We present a novel acoustic camera simulator (ACSim) that generates realistic sonar images by incorporating recursive ray tracing and sonar artifact modeling and provides various ground truth labels, enabling benchmarking and learning purposes. The 2D forward-looking sonar (FLS), also known as the acoustic camera, produces high-quality 2D images. Conducting real-world underwater experiments is challenging, making realistic sonar image simulation a necessary alternative. However, existing simulators often lack sufficient realism or are limited to specific scenes and phenomena. As a result, training on simulations and testing on real sonar images (i.e., sim-to-real) remains an open problem for deep learning-based applications. Our work introduces a novel sonar simulator with a customized rendering engine. We use recursive ray tracing to model multipath reflections in arbitrary scenes and propose physics-based shading for intensity computation. We propose a resampling method for anti-aliasing and model significant artifacts such as rolling shutter distortions and cross-talk noise. The simulator provides various ground truths for benchmarking and deep learning applications. We tested several tasks by training on synthetic images and demonstrated that the models also work on real images. We developed a Blender add-on for an enhanced user interface and will make the simulator open-source to advance future research.

Model-based control can provide high-accuracy performance over position-based or velocity-based control. Therefore, to employ model-based control in industrial robots, it is important to estimate the dynamic parameters as accurately as possible. However, traditional estimation methods, such as least squares (LS), are not sufficiently accurate, and the feasibility of dynamic parameters cannot be guaranteed. In this article, an iterative hybrid least square (IHLS) algorithm is proposed to estimate the base parameters of industrial robots by dividing the identification processes into two loops. The inner loop integrates a linear matrix inequality with the semidefinite programming technique to guarantee physical feasibility and reuses the torque deviations between the measured torque and predicted torque to estimate the base parameters of the robot, while the outer loop substitutes the Stribeck friction model for the Coulomb-viscous friction model to estimate the joint friction torque. Moreover, backpropagation neural network (BPNN) is introduced to further estimate the joint friction torque based on the Stribeck friction model. Experiments are conducted on two industrial robots, and four methods are compared in dynamic parameter identification. Experimental results show that the hybrid approach of the IHLS algorithm with the BPNN has the best performance among the four methods.

In the super-aging society, it is necessary to understand gait initiation (GI) to solve motor control problems faced by older adults and hemiplegic patients. The purpose of this paper is to confirm that Anticipatory Postural Adjustments (APAs), which are considered important for understanding GI, are effective for GI. Using musculoskeletal model and control models for standing, GI, and gait, we simulated standing, GI, and steady gait. We found that even though the GI control proposed in this paper was not designed to produce APAs, APAs appeared as a result of optimization. This suggests that APAs may be an effective motion in GI.

Muscle tone contributes to the maintenance of standing posture against external forces, but the effects of changes in the magnitude of muscle tone in the arms are not fully clear. This study aimed to investigate the effects of increased muscle tone in the arms during postural recovery from external forces. Perturbed stance simulations, in which a neural controller controlled a musculoskeletal model under three different arm muscle tone conditions were performed. The lower body muscles were controlled by the sum of constant values that corresponded to muscle tone and feedback control components, based on muscle length and lengthening velocity. Additionally, the arm muscles were controlled by the sum of constant values and feedback control components based on head and angular acceleration. The control parameters were optimized to minimize center-of-mass movement. Consequently, the center-of-mass motion was maximized under conditions of high arm muscle tone. This suggests that an increased arm muscle tone is detrimental to the maintenance of standing balance.

In an aging society, it is increasingly important to solve problems related to human gait. One of these issues is the relationship between gait and muscle tone. In this study, we propose a neuromusculoskeletal model that can be used to represent and evaluate the movement changes generated by changes in muscle tone in human gait using computational simulations. The model is based using a musculoskeletal model with 70 muscles and a neurological controller model that can represent muscle tone. As a hypothesis to judge the validity of the model, a high muscle tone makes it difficult to maintain gait and makes the stride length narrow. A total of 1470 parameters were optimized for 3 days for each trial with varying muscle tone. As a result under the condition of high muscle tone, the gait maintenance time became shorter, and the stride length became narrow compared with the experimental value. Therefore, the hypothesis used to judge the validity of the model that gait maintenance becomes difficult and stride length becomes narrow under high muscle tone was proved. In addition, the tendency for changes in the angle of the knee joint, the distance traveled by the center of the foot pressure, the number of times the foot touched the ground, the distance the foot swung forward, the regularity and periodicity of each gait, and the bimodality of foot pressure were evaluated and confirmed to be natural. From these results, we conclude that the neuromusculoskeletal model for gait proposed in this study was proper.

The development of body sensor networks (BSNs) with rich multimodal signals has enabled highly accurate fine-grained action detection, which is the cornerstone of many humancomputer interaction applications. However, in the case of consecutive fine-grained actions, most existing wearable sensor-based detection methods are constrained by sliding windows because of their limited temporal receptive fields, and existing sequence-to-sequence detection methods cannot effectively leverage the potential of multimodal information of wearable sensors. Herein, to give multimodal signals full play in fine-grained action detection, we propose a novel temporal convolutional network by designing a channel attention-based multistream structure. We apply it to a promising application for correct and incorrect patient transfer nursing action detection. A dataset is collected from a BSN on a patient when nurses perform patient transfer. Extensive experiments on our dataset and public dataset (C-MHAD) demonstrate that the proposed method is superior to the state-of-the-art methods, because it can strengthen the utilization of prediction features from the more convincing modal stream at each time frame.

We present a highly accurate and efficient, yet simple, two-stage voting scheme for distinguishing inlier 3D correspondences by densely assessing and ranking their local and global geometric consistencies. The strength of the proposed method stems from both the novel idea of post-validated voting set, as well as single-point superimposition transforms, which are computationally cheap and avoid rotational ambiguities. Using a well-known dataset consisting of various 3D models and numerous scenes that include different occlusion rates, the proposed scheme is evaluated against state-of-the-art 3D voting schemes, in terms of both the correspondence PR (precision-recall) AUC (area under curve), and the execution time. A total of 374 experiments were conducted for each method, which involved a combination of four models, 50 scenes, and two down-samplings. The proposed scheme outperforms the state-of-the-art 3D voting schemes in terms of both accuracy and speed. Quantitatively, the proposed scheme scores 97.0% ±12.9% on the PR AUC metric, averaged over all of the experiments, while the two state-of-the-art schemes score 74.2% ±22.2% and 78.3% ±26.4%. Furthermore, the proposed scheme requires only 24.1% ±6.0% of the time consumed by the fastest state-of-the-art scheme. The proposed voting scheme also demonstrates high robustness against occlusions and scarce inliers.

In this paper, we propose objective functions to estimate the principal contact between a unknown manipulated target object and its unknown surroundings from the motion of the object. We derived the objective functions based on the fact that contact involves a pair of geometrical primitives (a point of vertex, a line of edge, and a plane of face) for the singular condition of the calculation for their intersection or their spanned space from the point of view of geometrical algebra. The minimization of the proposed objective functions, which are differential quadratic forms of the Kronecker product of geometrical parameters, efficiently provided us the contact geometries that constrained the object movements. Additionally, the proposed objective functions are fundamentals for identifying contact during compliant manipulation, and we showed that the objective functions provide a clue for contact identification via experiments.

