Robots equipped with artificial intelligence technology include learning functions. Purely inductive learning methods formulate general hypotheses by finding empirical regularities over the trainning examples. Purely analytical methods use prior knowledge to derive general hypotheses deductively. Therefore, when the physical environment of a robot is complex, there is a problem of increased computational time required for information processing. In particular, when a large number of robots transmit information, more computational time is required for information processing. The distance-based topological method proposed in this paper first constructs the topology based on the distances between robots, and then generates information weights according to the stages of the topology. The technique proposed in this paper has been experimentally confirmed to have excellent performance in environments with a large number of robots and complex physical conditions.

This paper describes a study on posture control of the multi-legged biomimetic underwater robot (CALEB10). Because the underwater environment has a feature that all degrees of freedom are coupled to each other, we designed the posture control algorithm by separating each degree of freedom. Not only should the research on posture control of underwater robots be a precedent study for position control, but it is also necessary to compensate disturbance in each direction. In the research on the yaw directional posture control, we made the drag force generated by the stroke of the left leg and the right leg occur asymmetrically, in order that a rotational moment is generated along the yaw direction. In the composite swimming controller in which the controllers in each direction are combined, we designed the algorithm to determine the control weights in each direction according to the error angle along the yaw direction. The performance of the proposed posture control method is verified by a dynamical simulator and underwater experiments.

This paper presents a force control based on the observer without taking any force or torque measurement from the robot which allows realizing more stable and robust human robot interaction for the developed multi-functional upper limb rehabilitation robot. The robot has four functional training modes which can be classified by the human robot interaction types: passive, active, assistive, and resistive mode. The proposed observer consists of internal disturbance observer and external force observer for distinctive performance evaluation. Since four training modes can be quantitatively identified as impedance variation, position-based impedance control with feedback and feedforward controller was applied to the assistive training mode. The results showed that the proposed sensorless observer estimated cleaner and more accurate force compared to the force sensor and the impedance controller embedded with the proposed observer completed the assistive training mode safely and properly.

The CALEB10 is a multi-legged biomimetic underwater robot. In the last research, we developed a swimming pattern named ESPG (Extended Swimming Pattern Generator) by observing diving beetle’s swimming actions and experimented with a positive buoyancy state in which CALEB10 floats on the water. In this paper, however, we have experimented with CALEB10 in a neutral buoyancy state where it is completely immersed in water for pitch motion control experiment. And we found that CALEB10 was unstably swimming in the pitch direction in the neutral buoyancy state and analyzed that the reason was due to the weight proportion of the legs. In this paper, we propose a pitch motion control method to mimic the pitch motion of diving beetles and to solve the problem of CALEB10 unstably swimming in the pitch direction. To control the pitch motion, we use the method of controlling additional joints while swimming with the ESPG. The method of obtaining propulsive force by the motion of the leg has a problem of giving propulsive force in the reverse direction when swimming in the surge direction, but this new control method has an advantage that a propulsive moment generated by a swimming action only on a target pitch value. To demonstrate validity this new control method, we designed a dynamics-based simulator environment. And the control performance to the target pitch value was verified through simulation and underwater experiments.

Static balance of an articulated robot arm at various configurations requires a torque compensating for the gravitational torque of each joint due to the robot mass. Such compensation torque can be provided by a spring-based counterbalance mechanism. However, simple installation of a counterbalance mechanism at each pitch joint does not work because the gravitational torque at each joint is dependent on other joints. In this paper, a 6 DOF industrial robot arm based on the parallelogram for multi-DOF counterbalancing is proposed to cope with this problem. Two passive counterbalance mechanisms are applied to pitch joints, which reduces the required torque at each joint by compensating the gravitational torque. The performance of this mechanism is evaluated experimentally.

An increasing number of researches and developments for personal or professional service robots are attracting a lot of attention and interest industrially and academically during the past decade. Furthermore, the development of intelligent robots is intensively fostered as strategic industry. Until now, most of practical and commercial service robots are worked by remotely operated controller. The most important technical issue of remote control is a wireless communication, especially in the indoor and unstructured environments where communication infrastructures might be destroyed by various disasters. Therefore we propose a multi-robot following navigation method for securing the valid communication distance extension of the remote control based on WPAN(Wireless Personal Area Networks). The concept and implementation of following navigation are introduced and the performance verification is performed through real navigation experiments in real or test-bed environments.

Various driving mechanisms to adapt to uneven environment have been developed for many urban search and rescue (USAR) missions. A tracked mechanism has been widely used to maintain the stability of robot’s pose and to produce large traction force on uneven terrain in this research area. However, it has a drawback of low energy efficiency due to friction force when rotating. Moreover, single tracked mechanism can be in trouble when the body gets caught with high projections, so the track doesn’t contact on the ground. A transformable tracked mechanism is proposed to solve these problems. The mechanism is designed with several articulations surrounded by tracks, used to generate an attack angle when the robot comes near obstacles. The stair climbing ability of proposed robot was analyzed since stairs are one of the most difficult obstacles in USAR mission. Stair climbing process is divided into four separate static analysis phases. Design parameters are optimized according to geometric limitations from the static analysis. The proposed mechanism was produced from optimized design parameters, and demonstrated in artificially constructed uneven environment and the actual stairway.

This paper introduces a design of multi-dimensional complex emotional model for various complex emotional expression. It is a novel approach to design an emotional model by comparison with conventional emotional model which used a three-dimensional emotional space with some problems; the discontinuity of emotions, the simple emotional expression, and the necessity of re-designing the emotional model for each robot. To solve these problems, we have designed an emotional model. It uses a multi-dimensional emotional space for the continuity of emotion. A linear model design is used for reusability of the emotional model. It has the personality for various emotional results although it gets same inputs. To demonstrate the effectiveness of our model, we have tested with a human friendly robot.

This paper presents a multi-robot localization based on multidimensional scaling (MDS) in spite of the existence of incomplete and noisy data. While the traditional algorithms for MDS work on the full-rank distance matrix, there might be many missing data in the real world due to occlusions. Moreover, it has no considerations to dealing with the uncertainty due to noisy observations. We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nyström approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

This paper presents a decentralized coordination for a small-scale mobile robot teams performing a task through cooperation. Robot teams are required to generate and maintain various geometric patterns adapting to an environment and/or a task in many cooperative applications. In particular, all robots must continue to strive toward achieving the team’s mission even if some members fail to perform their role. Toward this end, given the number of robots in a team, an effective coordination is investigated for decentralized formation control strategies. Specifically, all members are required first to reach agreement on their coordinate system and have an identifier (ID) for role assignment in a self-organizing way. Then, employing IDs on individual robots within a common coordinate system, a decentralized neighbor-referenced formation control is realized to generate, keep, and switch between different geometric shapes. This approach is verified using an in-house simulator and physical mobile robots. We detail and evaluate the formation control approach, whose common features include self-organization, robustness, and flexibility.

The paper proposes a human-following behavior of mobile robot and an intelligent space (ISpace) is used in order to achieve these goals. An ISpace is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to follow a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to follow the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and following of the walking human with the mobile robot are presented.