One of the problems in imitation between humans and robotd is embodiment gap. Therefore, in this paper, we propose a system that realizes movement level imitation between humans and robots for embodiment gap. We target embodiment gap arising from link length and number of joints, and estimate embodiment gap by solving not only the inverse kinematics but also the forward kinematics using the Levenbarg-Marquart method. By doing this, human movement is separated into end-effector-oriented movement and joint angle-oriented movement according to the robot's own embodiment. Then, based on the separated movement, we realize the imitation of movement using Behavior Cloning based Energy-Based Model. We applied the proposed method to a small humanoid robot and verified its effectiveness. As a result, we confirmed that the proposed method separates human movement dependent on the robot's embodiment. In addition, we realized the imitation learning of movement corresponding to embodiment gap estimated from the separated human movement.

In this study, we developed a process of heuristically searching and selecting the optimal viewpoint for capturing a good composition of a group of moving subjects with an autonomous indoor drone camera. The subjects on the drone's camera screen are represented using a Gaussian mixture model. The Kullback–Leibler divergence between the Gaussian mixture model and a user-defined reference composition is evaluated and defined as the composition evaluation value. The drone searches for a viewpoint in a 3D space to optimize this value using particle swarm optimization. To facilitate the search, a recursive Gaussian process is employed to update the prediction of the observation result. Through the proposed method, a sufficient optimal viewpoint can be obtained, even for moving subjects.

Adjustable autonomy is an attractive paradigm to deploy autonomous robots that require occasional interaction with humans or partner robots. In multi-robot applications, autonomy levels such as teleoperation and fully autonomous are extended to team levels, and depending on the task might have complex hierarchical relations.This paper presents a preliminary work on multi-robot coordination strategy based on evaluating the robot’s level of autonomy. With appropriate assumptions, choosing the level of autonomy can be interpreted as a cooperative planning problem. To this end, we propose to encode the robot’s task and motion plans as a Behavior Tree (BT) to monitor the execution and react to external disturbances. Our approach combines an informative path planning with BT synthesis to obtain a plan that allows a robot to explore and act depending on the uncertainty in an environment representation. We demonstrate how a robot can switch between the single and cooperative execution depending on the exploration’s outcome in a navigation among movable objects scenario.

In this study, we aim to propose a robot photographer system which could take photographs for people with high aesthetic values by searching for the optimal photographing viewpoint autonomously. Aesthetic composition evaluation and observing viewpoint selection are two main issues which should be considered to construct this system. Different from conventional composition evaluation methods which are following some well-known rules such as rule of thirds and diagonal rule, we focus on yohaku of photographs, which means the perceptual empty space in Japanese, to help to construct an aesthetic composition more flexibly. To achieve this purpose, we first propose the yohaku detection method for actual photographs. Then aesthetic composition evaluation will be executed from the viewpoint of visual balance between ordinary targets and yohaku. Finally, using the viewpoint selection method called Composition Map, autonomous photographing will be achieved by evaluating the aesthetic value of scenes with our system.

In this paper, we propose a novel and robust cognitive sharing method based on edge-label subgraph matching for ROI sharing and object recognition. This method enhances a vision based cooperation for UAV and ground robot system under an unknown environment where GPS is not available. The proposed subgraph matching method is an extension of Nema in that only edge-label graph is assumed and hence it can be applied to the situation where the node-labels are hard to distinguish. This method is applied to the navigation system where a UAV navigates a ground robot to the target position via ROI sharing. We test the method through a series of simulations and confirm a stable and good performance in different situations.

Conventional humanoids have achieved walking and running by independent controllers, even though a transition between these independent motions should be added to connect them while ensuring stability. In contrast, human selects walking/running at low/high speed in terms of energy efficiency, and transits between them naturally. This fact implies that human gait shares the inherent controller among walking and running despite their quite different appearances. Hence, we propose a “unified bipedal gait,” which includes walking, running, and the transition. The unified bipedal gait has the inherent controller: passive dynamic autonomous control (PDAC) with a damping and spring loaded inverted pendulum (D-SLIP) model. The PDAC constrains the humanoid natural dynamics by a virtual holonomic constraint (VHC) that degenerates the natural manifold of the states for stabilization. Compliance in the D-SLIP is capable to yield the required characteristics of walking/running: low/high compliant legs for walking/running. Thus, a novel VHC is designed to extract the required characteristics of walking/running from the D-SLIP dynamics and form the proper manifold. As a result, we achieved the unified bipedal gait that bifurcates to walking and running via the natural transition. The high energy efficiency was confirmed in this unified bipedal gait at any gait speed.