A nursing action evaluation system that can assess the performance of students practicing patient handling related nursing skills becomes an urgent need for solving the nursing educator shortage problem. Such an evaluation system should be designed with less hand-crafted procedures for its scalability. Additionally, realizing high accuracy of nursing action recognition, especially fine-grained action recognition remains a problem. This reflects in the recognition of the correct and incorrect methods when students perform a nursing action, and low accuracy of that would mislead the nursing students. We propose a multi-attention deep recurrent neural network (MA-DRNN) model for nursing action recognition by directly processing the raw acceleration and rotational speed signals from wearable sensors. Data samples of target nursing actions in a nursing skill called patient transfer were collected to train and compare the models. The experiment results show that the proposed model can reach approximately 96% recognition accuracy for four target fine-grained nursing action classes helped by the attention mechanism on time and layer domains, which outperforms the state-of-the-art models of wearable sensor-based HAR.

This paper presents a contact-event-triggered filter using only a force-torque sensor with impedance control for non-cooperative, rotating, heavy object capture. Contact events are modeled for prediction, and detected to trigger the particle filter's updating process. By combining these features, a computationally efficient, contact-event-triggered filter is proposed. For our purpose of capture using impedance control, expected contact events, collisions and sliding are defined for prediction and detection. This novel method is implemented in an air bearing robotic system, and has demonstrated its superiority with the highest success rate (100%) for sliding contact mode cases, whereas the previous method could only yield a success rate of 87.9%. The computation resource is demonstrated to be limited, with a computation time of 4.2 milliseconds on average and 8.3 milliseconds at worst.

In this paper, we propose an optimization method to determine the design parameters comprising joint displacement parameters in a serial manipulator having small degrees of freedom realizing an approximated target trajectory. Using generalized differential kinematics, we can achieve efficient optimization without solving the inner-loop optimization required to obtain the reachable points.

This paper proposes real-time collision-based contact mode estimation with only a force-torque sensor for capturing a moving rigid body. The contact modes are defined for determining when to generate the signal to close the robotic hand for establishing object closure. In our particle filter approach, collision-triggered filter is used to determine the contact mode with the least amount of computation. Brach's collision model is used for our collision model-based approach for a rigid body because it is computationally light-weighted and enables the sampling of three collision properties for the particle filter. The validity of our method is experimentally demonstrated by achieving the highest success rate using the reasonable computation resources required (average of 3.9 milliseconds and worst of 6.1 milliseconds with our setup), and verifying each computation resource (or number of particles) based on the size of motion estimation error in the pre-capture phase.

This paper reports the latest progress of an ongoing project utilizing a ducted-fan propulsion system to improve a humanoid robot's ability to step over a broad ditch with a height difference between the two sides. This work focuses on the methods of calculating the boundary and optimizing stepping posture to use less thrust and keep the robot balanced while stepping over the ditch. With the proposed methods and new two-dimensional gaits, the prototype robot, named Jet-HRl (Jet Humanoid Robot ver.l) was able to completely step over a broad ditch with 450mm in width (up to 97% of the robot's leg's length), and a height difference of 100mm between two sides.

The main function of robots is to assist humans with tasks. We can find successful examples in factories and hospitals. Yet, service mobile robots, designed to help humans in houses, are still in its early stages. The current state of service mobile robotics inspired me to start my research in this field. First, during my Master thesis I developed a semi-direct teaching method for robots. The method conveys human knowledge on how to grasp objects through the use of a teaching tool. I evaluated the method by teaching mobile robots with a parallel gripper. The system met the proposed targets, yet, the results showed me that small robots with limited capabilities cannot be used for many tasks.

A teaching-playback robot manipulator system whereby the user controls the manipulator through a teaching pendant has been used widely in industrial applications. Kinematic singularity issue becomes an important problem in the control of robot with a teaching-playback system. In this paper, we propose and investigate three singularity avoidance methods for a teaching-playback robot manipulator system. Nonredundancy singularity avoidance (NRSA) attempts to reduce both the position and orientation errors of the end-effector with the same priority. Redundancy singularity avoidance (RSA) attempts to reduce the position error of the end-effector with the first priority and reduce the orientation error of the end-effector with the second priority; Both NRSA and RSA are based on a modification of a Jacobian matrix. Point-to-point singularity avoidance (PTPSA) makes the end-effector pass through a singular region based on joint-interpolated control without maintaining the position and orientation of the end-effector. Experimental case studies are developed to investigate the manipulator performance when the end-effector approaches the wrist and shoulder singularity. The maximal end-effector trajectory error and users' feelings are statistically evaluated and analyzed in the experiment. The results of the experiment show the effectiveness and practice of the proposed methods.

Task-models concretize general requests to support users in real-world scenarios. In this paper, we present an IR based algorithm (IRTML) to automate the construction of hierarchically structured task-models. In contrast to other approaches, our algorithm is capable of assigning general tasks closer to the top and specific tasks closer to the bottom. Connections between tasks are established by extending Turney’s PMI-IR measure. To evaluate our algorithm, we manually created a ground truth in the health-care domain consisting of 14 domains. We compared the IRTML algorithm to three state-of-the-art algorithms to generate hierarchical structures, i.e. BiSection K-means, Formal Concept Analysis and Bottom-Up Clustering. Our results show that IRTML achieves a 25.9% taxonomic overlap with the ground truth, a 32.0% improvement over the compared algorithms.

A topic model capable of assigning word pairs to associated topics is developed to explore people's activities. Considering that the form of word pairs led by verbs is a more effective way to express people's activities than separate words, we incorp

A semi-direct teaching method for specifying information for grasping objects by mobile robots in home or office environments through the generation of a teaching data is proposed in this paper. Information specified during the teaching process includes: object's shape, grasping force, grasping position, and orientation. To achieve this data indication, we propose the use of a teaching tool which has the same mechanism as the hardware placed on the robot (gripper). This enables our system to carry out the teaching process without using the robot. In the experiments, teaching data was generated for three objects, and our approach method was evaluated to determine the accuracy of the measurements obtained.

In warehouse design, designers have to consider complex systems including temporary storage area, where loads are placed when they are passed from one machine to another. In this paper, a queuing network model is proposed to calculate the temporary storage area required for a warehouse. Previously, the warehouse design was made difficult because the design constraints and parameters were inter-related. In this paper, warehouse system is modeled as a network of nodes and the temporary storage area is expressed as a queue within each node. We used a typical warehouse system as a case study to evaluate the effectiveness of this design method. The proposed method can simultaneously determine the required temporary storage area, number of machines and long term storage area. Layout constraints can also be taken into consideration with this method.

For motion coordination of a multi-agent system, a simulation-based rule generating method is needed. However, previous studies assuming understandabieness of calculated rules for users do not exist. In this paper, a simulation-based simplified and robust rule generation system for multi-agent scheduling problem is proposed using parallel algorithm discovery and orchestration (PADO) and simulated annealing programming (SAP). In addition, a method to extract constraints of rules from a simulation is also proposed. This proposed method is evaluated with an aircraft control problem, and robust rules can be calculated. Moreover, with the method to extract constraints, the average calculation time can be 80% less than that without the proposed method.

