is typically utilized in robot manipulators. Unlike conventional force sensing methods, a six-axis force/torque sensor is placed between the cover and the link of the robot manipulator. As a result, the proposed method provides information of the three-axis contact force applied to the cover surface and its contact location. Therefore, the cover itself becomes a sensorized cover based on the intrinsic force sensing method. To evaluate its sensing performance, the sensorized cover is experimentally validated using reference sensors. Finally, an experiment is performed in which the robot successfully recognizes letters written on the cover, indicating a high level of contact force sensing performance.

The industrial dual-arm robot is being developed. The developed industrial dual-arm robot aim to work with human workers or to work instead of human workers. Redundancy by high degree of freedom caused by arm and waist make robot movement difficult in the narrow space for human workers. Robot arms would take unexpected posture without proper redundant control method. In particular elbows can cause hazard situation by colliding with the environment or body of robot. Here novel method to control robot elbows is introduced. It shows good performance without loss of the position precision of end-effectors. Also it does not require high computing power, which make it useful for practical robot control. The proposed method is confirmed by the simulation.

Many optimization problems in recent engineering are complex and high-dimensional problems, a so-called Large-Scale Global Optimization (LSGO) problem, due to the increasing requirements for multidisciplinary approach. This paper proposes a novel Bare Bones Particle Swarm Optimization (BBPSO) algorithm to solve LSGO problems. The BBPSO is a variant of a Particle Swarm Optimization (PSO) and is based on Gaussian distribution. The BBPSO does not consider the selection of controllable parameters of the PSO and is a simple but powerful optimizer. This algorithm, however, is vulnerable to LSGO problems. This study has improved its performance for LSGO problems by combining the heterogeneous cooperation based on the information exchange between particles and the Gaussian jump strategy to avoid local optima. The CEC'2015 Special Session on Large-Scale Global Optimization has given 15 benchmark problems to provide convenience and flexibility for comparing various optimization algorithms specifically designed for large-scale global optimization. Simulations performed with those benchmark problems have verified the performance of the proposed optimizer and compared with the reference algorithm DECC-G of the CEC'2015 special session on LSGO.

All of industrial robots need the fixed-time control property commonly. The EtherCAT is a highly flexible network protocol that is developing at a rapid rate. It is extention of conventional ethernet with a high speed communication and simple wiring characteristic. With the ethercat, the implementation of the industrial robot is more easier. Here, the implementation of the industrial robot with the function of the direct teaching is described.

The safety of humans working with robots is an important issue. Many studies have addressed related methods, but fundamental limits to meet required safety have been met owing to the absence of compliance in the robot actuators. Pneumatic muscle is considered to be a basic actuator and offers the advantage of intrinsic elasticity to achieve the joint compliance. Here, the joint compliance actuated by pneumatic muscle is actively utilized to enhance human safety during collisions. To this end, the authors present a novel approach to control compliance and position independently without affecting on the each other's performance using pneumatic muscles. The presented method is verified by experiments using a physical robot.

This paper proposes a robust method to estimate sound direction for mobile robot interface. In contrast to other methods, the proposed method estimates sound direction at intervals of short time to deal with deterioration by movement of sound source or robot. The two methods, discrete Kalman filter and time delay of arrival (TDOA) using generalized cross-correlation (GCC), are combined by quantifying reliability of TDOA. The combination prevents the deterioration by low power of signals, noise, and reverberation because discrete Kalman filter efficiently filters out such effects. The result of experiments, in which mobile robot estimates azimuth angle with three microphones, shows that the proposed method provides robustness on estimating sound direction even if the sound source or robot is moving during estimation procedure.

Pneumatic muscle has many advantages such as elasticity, high power and structural similarity to a living thing's muscle. There has been many researches to control robot actuated by pneumatic muscles, but conventional theories are hard to apply on real robot plants because of their assumptions and disregards of pneumatic muscle's physical aspects like size of pneumatic muscle and its controller. Here, the new method for saving space which is occupied by many controllers to operate robot actuated by pneumatic muscles is proposed. Actually there is easy way to control pneumatic muscle using the commercial proportional pressure regulator, but its size is not suitable to be embedded on stand alone robot. So, new method using the pressure switches of compact size and encoders is suggested. This new method is tested on a robot link with ball joints, actuated by four pneumatic muscles

This paper deals with the emotional model of the software-robot. The software-robot requires several capabilities such as sensing, perceiving, acting, communicating, surviving and so on. There are already many studies about the emotional model like KISMET, AIBO. Though many emotional models are implemented, their human response architectures are invariant as time passes. Conventional emotional models make a robot like and obey a human during robot operation. This is natural for robots which are used in industrial, service and military areas but for pet robots this property lets robot look like real robots not real pets. Actually, a robot's emotional model is studied to implement robot intelligence more easily but emotion expression has to be exaggerated in a pet robot to make it interesting to humans. Pet robots must show dynamical emotion variations. The emotional model with the modified friendship is studied in this paper to overcome the conventional emotional model's limits to apply to pet robots. Friendship is distinguished as positive, negative and static schemes and an emotional model with the modified friendship is implemented and simulated on a software-robot.

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks such as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed on the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake would not operate well because the moving object may have large differences in its position or shape, between successive images. Snake's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model (ACSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.