This paper deals with a visual assistance system for a remote control of a working robot. While an operator controls the working robot, an another autonomous monitoring robot evaluates a suitable viewpoint to capture the working situation, and dynamically moves to the optimal viewpoint for monitoring. The monitoring robot detects a gestalt of the scene, where a latent and expected interactive objects are included in the region of interest (ROI). Latent Dirichlet Allocation (LDA), which is a probabilistic topic model, is employed to judge the degree of risk around the working robot. Such a situated ROI is transmitted to the operator in real time. Experimental results shows that the proposed system can enhance the remote control mission by reducing the occurrence of error significantly.

This paper proposes a quasi-passive dynamic autonomous control (Q-PDAC) for a three-dimensional (3-D) bipedal gait of humanoid robots from start points to goal points. The major approach for 3-D traveling is currently footstep planning by a constantly stable gait with an emphasis on its accurate and secure traveling. However, energy would potentially be wasted when the robot accurately travels according to the planned footsteps. In contrast, a limit-cycle-based gait possesses the good efficiency by a gait speed control, although the accurate and secure traveling is difficult for it. Its gait speed control is unfortunately not enough to freely travel on 3-D spaces: shortages of a turning speed control, trackability of horizontal speed, and stability of the bipedal gait. Hence, the Q-PDAC supplies three proper angular momenta by hip and ankle joints to achieve the turning motion and enhance the trackability of horizontal motion. Three angular momenta are simply designed consistent in the PDAC dynamics, and achieved the sufficient gait speed control for 3-D traveling. As a result, the robot can efficiently travel from the start point to the goal point while following a leader point not to collide with walls.

Walking capability composed of stability and efficiency is one of the most important issues in the field of humanoid robots. An effective swing of the arms is expected to enhance the walking capability under the constraints from the limited body. We propose an arm-swing method to enhance the stability and efficiency by selecting optimal arm-swing strategy depending on the walking conditions. In this research, we select the optimal strategy between the support of the center of gravity (COG) tracking for stability and the walk without arm-swing for efficiency. To support the COG tracking, we employ a predictive control. States are defined as an inverted pendulum model and inputs are given as an inertial force of arm-swing. Input and output weights in the predictive control are adjustable by a support weight introduced in this paper. Selection of the optimal support weight by a selection algorithm for locomotion (Su-SAL) switches the two strategies by adjusting the ratio of input and output (I/O) weights. Su-SAL maximizes the efficiency while keeping the stability in comparison with the case of the constant support weight.

One serious problem in autonomous mobile robot is the accurate and effective following of an unknown path traced in the environment in compliance with the kinematic limits of the vehicle, i.e., bounded linear and angular velocities and accelerations. In this case, the motion planning must be implemented in realtime and must be robust with respect to the geometric characteristics of the unknown path such as curvature. To achieve good tracking capability, this paper proposes a path tracing system (PTS) with velocity control system, which is based on the curvature of the trajectory ahead of the vehicle to improve the image processing performance and tracking precision. A feedback system is also presented to guarantee the precision of the velocity control system and tracking system. To validate this curvature based velocity control system, a comparative experiment between PTS with speed control module and PTS without speed control module, has been operated. A simulation has also been carried out for confirming the optimum cornering speed corresponding to curvature value.

Visual recognition in multi-robot systems is afflicted with a peculiar problem that observations made from different viewpoints bring different perspectives. Due to the lack of a discriminable representation between a target and its surroundings in different viewpoints, realizing cognitive sharing of the object among the robots in an unconstructed environment is a challenging issue. In this paper, we propose novel description algorithms of the target representation based on ambiguity minimization of peripheral context. The target is represented by a labeled-graph and its structure is determined by minimizing a metric of representational ambiguity using an entropy evaluation with metaheuristics. Experimental results show the significantly improvement of cognitive sharing by the proposed method.

A cane-type robot called intelligent cane has been developed to support the elderly during walking. By supporting a part of a user's body weight, the cane robot aims to reduce a load applied to a user's affected leg. Therefore, while the user's affected leg is a support leg, it is preferable that the cane robot stops to sufficiently support the user. In our previous work, the cane robot is controlled based on horizontal component of force applied to the cane robot and moment around a vertical axis. In this paper, virtual friction force, which is proportional to vertical component of force, is proposed to improve a walking assistance capability of the cane robot. In addition, virtual frictional coefficients are arranged based on the user's state inferred by a laser range finder. By employing the proposed method, the cane robot moves easily in the both legs support phase, stops in the healthy leg support phase, and supports the user reliably in the affected leg support phase.