We propose a topic model capable of generating tri-layer clusters, each of which is composed of a topic layer, an activity layer and a word layer. The objective is to better predict activities involved in documents by considering general topics of the activities for clustering. The proposed model is a supervised topic model based on the Latent Dirichlet Allocation (LDA). As a follow-up study of word-pair generation LDA (wpLDA) model, the model introduces the topic-specific activity distribution as an external input, with an activity node inserted into the main generation thread. In addition, we refer to D. Ramage et al.'s one-to-one correspondence to directly learn word-activity tags. An experiment was conducted to prove the feasibility of this model. We chose ten top-listed activities from the wish clusters obtained by the previous wpLDA research, and used each as the key words to extract thirty tweets for training and five for testing, respectively, tagging the tweets with the corresponding activities. By applying the proposed model, we obtained the expected tri-layer clusters in the training phase. Then, in the testing phase, we utilized the activity-specific word distribution derived from the training results to learn the activities of the testing documents. The Stanford Classifier was put forward as the control group, and the activity prediction accuracy demonstrates that the proposed model exhibits the superiority in multi-activity prediction.

In super aged society, system that assists social activities of older adults is needed for cognitive enhancement. Group conversation is one of the social activities. One of the largest problems for older adults is the imbalance of participation to conversation. Our approach is to develop a system that assists group conversation of older adults, by evaluating speech duration and facial expression of each participant during conversation. It enables all participants evenly to take part in the conversation, which tends to be difficult for older adults. We analyzed 15 group conversations for calculating the parameters used for evaluating the conversation. The effectiveness of the system using the obtained parameters was validated through the experiment. The system which evaluates only speech duration of each participant and the system which evaluates both speech duration and facial expression of each participant during group conversation were compared by analyzing 8 group conversations for each. The results demonstrated that the system which evaluates both speech duration and facial expression of each participant can make all participants to take part in the conversation evenly and actively. We successfully developed the system that can support group conversation regardless of the contents.

We demonstrate a generative model that incorporates word-pair connection into the smoothed LDA model to intuitively discover people's wish related activities. The widely used model, LDA topic model, generally generates clusters in the form of separate words. However, this form is not intuitive enough to express people's activities. Therefore, we consider the word-pairs led by verbs can better describe users' intentions and activities, and we prefer to present this collocation under topics as the clustering results. We mathematically present the relatedness between verbs and non-verb words through association rule, and build the physical connection of word-pairs and possible topics. By incorporating the connection lattice into the smoothed LDA, the word-pair LDA model is created. In the experiments, Twitter posts about “new year's resolutions” were chosen as the data source. The results show that the proposed model performs well on perplexity, and presents excellent intuitive character.

Minimizing the task completion time of manipulator systems is essential in order to achieve high productivity. In this paper, this problem is dealt with by utilizing the redundant degrees of freedom (DOF) of a given task and the tool attachment optimization. For example, in a vision-based inspection where a camera is held by a manipulator, the extra DOF can be brought about by allowing the camera to be translated along its approach axis or rotated about this axis when capturing images. Furthermore, the manipulator end-effector position and orientation is optimized by designing an additional linkage at the manipulator end-effector which is called a tool attachment. A 7-DOF manipulator system is used in the simulations to verify the proposed approach. Results showed that this approach can minimize the task completion time by about 17% compared to conducting only motion coordination.

Ensuring fault tolerance of robotic systems is a challenge for factory automation. This paper focuses on multi robot manipulation and maintenance for fault-tolerant systems. For this purpose, a manipulation strategy of multiple mobile robots, that enables a system to continue operating even if a working robot undergoes preventive maintenance or fails and undergoes corrective maintenance, is implemented. In addition, a robot failure and a maintenance policy for preventive and corrective maintenance activities are mathematically modeled on the basis of reliability engineering. Thus, working robots are allowed to undergo preventive maintenance at an optimal interval and corrective maintenance each time they fail. Finally, through simulation experiments, the effectiveness of an integrated multi-robot manipulation and maintenance in industrial applications is shown.

This paper focuses on the effectiveness of maintenance in fault-tolerant multi-robot systems. Such a system is enabled to work as long as one robot works; namely, completely parallel. For this system, it is required to ensure fault tolerance and maintain high performance taking robot failures and maintenance, such as prevention and correction of robots, into account. Therefore, we propose an optimal maintenance strategy on the basis of reliability engineering. This strategy enables robots to undergo preventive maintenance at optimal intervals and corrective maintenance each time they fail. Through simulation experiments, the effectiveness of the optimal maintenance strategy is investigated. In addition, the influence of robots undergoing maintenance on system performance is mathematically and experimentally discussed on the basis of the number of robots and maintenance strategies.

Task completion time and cost are two significant criteria for the selection of manipulator system. For a given task, several Pareto solutions of manipulator systems should be derived based on the evaluation of these two criteria. However, this process requires a large calculation time. In this paper, we propose a method that can select appropriate systems by evaluating task completion time and cost within the desired calculation time. In the proposed method, multiple objective particle swarm optimization (MOPSO) is employed to search for appropriate manipulator systems from a set of candidate systems. Location optimization and motion coordination are integrated to derive the task completion time and the relative cost is used to evaluate the cost of a manipulator system. We employ particle swarm optimization (PSO) for location optimization and use nearest-neighborhood algorithm (NNA) for motion coordination, since PSO and NNA have a high speed of convergence to a good solution. The proposed method is applied to a set of tasks and is proved to be effective and practical.

This paper describes a methodology regarding an autonomous cruise control (ACC) of circulating multi-robot which is directly effective for solving the congestion regardless of the presence of a bottleneck. For this purpose, we focus on an external interaction force between the robots. This force is generated with the use of a virtual damper. In this methodology, two control scenarios are presented: a damping force acts on a moving robot as the external interaction force, (I) only when its preceding robot(s) is/are stopping or being congested and (II) when a robot exists in front of the moving robot, in order not to become involved in the congestion. This paper deals with a circuit and a robotic manufacturing-transportation system for industrial automation. Through simulation experiments, it is shown that the proposed ACC successfully solves the congestion, and finally, improves the throughput. In addition, the superiority of the two control scenarios, ACCs (I) and (II), is discussed.

When a male silk moth senses sexual pheromone of a female partner by using its antenna, it repeats certain series of walking pattern and arrives to the partner. This walking pattern is generated in Lateral Accessory Lobe (LAL) and the ventral protocerebrum (VPC) domain which controls physical exercise. Therefore, in this study, we elucidate the process of this behavior by constructing a neural network model of the LAL domain. Concretely, we build a model that treats some numbers of neurons as one neuron and estimate strength of each connection between 10 neuron representatives of neuron groups with Genetic Algorithm. The estimated network is verified and consided from engineering and biology.

In this paper, we focus on a batch manufacturing system with multiple industrial robots. In this system, material-handling robots (MHRs) and material-processing robots (MPRs) are operating. Since various operations are conducted at certain places, one of them might include a localized bottleneck. A localized bottleneck is a constraint that dominates the maximum amount of production in a system, that is, the productivity. In addition, the bottleneck induces congestion; as a result, the productivity declines. For these issues, there are two primary challenges: bottleneck prevention and restraint. To solve them, suitable operational techniques with respect to the MHRs and MPRs are needed in order for them to operate appropriately while relating to each other. In this paper, a constructive approach toward multi-robot coordination problems is taken. In this approach, we propose applicable operational techniques for the robots. Through simulation experiments, we examine the effectiveness of the proposed techniques and their combinations and, finally, show an integrated operational technique. Each of the operational techniques solves a localized bottleneck and the congestion, and the integrated technique successfully improves the productivity and results in the most efficient system.

The minimization of task completion time of robot arms has been an extensively studied area in robotics. Previous researches mostly focused on optimization methods for the motion planning and collision avoidance, which did not involve any modifications in the hardware design of a robot arm. Some researches, on the other hand, fully design a specialized robot arm for a given task. In this study, we propose a hybrid design composed of a hardware design and an optimization method. The hardware design is a tool attachment, which is a fixed linkage attached between the end-effector of a robot arm and a tool. In the optimization method, we incorporate base placement design, goal rearrangement and collision avoidance through motion coordination in order to minimize the task completion time of a robot arm. Our proposed design is tested using a 6-DOF robot arm and a 1-DOF rotating table. The method is evaluated over a single task and a set of tasks showing its effectiveness and applicability for practical applications.

Long-time exposure to repetitive or monotonous work is associated with increased risk for musculoskeletal disorders that are due to muscle fatigue. Previously, researchers reported that muscle fatigue can be estimated using a low-frequency band of an SEMG signal. However, these studies were performed exclusively during static contraction tasks. The objective of the present study was to evaluate and determine the frequency range for a low-frequency band. In addition, the performance during dynamic contraction tasks was analyzed. A group of healthy university students (8 males) was recruited, and endurance handgrip tasks were conducted. SEMG signals were captured from the forearm muscle. The frequency range for the low-frequency band was redefined as 5 - 40 Hz. The results from a dynamic contraction task indicated that a low-frequency band is a reliable method for indexing muscle fatigue from SEMG signals.

A work cell is generally designed to achieve a high throughput and its size is typically viewed as contingent to component sizes. In this paper, we aim to design a compact work cell (spatial requirement) and to minimize its task completion time (temporal requirement) to a value set as a constraint. By doing so, a work cell occupies a minimal space and achieves its desired throughput. The work cell size is evaluated based on the size and the swept volume of components. This evaluation is important since a robot arm can have a very large swept volume depending on a given task. To satisfy the spatial and temporal requirements, we propose the integration of the base placement optimization, goal rearrangement, and motion coordination between the robot arm and the positioning table. Furthermore, we introduce two motion coordination schemes based on the spatial and temporal requirements. We showed the effectiveness of the proposed method through simulations.

This paper focuses on an automated batch manufacturing system with material-handling robots (MHRs) and material-processing robots (MPRs). In this robotic manufacturing system, materials transported by the MHRs are processed by the MPRs. These operations cause a bottleneck in the system. Furthermore, the bottleneck induces congestion of the MHRs. In the system, the effect of an operational delay due to bottlenecks affects the entire operation. Accordingly, there is an event in which the congestion extends and the system throughput not only fails to increase but also may become worse, even if more robots are used to improve the productivity. For this challenge, we propose a behavior control method for the MHRs to eliminate or ease the congestion that arises from a bottleneck. Each MHR controls its own behavior adequately by using the external force of a virtual damper in order not to become involved in the congestion. Finally, through a simulation experiment, we show that the proposed control method improves the system throughput and its effectiveness for a more efficient system operation.

The minimum-time motion coordination is an important subject in robotics. In this study, the arrangement of several goals, which is treated as a traveling salesman problem (TSP), is incorporated to this subject. Although TSP has been studied in most previous works; but, solving a TSP that takes into account collision occurrences has not received much attention. This instance arises when a robot arm has to plan a sequence of reaching goals with other moving objects and/or other robot arms. If goals are also moving, then the problem becomes more complex since the end configuration of robot arm is undefined when reaching a goal. In this study, in particular, a 6-DOF robot arm has to reach several goals found in an object while a 1-axis positioning table simultaneously positions the object; thereby changing the goal locations and collision occurrences are inevitable. For the purpose of this study, the TSP is solved effectively with motion coordination and collision avoidance. The collision-free configurations of a robot arm when reaching goals are solved through motion coordination. Collision is avoided by exploiting the redundancy of the system. The above-mentioned solution is verified through a simulation utilizing an object with various numbers of goals and their positions, and is proven effective.

We have proposed “attentive workbench (AWB),” an assembly cell that supports the production activities of human workers. Attentive Workbench is composed of an augmented desk interface and self-moving trays with the Sawyer planar motors. In this paper, real assembly experiments using the implemented AWB system are carried out. The merit of the physical assembly support by the proposed system is evaluated quantitatively in the view of necessary time for product assembly.

When robot maintenance impedes the operation of other robots in a seaport container terminal, the operational efficiency is reduced. Therefore, robot reliability is necessary. In this paper, we propose a reactive robot control system with hybrid operational models for flexible and efficient container handling in consideration of robot reliability. For this purpose, we focus on three operational models in the states of normal, preventive, and corrective maintenance in order to utilize the mutual substitutability of the operation among robots. By applying hybrid operational models, each robot is able to respond to the dynamically changing states reactively. Finally, we discuss the effectiveness of the proposed system in a seaport container terminal.

In a warehouse, efficient product transportation is required for high productivity. In general, it is recognized that mixed-load transportation can decrease the moving time to some extent. However, many scheduling methods without regard to mixed-load transportation have recently been proposed. In this respect, we propose a design for scheduling problems with mixed-load transportation handled by vehicles. The proposed method uses two well-known dispatching rules and a local search method based on a simulated annealing algorithm. Two rules are applied to obtain the initial solution while ignoring the mixed-load transportation, and the local search method is applied to improve the initial solution to obtain a semi-optimal solution in consideration of mixed-load transportation. To evaluate the applicability of the proposed system, we implemented our system with several product transportation environment. The simulation results verify the effectiveness of the proposed method.

We aim to realize an intelligent robotic system that delivers necessary objects to human desk-workers in home or office environments. In order to meet this goal, we propose methods to understand a request for support of a user of the system. We adopt an approach that the system measures the userpsilas hand and eye movements in reaching for an object to detect and understand the request. Based on the detection and understanding of the request, the system delivers necessary objects to the user using self-moving trays. The experimental results demonstrate the advantage of the methods proposed here.

Nurses are with the primary mission to provide quality services to patients up to 24 hours a day. In general, different nurses (especially between nurse experts and novices) handle the pre-assigned nursing cares in different ways, which directly affect the quality of nursing cares. In this respect, it is necessary to quantitatively elucidate the nursespsila action rules for effectively nursing instruction and quantitative evaluation of staffing levels. In this paper, we will analyze the daily nursing cares to quantitatively illustrate the implicit action rules of nurses on their provision of cares from the viewpoint of scheduling. Unlike the conventional methods mainly based on the interviews/dialogues, we hypothetically model the nursespsila action rules as some traditional dispatching rules, and elucidate the nursespsila action rules by evaluating the similarities of the actual nursing care schedules with planned nursing care ones. By analyzing several actual observed nursing cares, we quantitatively elucidate the action rules of veterans and novices, and furthermore, we compared their differences on the rules.