An environmentally specific type of locomotion (e.g., bipedal or quadrupedal walking) is effective only under the specified environments. However, other conditions could cause physical body constraints and decrease mobility. Despite these constraints, legged robots are desired with high overall mobility such that they can walk under various conditions. Thus, a combination of types of locomotion is needed to maximize overall mobility. We have developed a gorilla-type robot, which can switch between bipedal and quadrupedal walking. A selection technique to optimize locomotion choice would be beneficial to the robot, which will experience challenging situations when walking through complex terrains, receiving disturbances, or malfunctioning. We present a selection algorithm for locomotion (SAL) that improves overall mobility by autonomously selecting the optimal locomotion. The falling risk of each locomotion mode is evaluated with a Bayesian network to represent the robot's situation. The evaluation function for the SAL determines the optimal locomotion choice based on falling risk and moving speed. In this paper, the SAL is used for two state variables of locomotion: gait (Ga-SAL) and speed (Sp-SAL). Both the simulations and experiments validated that the robot traveled efficiently in complex environments.

In human and robot collaborative hybrid assembly cell as we proposed, it is important to develop automatic subtask allocation strategy for human and robot in usage of their advantages. We introduce a folk-joint task model that describes the sequential and parallel features and logic restriction of human and robot collaboration appropriately. To preserve a cost-effectiveness level of task allocation, we develop a logic mathematic method to quantitatively describe this discrete-event system by considering the system tradeoff between the assembly time cost and payment cost. A genetic based revolutionary algorithm is developed for real-time and reliable subtask allocation to meet the required cost-effectiveness. This task allocation strategy is built for a human worker and collaborates with various robot co-workers to meet the small production situation in future. The performance of proposed algorithm is experimentally studied, and the cost-effectiveness is analyzed comparatively on an electronic assembly case.

This paper introduces a region of interest (ROI) sharing protocol for robots, each of which observes different regions in the same scene. This type of observation is generally referred to as “distributed sensing.” The ROI sharing protocol is implemented based on a concept of semantic stability, which helps to find a stable context by selecting sufficient information from an image or a scene. The semantic stability is formulated based on semantic relatedness and the information gap in the relations found in the ROI. Moreover, the necessary and sufficient information is determined by selecting the best ROI from a given image. Finally, the best ROI is shared between the robots for making cooperation. Experimental results show that the ROI sharing is efficient in terms of task performance and robustness.

This paper shows improvement of stability and efficiency for mobility using locomotion selection strategy. First strategy is the selection of a gait relying on locomotion rewards. The locomotion reward has been proposed as an indicator for selection algorithm based on Falling Risk and the moving speed. This strategy has achieved a capability of large changes of uncertainties, such as a steep slope. Second strategy is adjustment of moving speed by the extended locomotion reward that explicitly shows the relationship between the moving speed and Falling Risk. The robot aims at the maximum moving speed without a falling, and removes small changes of uncertainties as a result. We performed an experiment in order to confirm effects of two strategies in an environment that includes a rough terrain as a small uncertainty and two steps as a large uncertainty. The robot improved the moving speed about 37.5% from the case of only using the gait selection strategy.

Intelligent cane is a robot developed for assisting the elderly or handicapped people in walking. As a nursing-care device, the cane robot is designed to assist the elderly or handicapped people not only in indoor environments but also in outdoor environments. Therefore the cane robot is required to be smaller and lighter. In addition, it is preferred that the cane robot is movable in omni-directions so that it can be used in various situations. A concept called “intentional direction (ITD)” was proposed to estimate the user's walking intention by analyzing signals from a 6-axis force/torque sensor fixed to the handle of the aluminum stick. Admittance control method was applied to the motion control of the cane robot. In this paper, a new algorithm based on the usage and purpose of an ordinary cane is proposed. In the proposed algorithm, the cane robot is appropriately stopped to support the elderly more effectively. The effectiveness of the proposed method is verified through the experiments.

In electronic manufacturing system, the design of the robotic gripper is important for the successful accomplishment of the assembly task. Due to the restriction of the architecture of traditional robotic hands, the status of assembly parts during the assembly process cannot be effectively detected. In this research, an intelligent robotic gripper - i-Hand equipped with multiple small sensors is designed and built for this purpose, getting the essential parameters for some specific mathematical model. Mating connectors by robot, as an experimental case in this paper, is studied to evaluate the performance of i-Hand. A simple new model is proposed to describe the process of mating connectors, within which the distance between the connector and deformable Printed Circuit Board (PCB) is detected by i-Hand. An online Fault Detection and Diagnosis (FDD) algorithm is proposed. Various possible situations during assembly are considered and handled according to an event driven work flow. The effectiveness of proposed model and algorithm is proved by the experiments.