In most Japanese hospitals, different nurses handle the pre-assigned nursing cares in different ways, which directly affect the quality of nursing cares. To our knowledge, there has been less attention on ensuring that nurses provide nursing cares in a timely and accurate fashion. Consequently, in this paper, considering the similarities to the traditional job shop scheduling problems, we will model the daily nursing care scheduling problems and propose an efficient scheduling method based on simulated annealing algorithm. By iteratively local searching based on simulated annealing: (1) permutating the tasks from one nurse to another and (2) permutating the sub tasks handled by a nurse from its original position to another new one, the proposed method is evaluated to be applicable to the nursing care scheduling problems (providing comprehensive, coordinated and cost effective nursing cares to patients).

We address a rearrangement task by multiple robot in this paper. A rearrangement task has constraints regarding the order of the start, grasping and finish time. Calculating these constraints has a high computational cost. We propose a rearrangement method that calculates constraints efficiently. In our approach, not all constraints are calculated, but some of them will be calculated step by step. The proposed method is tested in a simulated environment with up to 4 mobile robots. The methods are compared, and the results indicate that the proposed method is superior.

Automation of transportation systems and promotion of the operations are becoming an international demand on seaport container terminals. For this issue, we propose an integrated design methodology. In other words, in addition to the design of the appropriate number of machines, efficient system layout, and system management models, we attempt to design machines' specifications, which are operating in an automated transportation system. The objective of this study is to maximize the system efficiency while minimizing the changes of the specifications. Therefore, we evaluate the specifications based on the system throughput of the constructed system; then, make clear the impacts of the specifications on the system. Finally, for imposed demands, we design highly efficient transportation systems. From the construction costs and design results, we show a need to take into consideration the machines' specifications.

For the design of an automated transportation system in an actual seaport container terminal, it is necessary to take into consideration the maintenance of operating robots (AGV: Automated Guided Vehicle and ATC: Automated Transfer Crane). For this purpose, we develop an operation model in which each robot enters a maintenance mode while operating on the basis of its reliability. We also aim to design the mean time between failure (MTBF) which is a kind of a measure of robot performance on the basis of the robots’ reliability as well as the number of robots. However, this is a combinatorial design problem. Therefore, we propose a design methodology in order to derive one combinatorial design solution for a given demand by considering a system management cost that includes a system construction cost and a penalty cost. The designed systems are evaluated on the basis of the system management costs. Finally, we present the validity of the proposed design methodology and designed systems.

We aim to realize a robotic system that hands over a necessary object to a user as soon as he/she attempts to reach out his/her hand for it. In order to realize such system, the following is required: 1) detection of the reaching movements, 2) prediction of the target object among multiple objects, and 3) handing-over the object to a user. In this paper, the first two of them are discussed. To detect the reaching movements, we use the speed of a hand movement, the smoothness of the hand trajectory, and the relation between a position of hand and a gaze point. To predict the target object, we use a probabilistic model based on the user's hand movements and his/her gaze direction. The proposed system can detect reaching movements and predict the target object at a very early stage. A description of the experimental results demonstrates the advantage of the method proposed here.

In this paper, we analyze and compare the performance of the vertical and the horizontal automated-guided-vehicle transportation systems. We use results in queuing network theory and a transportation simulator to design a hybrid strategy for this study, and to set the appropriate number of agents in the systems. Next, these two transportation systems are evaluated based on cost-effectiveness criteria. For this purpose, the total construction costs of the systems for the various transportation demands are compared. Finally, we provide analytical results to evaluate and to obtain the most efficient system, based on the validity of each system, under different demand scenario. Note to Practitioners-A good design methodology is essential for the study of the optimal layout in an automated container terminal. Port designers need to select the most efficient automated-guided-vehicle (AGV) transportation system, and to set the appropriate number of agents operating in the system. This study presents a hybrid design methodology and a cost-effectiveness comparison of the vertical and the horizontal transportation systems. Our proposed design methodology is able to derive the combinatorial optimal design solutions rapidly, and at the same time pin point the bottleneck in the system. This proposed methodology can be easily applied to any transportation or logistics system, provided the system can be divided into components represented as nodes in a graph. Our results demonstrate that the horizontal AGV transportation system is more effective than the vertical AGV transportation system under most demand scenarios.

A searching methodology with goal state optimization considering computational resource constraints is proposed. The combination of "an extended graph search methodology" and "parallelization of task execution and on-line planning " makes it possible to solve the problem. The proposed method is evaluated with a rearrangement problem of 20 movable objects, and the effectiveness is shown through simulation results

In this paper, the final stage of a multiphase approach for solving the picking problem in a warehouse is addressed. Given a number of agents, each with its own set of picking sequences (trips or routes) to accomplish, a dispatching problem is described and shown to have a non-polynomial search space with respect to the number of agents and number of routes. A simulation-based scheduling procedure is proposed to solve the problem. The aim is to reduce potential delays induced by agent queues. Extensive statistical simulations on a realistic warehouse operating at varying conditions are conducted to show that the said dispatching procedure is able to make significant improvements with respect to minimizing operating time, on the average, over the case when no dispatching policy is applied to the agents

Semiconductor manufacturing is mainly characterized by diversity of products, different process types, and random failures. It is extremely difficult to solve scheduling problems due to high frequency of disturbance occurrence. This paper proposes a new approach for the online manufacturing rescheduling method. Instead of having a dispatching process based on dispatching rules once again, we revise the existing schedule based on message passing principle once the disturbance occurs, and improve the performance of the revised schedule by the introduction of a quick local search according to permutations of processes on semi-critical paths. The proposed method can release the influence of disturbances in less than 1 second, and finish rescheduling process in less than 1 minute. Through the actual problems with about 200,000 processes, this method is evaluated to be more effective for actual manufacturing than the dispatching-rule based methods, some of which have been applied in many facilities

We have proposed "attentive workbench (AWB)," a deskwork support system. Using cameras, projectors, and self-moving trays, the system supports human user from both physical and informational aspects. In this paper, an interface between the user and AWB based on pointing gestures is developed. Considering the usage of AWB, a simple and reliable interface with quick response is implemented. Deskwork support using the present interface and real self-moving trays are demonstrated

We aim to realize a robotic system that hands over a necessary object to a user as soon as he/she attempts to reach out for it. In order to realize such system, the following is required: 1) detection of the reaching movement, 2) prediction of the target object among multiple objects, and 3) handing over the object to a user. In this paper, the first two of them are described and discussed. We apply the smoothness and the speed of the hand movement to distinguish whether the movement is for reaching or not. And in order to predict the target object, we define the certainty according to the relative movement of the hand to each object. To evaluate the performance of the proposed method, we compare the method with the minimum jerk model based approach. A description of the experimental results demonstrates the usefulness of the method proposed here

An algorithm that acquires the intermediate goals between the initial and goal states is proposed for an agent executing multiple tasks. We demonstrate the algorithm in the problem of rearranging multiple objects. The result shows that the moving distance to transfer the entire objects to their goal configuration is 1/15 of that without using intermediate goals. We experiment using a real robot to confirm that the intermediate goal can be adapted to a real environment. Our experimental results showed that an agent could adapt the intermediate goals, which were acquired in the simulation, to the experimental environment.

In this paper, two designs for optimal Automated Guided Vehicle (AGV) transportation systems are presented. One is vertical and the other, horizontal. For these systems, the hybrid design methodology proposed here is used. Therefore, we describe how to model and formulate these transportation systems and derive the design parameters, that is, the optimal design solutions. We next evaluate these two transportation systems based on the various transportation requirements, that is, the demands from a port authority. For this purpose, we compare the systems based on the total costs in constructing them. Finally, the evaluation and analytical results are provided, and the most convenient system is presented based on the validity of each system for the given demand.