This paper deals with a method of locomotion selection based on Falling Risk and moving efficiency. The robot estimates information from sensors by solving state equation. The robot evaluates the Falling Risk as an indicator of uncertainty. Falling Risk is derived from measured information by using Bayesian Network. Locomotion selection during walking is modeled as a Semi-Markov Decision Process and the most appropriate locomotion is selected by using the greedy algorithm. As a result, the robot can move in the environment that is difficult to travel by single locomotion mode, maintaining the maximum moving efficiency.

For multi-contact robot motion, a closed chain is formed by robot links and the environment. This paper proposes a new methodology named “key joint method” for reducing the energy cost by adjusting an internal stress inside a closed chain. Firstly, we analyze the internal stress theoretically taking the degrees of freedom (DOF) and the number of position actuated joints into consideration, then a practical key joint method is proposed by changing a suitable redundant position controlled joint to be force control. After that, a parametric family is introduced for representing various of possible motions subjected to the robot dynamics and other constraints. Finally, a general optimization method is proposed for planning an energetically efficient multi-contact robot motion taking the motion trajectories and internal stress into consideration. As an example, the pace gait ladder decent motion is taken to explain the principle and realization of the proposed method. As experimental evaluation shows, the key joint method is effective for reducing the energy cost in the multi-contact motion.

An intelligent cane robot (iCane) was designed for aiding the elderly who have muscle weakness on lower limbs. A commercial omni-directional wheels robot was used as an omni-directional mobile base, and an aluminum stick was installed on the base of cane robot. A Concept called “intentional direction (ITD)” was proposed for estimating the user's walking intention by analyzing the signal of a 6-axis force/torque sensor which is fixed to the handle of stick. A universal joint driven by two DC motors was designed to control the posture of the stick. As a care-nursing device, the cane robot was designed to assist the elderly in both indoor and outdoor environments. Therefore the size and weight of cane robot should be minimized. But in that case, there is high risk that the cane robot would be pushed over by the user. In this paper a constrained nonlinear multivariable algorithm was designed to optimize the stable posture of cane robot. By controlling the posture of stick, the maximums sufferable torque moment which lead to cane robot falling over can be increased. The experimental results show that the stability of cane robot can be enhanced effectively.

Manufacturing industry tends to employ more flexible assembly cells for High-Mix, Low-Volume production. We has proposed an innovative human and robot hybrid assembly cell within this purpose to solve the problem of persistent growing cost for human resources and now and again changes in programs and configurations for robots, and to achieve a high manufacturing efficiency. One of the key issues is to find out the optimal way of allocating the assembly subtasks to both human and robot. In this paper, a model for assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly is proposed. A Dual Generalized Stochastic Petri Net (GSPN) model is theoretically researched and then built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost for possible strategies, and Multiple-Objective Optimization (MOOP) method related Cost-effectiveness analysis is adopted to select the optimal ones. We demonstrate the effectiveness of this approach by comparing the simulation and experimental results.

In this study, we present an omni-directional cane robot for aiding the elderly and handicapped people walking. The motion control problem is investigated for both normal and abnormal walking conditions. In the case of the user's normal walking aided by the cane robot, a concept called “Intentional Direction (ITD)” is proposed. Guided by the online estimated ITD, we apply the admittance control method in the motion control of cane robot. The gravity compensation is also considered because the user is possible to walk on a slope. For the abnormal walking, we mainly studied the case of user's falling down. The Center of Gravity (COG) of user can be estimated from the angle of an inverted pendulum which represents human model. Fall prevention algorithm based on the relationship between user's COG and the cane is proposed. This algorithm is also applied in the motion control of cane robot. The proposed method are verified through experiments.

This paper describes a 3-D biped walking over rough terrain. The robot is modeled as the special 3-D inverted pendulum that can change the length. The dynamics of the 3-D inverted pendulum is modeled as 2-D autonomous system by applying the Passive Dynamic Autonomous Control (PDAC) that is based on the assumption of point-contact of the robot foot and the virtual holonomic constraint as to robot joints. We analyze the stability of the 2-D autonomous system by use of Poincarée map, and derive the stable range over rough terrain. By applying the virtual compliance control to an actual robot “Gorilla Robot III”, the angle of the pendulum is modified. Finally, the 3-D biped walking over rough terrain is realized by use of the Gorilla Robot III.

This paper describes walk-to-brachiate transfer of a multi-locomotion robot (MLR). The MLR has multiple types of locomotion such as biped walking, quadruped walking and brachiation. This transfer is carried out through vertical ladder climbing as the robot must raise its body to start brachiating. As a result we have designed two stable transfer motions from walk to climb and from climb to brachiate, while contact situations and constraints of the robot are changing during the transfers. In addition, we have proposed a control algorithm by considering the reaction force from environment, and the setting of parameter is based on a kinetic model of the robot in order to tolerate relative position errors between the robot and its environments such as rungs of the ladder. The robustness of the designed motions with error corrections is experimentally verified.

This paper introduces the concept of semantic evaluation of Region of Interest (ROI) for intelligent robots. The intelligent robot must have the capability of understanding situations. The first step of understanding of the situation is to find where to focus on and how to behave. Focusing on some particular area or region needs selection of the objects of interaction relevant to the context. Moreover, the focused area needs to be semantically evaluated to quantify the semantic relations. In this paper, we first detect interacting objects based on dynamic interaction. Then we recognize probable objects using Dynamic Bayesian Networks. Using the probable objects and a mutual supplementation model, we determine the contextual object. We form ROIs based on possible combinations of objects and the contextual object. Finally, we semantically evaluate each ROI. Various experimental results are provided to illustrate our method.

The applicative field of activities of robots which have only one locomotion strategy is limited. As a mean of enhancing the mobile range, it is necessary to have various locomotion modes. Therefore, we focus on dynamic transitions between several kinds of locomotion modes adapting to environmental changes. In this paper, we aim to realize a stable locomotion along some unknown test courses with transition between biped and quadruped walks. To achive this transition, we propose a method to get environmental information and internal conditions. Robot plans locomotion based on recognition of test courses and estimate stability of walking using Bayesian Network. The effectiveness of proposed method is verified by experiments.

There has been increasing interest in a type of underactuated mechanical systems, mobile-wheeled inverted-pendulum (MWIP) models, which are widely used in the field of autonomous robotics and intelligent vehicles. Robust-velocity-tracking problem of the MWIP systems is investigated in this study. In the velocity-control problem, model uncertainties accompany uncertain equilibriums, which make the controller design become more difficult. Two sliding-mode-control (SMC) methods are proposed for the systems, both of which are capable of handling both parameter uncertainties and external disturbances. The asymptotical stabilities of the corresponding closed-loop systems are achieved through the selection of sliding-surface parameters, which are based on some rules. There is still a steady tracking error when the first SMC controller is used. By assuming a novel sliding surface, the second SMC controller is designed to solve this problem. The effectiveness of the proposed methods is finally confirmed by the numerical simulations.

This paper proposes a three-dimensional biped dynamic walking algorithm based on passive dynamic autonomous control (PDAC) which is previously proposed. The robot dynamics is modeled as an autonomous system of a three-dimensional inverted pendulum by applying the PDAC concept that is based on the assumption of point contact of the robot foot and the virtual constraint as to robot joints. Due to autonomy, there are two conservative quantities named ldquoPDAC constantrdquo, that determine the velocity and direction of the biped walking. We also propose the convergence algorithm to make PDAC constant converge to arbitrary value, so that walking velocity and direction is controllable. Numerical simulation results validate proposed algorithm.

There has been an increasing interest in a kind of underactuated mechanical systems, mobile wheeled inverted pendulum (MWIP) models, which are widely used in the field of autonomous robotics and intelligent vehicles. Robust velocity tracking problem of MWIP systems is investigated in this study. In the velocity control problem, model uncertainties accompany uncertain equilibriums, which make the controller design become more difficult. A sliding mode control (SMC) method based on a novel sliding surface is proposed for the systems, which are capable of handling both parameter uncertainties and external disturbances. By assuming the specially designed sliding surface, the proposed SMC controller is capable of eliminating the steady velocity tracking error. The asymptotical stability of the closed-loop system is achieved through selecting sliding surface parameters in terms of some rules. The effectiveness of the proposed methods is finally confirmed by numerical simulations.

This paper addresses a three-dimensional biped dynamic walking control based on Passive Dynamic Autonomous Control (PDAC). In our previous work, the robot dynamics is modeled as a two-dimensional autonomous system of a three-dimensional inverted pendulum by applying the PDAC concept. In addition, the convergence algorithm based on conservative quantities named ¿PDAC constant¿ was proposed, so that walking velocity and direction is controllable. In this paper, we apply our control framework to an experimental robot ¿Multi-locomotion Robot¿; then the performance and the efficiency of the proposed control algorithm are verified by experiments.