We propose “Attentive Workbench (AWB),” a new cell production system in which an intelligent system supports human workers. Using cameras, projectors, planar motor driven self-moving trays and other devices, the system supports workers from both physical and information aspects, recognizing worker’s condition and intention. This paper outlines AWB and deals with physical assembly support using self-moving parts trays. Two different schemes, centralized and decentralized, for controlling multiple parts trays are evaluated through experiments and simulations. The centralized control scheme is found to have the higher performance than the decentralized control scheme.

The job-shop scheduling problem (JSSP) is one of the most difficult NP-hard combinatorial optimization problems. It is extremely difficult to solve the complex job shop scheduling problems because of the disturbances of manufacturing environment. This paper proposes a new method based on the reaction-diffusion equation on a graph [Yuasa, H and Ito, M (1998)] to solve JSSP with disturbances. This method considers the operations of JSSP as the autonomous decentralized system elements. According to the definition of the local potential functions between two operations (two operations on the same machine or the same job), the original schedule can be revised to accommodate with the disturbances of manufacturing, such as machine breakdown, processing time variation, in a short time. The present method is tested for benchmark problems with disturbances.

Design process of a robust flow-path network for automated guided vehicle (AGV) systems is proposed in this paper. An effectiveness of a system with a robust flow-path network does not sink against any task. However, for this robust flow-path, the number of possible tasks is very large in AGV systems, therefore it is impossible to test the promising flow-path network against all of possible tasks. The problem is solved by the method of difficult task design with genetic algorithm (GA). The effective flow-path network is designed with GA simultaneously, because the difficult tasks depend on the flow-path networks. Competitive co-evolution is applied to the simultaneous design. Results of the designing are shown through simulations and the designed flow-path network makes it possible to complete 10000 tasks that are generated randomly.

The development of a highly efficient management methodology for an automated container terminal (ACT) poses a problem for port authorities. The focus here is on a transportation system for an automated guided vehicle (AGV) for an ACT. In this paper, we design the detailed management models, i.e., agent cooperation and container storage planning for the transportation system. Then, we optimally design systems that are constructed with the use of the designed management models. Comparisons of the systems are made to evaluate cost effectiveness based on the total construction cost and validity of the management models. Finally, a proposal is made for the most efficient management system.

We have proposed "attentive workbench (AWB)", a new cell production system in which an intelligent system supports human workers. Recognizing worker's condition and intention, the system supports workers from both physical and information aspects. This paper deals with physical assembly support using self-moving parts trays. The system delivers necessary assembly parts to workers and clears finished products quickly. On transporting large products, multiple trays are subjected to form a rigid body working as a large single tray. In order to realize this arrangement of trays in real-time, a planning method based on the priority scheme and heuristic rule is proposed. The present method is evaluated through simulations. A demonstration of assembly support using real self-moving trays is shown.

We have proposed "attentive workbench (AWB)," a deskwork support system. Using cameras, projectors, and self-moving trays, the system supports human from both physical and informational aspects. This paper deals with the problem for estimating the object that a user needs. We present one of the approaches for accurate estimation of a necessary object from the user's pointing gesture. The system arranges the self-moving trays by itself based on the user's past action sequences. Using force-directed method, good placement, which decrease false estimations and is not moved very much from the initial placement, can be obtained. A description of the experimental results demonstrates the usefulness of the method proposed here.

A rearrangement planning methodology of multiple movable objects by a mobile robot is proposed. Several objects are to be moved from an initial configuration to separate destinations as a result of a robot handling operation. This report proposes a planning algorithm consisting of a global motion planner and a local motion planner. The global planner utilizes a priority graph of a movable object, while the local planner requires a real-time search methodology. The simulation results of rearrangements with three, four, and eight movable objects demonstrate the effectiveness of the proposed algorithm.

Integrated design process for automated guided vehicle (AGV) systems is proposed in this paper. There are two problems to design AGV systems: transporter routings and flow-path networks. These two should be solved at the same time, because one problem depends on the other. This issue is solved with cooperative co-evolution in this paper. Both problems are represented as different species and genetic algorithm is applied to them. AGV systems designed with this method are evaluated through simulations with real data.

In this paper, we provide the comparison indicator of the AGV transportation systems. For this purpose, we propose an optimal design methodology for the AGV transportation system by using the queuing network theory. In this methodology, the queuing network theory and a simulation-based optimization method are integrated to obtain the optimal design parameters (i.e., these are the design solutions of this design problem). In this study, two different types of the AGV transportation systems are designed. Then the performance of transportation is compared. Finally, the characteristic of each transportation system that depends on the design parameters is provided.

This paper proposes a system that identifies the position and orientation of hand bones from magnetic resonance (MR) volume images by registration of a bone model. Investigation of the link structure of a human hand requires the measurement of the relative movement of the bones by identifying each bone configuration among a large number of hand poses of the same subject. To save total time in acquiring bone configurations, a three-step process implemented: (1) a bone polygonal mesh is generated for a bone model by manually segmenting the bone region from one of the MR scanning data; (2) the initial configuration of the bone model is set manually; and (3) the bone model is aligned in MR voxel data to maximize the performance indices. Experimental results show the validity of the identified configurations of the bones by the proposed system when compared with manually generated bone mesh configurations.

This paper proposes a method that reasons intent of motion through task order such as "switch on the light." Intent of motion means kinds of path of a contact point between a target and an end effector when a robot recreates ordered task. The intent is used for teaching. After a robot fixes a framework of task using the intent, it asks questions to a user to specify concrete values in the framework, the user replies them. This interaction creates motions of the robot. The kinds of path are defined as linear, circular and point to point. Moreover, impossible task is also considered. Four sets of P/sub f/(w): Probability that a word represents a path, and C/sub p/(w}: Certainty of P/sub p/(w) are added to each word in an electric thesaurus to enable the reasoning. After values are input in part of the sets by 8 production rules, they are propagated to the other sets through hierarchical relationship of words in the thesaurus. When the user enters two words, namely task and target, argmax/sub p/{P/sub p/(w)*C/sub p/(w)} becomes answer. Moreover, update and re-propagation of values in the sets using answers from the user make the reasoning more precise.

In this paper, fast heuristics for a centralized multi-agent route planner are presented and computationally evaluated. We solve a sub-problem of warehouse scheduling involving the routing of intelligent agents as a preliminary step in optimizing the total schedule. The problem involves the generation of routes for automated agents tasked with the transfer of items within a warehouse from storage pallets to a common loading shed. The goal is to minimize the total distance of the routes and the number of routes generated. This constitutes a multiple-objective optimization problem which is NP-hard and hence can take a prohibitively long time to solve using existing search-based techniques. The approach adapted here is to model the system as a split delivery vehicle routing problem (SDVRP) with grid distances and to solve it using heuristics based on tested operations research concepts. Twenty-two such heuristics are tested including the well-known greedy nearest-neighbor (NN) heuristic, and the established savings heuristic of Clarke and Wright. The author introduces two SDVRP variations of the NN algorithm, namely the nearest-fill (NF) and nearest-fill farthest-start (NFFS) heuristics. Existing SDVRP improvement procedures are also considered and generalized to produce numerous heuristic variations. This novel approach of applying fast vehicle routing heuristics to multi-agent routing has the advantage of yielding good quality results within a very short period of time. The results of the study show that the greedy NFFS heuristic combined with the improvement procedures, consistently produces superior results with regard to minimization of distance and the number of routes in an the instances tested.

Logistics network design problems are usually solved through experimental and intuitional approaches. These approaches are taken because of the complicated conditions that accompany problems associated with layout planning, stock analysis, network flow. In this study, the problem is divided into two parts: material flow and layout planning. An algorithm is examined in this study for a design that involves the formulation of material flow into the shape of mixed integer problems.

In this paper, region exploration path planning algorithm is proposed. In order for a mobile robot to perform this task, appropriate measures with the shape of the working environment, which may be intricate or curved, is necessary. In addition, a robot must be able to react and be flexible when confronted with obstacles. With this algorithm, these challenges can be met by approximately expressing the working environment in grid points and regenerating the path using one that was planned beforehand. Simulations are used to demonstrate proposed exploration path planning and re-planning algorithm.

The design for an artificial mark attached to small objects is presented in this paper. Three conditions are required for the design of the marks: (1) short calculation time, (2) easy attachment, and (3) individual calculation for each item. The marks are obtained by using three different extraction methods. The experiments determine the best mark. The mark using the method that extract colors and calculated the gravity has good results. We analyze the errors of the mark that has best experiment. The appropriateness of the experimental results is then confirmed. We have the experiment that the manipulator handles the objects with the best mark. The experiment is successful. Therefore, the usability of the mark is verified.

To achieve smooth motion of car-like robots, it is necessary to generate paths that satisfy the following conditions: maximum curvature, maximum curvature derivative, and curvature continuity. Another requirement is that human operators can manipulate the robots with ease. In this paper, a path expression methodology consisting of line segments, circular arcs and clothoid arcs is presented. In addition, a method of global path generation with a visibility graph is proposed. To establish this method, the following steps are proposed: (a) the arrangement of subgoals (middle points) and (b) the construction of the graph for path generation. By using the proposed method, the paths were shortened 14% on average.

In this paper, we propose cooperative exploration method for mobile robots in a working area. The method will generate and share the minimal-cost path for each mobile robot. Each mobile robot goes through several observation points to accomplish the exploration. The observation points should be arranged at a fixed distance from at least on corresponding observation point. In addition, the number of observation points and the lengths of paths for each mobile robot are to be minimized. The proposed method should have the efficiency in computational cost concurrently with the adaptability to dynamic environmental changes.

This is a review paper on Mobiligence research. In order to solve the mechanism of intelligence with which higher organisms can behave even in unknown environment, and to discover design principle of artificial systems that have such intelligence, a new concept called Mobiligence is introduced based on assumption that the intelligence must be emerged through the interaction of the system with the environment caused by its mobility. The research on the Mobiligence is characterized by embodied plasticity, co-embodiment with environment, and internal generation of abductive information. The framework of the research on Mobiligence is presented in this paper as well as the originality of the research.

The robotic model considered hereafter is an attempt to deal with the problem of mobility for simplified holonomic mobile robots, such as Braitenberg vehicles. The aim is to find out which behavioural constraints are needed to make emerging a collective behaviour in the form of "mobility task". In our model "situatedness" and "embodiment" are defined as in the traditional behaviour-based approaches. The situated body of such a simplified robot is reach enough to be more than a merely symbol manipulating process. The model provides sensor data and effector commands with the same meaning of energy exchange between the robot and its environment, eventually populated by other robots. When a group of robots want to cooperate for a common goal it uses "stigmergy" to allow each individual to take advantage from other individual behaviours. Stigmergy detection is evaluated by the so called "macroparameters" and triggered by a dynamical assignment of "roles", which can force a collective behaviour. The concept of "perceptual perturbation" introduced in our robotic model is a useful abstract tool to figure out collective mobility tasks. This property stems from the "autopoietic loop" shown by each individual and which, under specified conditions, can interact with other ones to form one or more autopoietic loops involving two or more individuals. Collective behaviours eventually emerge as a consequence of a non-linear interaction among behaviours.

This paper offers a proposal for an algorithm of controlling a mobile robot that searches for and rearranges objects with unknown locations and shape. In this paper, we divide the task into two parts: exploration task and rearrangement task. The algorithms for each part of the task are presented with respect to the effectiveness of the path length and computational cost. Additionally integration algorithm that effectively combines exploration and rearrangement is presented. Experiments with a real robot are conducted to demonstrate the effectiveness of the proposed algorithm.

This paper presents a robot system for handling various objects in home or office environments. A fixed manipulator utilizes marks on objects for handling and motion planning. A mark consists of two parts. One is the outer part, which indicates its pose (in this paper, "pose " denotes 3D position and orientation), and the other is a QR code, which is a kind of 2D barcode. A QR code is stored information of an object (e.g. its name). A user attaches several marks on each object. The manipulator accesses the information using a camera in its hand. The robot can decide its complex handling motion based on the information in QR codes, pose of the objects, which are estimated from pose of marks, and signals from proximity sensors. Experiments are conducted to verify the whole system.

In this paper, we propose a motion-planning method of multiple mobile robots for cooperative transportation of a large object in a three-dimensional environment. This task has various kinds of problems, such as obstacle avoidance and stable manipulation. All of these problems cannot be solved at once, since it would result in a dramatic increase of the computational time. Accordingly, we divided the motion planner into a global path planner and a local manipulation planner, designed them, and integrated them. The aim was to integrate a gross motion planner and a fine motion planner. Concerning the global path planner, we reduced the dimensions of the configuration space (C-space) using the feature of transportation by mobile robots. We used the potential field to find the solution by searching in this smaller-dimension reconstructed C-space. In the global path planner, the constraints of the object manipulation are considered as the cost function and the heuristic function in the A/sup */ search. For the local manipulation planner, we developed a manipulation technique, which is suitable for mobile robots by position control. We computed the conditions in which the object becomes unstable during manipulation and generated each robot's motion, considering the robots' motion errors and indefinite factors from the planning stage. We verified the effectiveness of our proposed motion planning method through simulations.

Describes a concept of a distributively placed modular robot system and a methodology of parameter identification of the system. The robot system consists of modules which have monofunction (e.g. sensor or actuator) and radio communication. Depending on tasks, the system is constructed by attaching modules to an object or in a working area distributively. Then the modules achieve a task by their cooperation. In our algorithm, the parameters are identified by executing motions with actuators, and observing the changes of the state with sensors. The motion which we call testing motion influences accuracy of identification because of sensing errors. Accounting for this, especially for the case of a mobile robot and a ceiling camera module, the design of modules' testing motion is made by evaluating the influence of the camera's sensing error and dead-reckoning's locating error. The effectiveness of the algorithm is shown in the simulation and a basic experiment.

The author has proposed the rearrangement planning methodology of multiple movable objects by an autonomous mobile robot. In the problem settlement, several objects should be moved from initial configuration to each destination through the robot's handling operation. The proposed algorithm has the following characteristics: (1) introduction of a real-time search methodology for application to sensor-based robots, (2) description of motion primitives-transit movement, transfer movement to the destination, transfer movement to temporal positions, (3) motion generation of each primitive with the standard motion planning methodology, and (4) proposal of an efficient heuristic function by using the solution of the stacker crane problem and the concept of the precedence graph. Simulation results indicate the effectiveness of the proposed algorithm from the viewpoints of planning quality and calculation cost.