Drivers are distracted by the large amount of information displayed, which raises the driver's visual and cognitive workload. Conventional hierarchical searching, which invariably requires time even for those familiar with systems, is another factor. Our proposed user-adaptive interface consists of user modeling using a hidden Markov model (HMM) and symbol matching which is the reconfiguration of a preferred navigation sequence. The interface learns a user's navigation patterns based on accumulated navigation sequences. The navigation sequence is reconfigured by the user model and a clue. The symbol matching is performed in compliance with the user's selection and the navigation sequence is recommended to the user. Experimental results showed that reconfiguring the navigation sequence and switches reduced navigation reaction time.

This paper describes an approach for real time detection and evaluation of region of interest (ROI) based on saliency and relevance. In our approach, we have considered moving background updating technique to adapt with real environment with camera equipped with mobile robot. We have introduced a new decision making criterion which selects the relevant objects based on human psychology of relevance. We have provided some experimental results which prove to be promising and realistic with this approach.

An animal has a characteristic ratio of forefoot and rear legs so that its morphology can adapt to the living environment. Likewise, the structure of robot should be better fitted the locomotion in the working environment. This paper derives an optimal structure of the quadruped robot, which minimizes the sum of joint torques of the robot. Minimization of the joint torque allows to reduce the joint acceleration in walking motion, and hence to reduce energy consumption. Numerical simulation analyzed joint torques in each limb length and slope angle under walking on a slope. The optimal rate of rear leg length (RRL) is derived by the simulation as the physical structure. Our analysis suggests that the joint torque will increase as the slope angle becomes steeper in the case that the rear legs are shorter than forelegs. On the other hand, the joint torque will decrease as the slope angle is declined in the case that the forelegs are shorter than the rear legs. Experimental results validated the simulation analysis.

This paper introduces a vertical ladder climbing of the humanoid robot only by the posture control without any external sensors. The humanoid robot does not have any special structure for fixing the body to the ladder. The robot maintains the body on the ladder by its grippers like human does. As a problem of this locomotion, a free gripper position of the climbing robot is not controllable because a yawing of the robot body around the axis connecting a supporting gripper and foot on the ladder is not fixed. To solve this problem, the momentum around AOY caused by the gravity is used to control the yaw motion of the body so that the various gait such as pace gait and trot gait could be realized in a ladder climbing maneuver. The algorithm of ladder climbing with recovery motion is experimentally verified by using ldquomulti-locomotion robot(MLR)rdquo which is developed to achieve various types of locomotion such as biped, quadruped walking and brachiation.

This paper proposes a new evaluation method of feeling in switch-pressing motion based on the surface electromyogram (sEMG) signals. We introduce a hypothesis that one always predicts a necessary strength when exerting onepsilas strength to act on an object and some moderate differences between the predicted force and the result would lead to a sense of comfort. In order to validate the hypothesis, a series of experiments are conducted on switch-pressing motion and corresponding feeling. First, the predicted muscular force is estimated from sEMG which reflects our intension. Second, we derive the difference between the predicted muscular force and the actual reactive force of the switch (variation of difference from prediction - stroke pattern; D-S pattern). Finally, we evaluate the feeling evoked by the switch-pressing motion based on the D-S pattern. Experimental results show correlativity between evaluation by our proposed method and subjective evaluation, and validate our working hypothesis that some moderate differences between the prediction and the result would lead to a sense of comfort.

The purpose of the present study is to control human biological rhythm and life cycle by optimization of awakening timing. We developed a wearable interface for controlling awakening time named "BRAC (biological rhythm based awakening timing controller)". BRAC could estimate bio-rhythm by pulse wave from finger tip and send awake signal to user. An ordinary alarm clock operates according to set times that have to be set in advance. However, humans have a rhythm in their sleep, which affects one's sleep depth and wake-up timing. We consider the simplest way to control or reset human's bio-rhythm or life style is to optimize the awakening timing and the sleeping hours. We examined the relationship between controlling awakening timing based on autonomous nerve rhythm and equilibrium function. Our findings suggest indicate that the prototype "BRAC" could evaluate user's biological rhythm and awakes user at the time optimized for physical function of equilibrium.

This paper describes a control for the brachiation robot with holonomic constraint. In our previous work, the brachiation controller is composed of two actions: swing-back and locomotion. The purpose of swing-back is to excite a robot so as to achieve the locomotion successfully, while locomotion action is to move forward by releasing the ladder with the backward arm in the direction of locomotion and gripping the target ladder with another arm. However, the conventional control can not apply brachiation in irregular ladder. And the convetional control was not consider energy efficiency. Thus, a novel method to achieve brachiation in irregular ladder and an energy-efficient brachiation by exacting a swing-back during locomotion is proposed. Experimental results show that the proposed method can improve the locomotion action as much as over 30% in energy consumption.

This paper presents a problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Most work on chemical sensing with mobile robots assume an experimental setup that minimizes the influence of turbulent transport by either minimizing the source-to-sensor distance in trail following or by assuming a strong unidirectional air stream in the environment, including our previous work. However, not much attention has been paid to the natural environment problem. Modification Particle Swarm Optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charged PSO, which is another extension of the PSO, has also been applied to solve dynamic problems. Odor source localization is an interesting application in dynamic problems. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles. Before applying the algorithm for real implementation, some important hardware conditions must be considered. Firstly, to reduce the possibility of robots leaving the search space, a limit to the value of velocity vector is needed. The value of vector velocity can be clamped to the range [-Vmax, Vmax]; in our case for the MK-01 Robot, the maximum velocity is 0.05 m/s. Secondly, in the standard PSO algorithm there is no collision avoidance mechanism. To avoid the collision among robot we add some collision avoidance functions.

This paper presents a problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Odor source localization is an interesting application in dynamic problems. Modified particle swarm optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charged PSO which is another extension of the PSO has also been applied to solve dynamic problems. We adopted two types of modified concepts of PSO for a new algorithm in order to control autonomous vehicles in more realistic environment where a speed limitation of the robot behavior and collision avoidance mechanism should be taken into consideration as well as the effect of noise and threshold value for the odor sensor response, also positioning error of GPS sensor of robot. Simulations illustrate that the new approach can solve such dynamic problems in advection-diffusion odor model environment

This paper describes an extension of the flexible transfer system (FTS) with self-reconfigurability of the physical modular configuration. The FTS is a two-dimensional transfer system composed of many modules which transfer a pallet to the neighboring module. A decentralized control which we previously have proposed can generate feasible transfer paths with the adaptability to environmental changes, but the fixed configuration of modules causes the limitation of the adaptability. Therefore, this paper proposes a self-reconfigurable flexible transfer system (SRFTS) and the module reconfiguration method, and addresses the issue of the coevolution of the physical configuration and the control strategy. The self-reconfigurability overcomes the drawback in the conventional FTS, and improves the transfer performance and the fault tolerance. The effectiveness of the proposed method is illustrated by numerical simulations.

This paper proposes a self-organizing control strategy of a signal network for urban traffic flows. The signal network is described using a system of nonlinear oscillators with the nearest neighborhood coupling. The natural frequency, split and coupling constants of the signals are adjusted dynamically according to local information of traffic flows so that the desired network offset patterns are self-organized through mutual entrainment. We provide a preliminary analysis of the coupled oscillator system. Numerical simulations demonstrate the effectiveness of the proposed method under dynamical environmental changes.

The flexible transfer system (FTS) is a self-organizing manufacturing system composed of autonomous transfer modules, which transfer a pallet loaded with machining parts. In the paper the central issue is the improvement of the performance of the FTS. By learning automata (LA), the FTS can generate a quasi-optimal transfer path by self-organization of a multi-layered strategic vector field corresponding to a task, and can also recover the system performance against sudden environmental change, such as the malfunction of some transfer modules or the change of machining task. However, in the case where the FTS is composed of large number of modules, we find that it may not work sufficiently. We suggest an idea to improve the performance in such case, and verify the effectiveness by numerical simulation.

We present the design of our small robot MARS (micro autonomous robotic system) and basic experiments of group behavior pattern. MARS is small mobile autonomous robot. The robot has sensors, battery and infrared light communication device. In addition, the robot can get programs from the host computer (programmable). We focus on group behavior design (cooperation, competition coordination etc.) to use real robot MARS and we try to realize the phase change of the group behavior pattern. One of our ultimate dream is designing life-like robotics systems, such as ant or fish societies.

This paper proposes a flexible transfer system (FTS) as one of the self-organizing manufacturing systems. The FTS is composed of autonomous robotic modules, which transfer a palette carrying an object. Through the self-organization of a multilayer strategic vector field corresponding to a task, the FTS can generate quasi-optimal transfer path in fully distributed way. We apply the learning automata for path generation algorithm. Simulation is conducted to evaluate the basic performance of the system and the results show the feasibility of application. Also, the developed hardware is explained.

The flexible transfer system (FTS) is a self-organizing manufacturing system composed of autonomous robotic modules, which transfer a palette carrying machining parts. The central issue is realization of both higher efficiency and flexibility to cope with environmental change, such as a sudden change of machining plan or breakdowns of the modules. Through the self-organization of a multi-layered strategic vector field corresponding to a task, the FTS can generate a quasi-optimal transfer path with learning automata. Also, the optimal planning is attempted by use of genetic algorithms, and is based on the global information on the system. We propose a hybridization method between the distributed and centralized approaches. Simulation is conducted to evaluate the basic system performance and the results show the effectiveness.

Many studies on swarm intelligent systems have been presented. However, analytical treatment on swarm intelligence has not been performed sufficiently, because of difficulties in finding general criteria to evaluate system performance. In this paper, we regard flexibility as one property of the robustness, and evaluate the flexibility of swarm intelligent systems. We propose indexes to evaluate the behavior of swarm intelligent systems, which focus an "flexibility". The proposed indexes provide a way to compare the performance between different swarm intelligent systems. We apply the indexes to evaluate two swarm intelligent systems using computer simulation, and discuss the results.

This paper proposes a system, which manages both efficiency and flexibility in the manufacturing system. It has been considered that an autonomous distributed system is superior to a centralized system in terms of fail-proof expansion, load dispersion, flexibility, and the possibility of the evolution. However, it is difficult to manage both efficiency and flexibility according to conventional method in an autonomous distributed system. To solve the problem, we propose a method. The method is that the carrier organizes the whole number of the carrier in the system, according to the volume of the task and the complexity of it. And we evaluate the proposed method of self-organizing behavior by a multi-AGV autonomous system in the quantitative dynamic environment. It shown that the system has higher efficiency in this system than in the centralized system with self-organization in the quantitative dynamic environment.

This paper deals with a modeling and controlling strategy for multi-robot group behavior. While distributed autonomous robot system has advantages in flexibility of the system, it has also difficulties in coordinating the global system behavior as expected by the designer. Our research is to develop an approach in dealing with this problem. Self-organization is a natural principle which can be seen in the dissipative system. We present a hybrid top-down and bottom-up approach and a basic model for coordinating a macro scale group behavior based on the self-organizing mechanism. In particular, we realize the self-organization of temporal behavior patterns.

Distributed autonomous robotic system (DARS) has advantage in flexibility, comparing with central control system. The former researches of DARS mainly deal with the system which is composed of homogenous robots. Cellular robotic system (CEBOT) is one of the distributed robotic system which consists of heterogenous robots and they can change their function by means of connection or disconnection of robotic units called "cell". Functional reconfiguration increases the adaptability against the change of environment further, but it has specific problem concerned with the cost for changing its function. This paper deals with the behavior and the property of system that is composed of functionary reconfigurable robots and we focus on the effectivity of dynamic reconfiguration and the property of system, especially the trade-off between the effectivity of reconfiguration and the cost of reconfiguration through computer simulation by using a model of load transport in the production system.

This paper describes a scheme to enhance adaptability of the multiple robot system using prediction results. The main purpose of this paper is attempt to construct a prediction model, which actively incorporates knowledge of the environment and reflects reasoning results of the conditionally dependent behavior pattern, not only based on the conventional statistical analysis. The presented method will meet the demand and contribute to more efficient decision making in the dynamical environment. Also, some remaining problems are mentioned finally.

This paper deals with a criterion to reduce excessive communication in the Cellular Robotic System (CEBOT). While communication plays a significant role in acquiring necessary information for the decision making, we should consider its cost especially in a case that a large number of agents are involved. Therefore, we attempt to achieve the "intelligent communication" that is defined as accumulation of knowledge bringing higher performance and less frequency in the robot communication. For acquiring information through the communication, a probability model is constructed. The decision is presumed based on the model but the risk for that is evaluated as to whether the communication is negligible. We show that there exists correlation between accuracy of the estimate model and communication necessity.>

This paper describes a predictive decision making strategy for the multiple robotic systems. Although communication plays an important role in the robot system, it tends to be regarded as merely information exchanges by data transmission. However, it is one of the vital intelligent activities in the multiple robot systems. In this paper, we propose a framework of "intelligent communication" which treats communication as an essential part of decision making considering interaction with the other agents. It includes knowledge abstraction from discrete events, intention reasoning and prediction, etc. This paper focuses on the intention reasoning with prediction, and proposes the concept of hierarchical prediction model for its realization. We discuss the advantage of prediction and its difficulty for application to the robot through the simulation results.>

Presents a new approach to communication in an autonomous distributed system, focusing on the cellular robotic system (CEBOT). The CEBOT is a self-organizing robotic system composed of a large number of robotic units (cells) with a simple function. In order for cellular robots to implement a task efficiently, cooperation and coordination among cells are of great importance and require efficient communication. Since communication takes place in the process of task execution, it is likely to be intensive and biased by the small number of cells related to the task. This could decrease communication efficiency. To deal with these problems of robot communication, the authors propose demand-based intensive communication (DBIC), and show its effectiveness with simulation and experimental results for the prototype CEBOT Mark IV.