In this paper, we describe a novel self-localization algorithm. Self-localization methods are required for lowering the computational cost and handling vague sensor data. Thus, we propose to use only the uniform distribution to represent probability distributions in Monte Carlo localization, and name this method a uniform Monte Carlo localization (Uniform MCL). We manifest the low computational cost and robustness of Uniform MCL in the environment of RoboCup Sony legged robot league.

This paper presents an integrated design for an automated guided vehicle (AGV) system. Three kinds of integration problems are solved: transporter routings are classified; the number of AGVs for the systems is determined; and suitable flow paths are calculated for the various kinds of transporter routings. The flow paths are calculated by solving an iterative direct problem using a genetic algorithm (GA). The set of transporter routings, the number of AGVs, and the flow path of the AGV systems are constructed with simulations.

This paper describes interface design and implementation based on a concept of simple instruction by pointing environmental features on navigation task of a modular robot system. The robot system is constructed by attaching modules (e.g. camera or mobile mechanism) to objects or a working environment distributively. The attached modules achieve tasks through cooperation, after constructing the system. Assuming that the system is constructed in different environments depending on tasks, the system is not expected to have knowledge of environment for autonomous behaviors. According to this, we propose a simple interface with instructing tasks on multi displayed images of cameras which are attached in unknown wider working area. The main features of the interface are as follows: (1)Indication of over lapped view of cameras for support human recognition of working area, (2) Easy precise operation by pointing environmental features on captured images. Our experiments show the effectiveness of our interface and the whole robot system.

This paper deals with the method to generate human whole body motion by identifying performance index to be optimized. Most of the human motion in daily life, including a "lift-up an object" motion, is complex and the performance index are regarded to vary during the motion. Lift-up an object motion is generated by solving inverse kinematics. The experimentally measured trajectory of the hand is given and the rest of the degree of freedom is determined by optimizing a performance index that blends several sub-functions. The coefficients for each sub-function are identified through the motion generation simulation.

Recently the activity of robots has been remarkable, and robots are branching out into general environment such as home or office. Although robots are active, planners uniquely decide the action method of robots when robots operate missions. Therefore, the most suitable approach is not assured. So, in this report we propose a method that we evaluate robots' actions from the viewpoint of both reliability and time, when robots operate missions. Then we calculate the most suitable approach for robots. To be concrete, by using a diagram we evaluate robots' actions and calculate the most suitable approach. In addition, we evaluate them not from all the actions immediately, but from the action units by making an evaluation index. So, we can shorten the time for evaluation. Finally, we realize the operations by a real robot with the most appropriate approach.

This paper deals with a task-assignment architecture for cooperative transport by multiple mobile robots in an unknown static environment. The architecture should satisfy three features: deal with a variety of tasks in time and space, deal with a large number of tasks compared with the number of robots, and decide the behavior in real time. The authors propose the following approach: we consider the unit of task (task instance) as the job that should be done in a short time by one robot. Based on the environmental information, task instances are dynamically generated using task templates. The priority of task instances is evaluated dynamically based on the number of robots and the configuration in the workspace. In addition, we avoid generating too many task instances by suppressing object motion. The main part of the architecture consists of two real-time planners: a priority-based task-assignment planner solved by using the linear programming method, and motion planners based on short-time estimation. The effectiveness of the proposed architecture is verified by a cooperative transport simulation in an unknown environment.

In order that robots may achieve tasks in unknown environments, research focusing on the following three points must be conducted: (1) design of commands from the operator to the robots; (2) environmental recognition to show the operator information about the remote work area where robots are working; and (3) interface between a system and the operator and information display of the work area for the operator. For (1), the focus is on the operation of the multiple robots by a single operator. For (2), the problem is divided into two parts; object recognition and self-positioning. As for (3), a graphical user interface is implemented. The operator mainly uses a mouse to input commands, and the operator watches a virtual environment to monitor remote working areas. The virtual environment is constructed with the environment information acquired by the method described in (2) above. After the problems mentioned above are solved, our system will provide a flexible command and monitoring structure and an operator would be able to command the robots appropriately. These are the main advantages of the method we are proposing. We verified the effectiveness of the proposed methods in experiments.

In this paper, the authors aim at realizing an accurate navigation system of automated guided vehicles (AGV). The authors propose a way of estimating positioning error with magnetic tape, which is widely used in a factory as an external sensor. However, flexibility for path relocation is insufficient, because, in general, the tape should be laid down on the floor from a start point to a goal point so that AGV can reach their target. To overcome this inefficiency, the authors firstly propose a semi-guided navigation methodology by means of two kinds of magnetic tapes based on an error analysis. The semi-guided navigation means that magnetic tapes are only placed at the start and the goal points individually. Therefore, this system enables us to remove most of the magnetic tape. Moreover, the authors attempt a fixed model learning to prevent stationary error while AGV run iteratively. Finally, the authors carry out experiments to evaluate and verify the efficiency of the proposed method.

This paper deals with a task-assignment architecture for cooperative transport by multiple mobile robots in unknown environment. The planner should satisfy three features: to deal with the variety of tasks in time and space; do deal with a large amount of tasks; and to decide behavior in real-time. The authors (1999) have proposed the following approach: 1) based on sensor information, tasks will be dynamically generated using task templates; 2) generation of task is tuned in quality by feeding back executed result and in the number by action suppression; 3) main part of the architecture consists of two real-time planner: priority-based task-assignment planner solved by using a linear programming method and motion planners based on short-time estimation. In this paper, we implement our task-assignment architecture on a real robot system and verify the effectiveness of the proposed method by cooperative transport experiment in unknown environment.

A transport module for constructing an efficient and flexible material handling system are proposed in the paper: an AGV (automated guided vehicle) module loaded a conveyer in which it is possible to hand-over operation between two modules. The operation can solve the deadlock problem among AGVs in the process of material handling. Prototype modules are created and hand-over experiments are made to measure motion characteristics of the modules. Transport simulations are made for a model plant layout by using values from former experiments. The results show the effectiveness of the proposed module from the viewpoint of transport efficiency, especially for cellular-type or process-type plant layouts.

We propose a motion planning method for cooperative transportation of a large object by multiple mobile robots in a 3 dimensional environment. This task has various kinds of problems, such as path planning, manipulation and so on. All of these problems can't be solved at once, since computational time is exploded. Accordingly, we divide a motion planner into a local manipulation planner and a global path (motion) planner, and design these two planners respectively, and we integrate two planners. Namely, we aim at integrating a gross motion planner and a fine motion planner. As to the local manipulation planner, we build a manipulation technique, which is suitable for mobile robots by position control. We compute conditions, in which the object becomes unstable during manipulation, and generate each robot's motion considering the robots' motion errors and indefinite factors from the planning stage. As to the global path planner, we reduce the dimensions of the configuration space (C-space) using the feature of transportation by mobile robots. We can find a solution with searching in this smaller dimensional C-space using the potential field defined in the C-space, and constraints of the object manipulation are considered as the potential function. We verify the effectiveness of our proposed motion planning method through simulations and experiments.

We propose an architecture to accelerate reinforcement learning by a mobile robot. In order to solve the problem of explosion of learning time in former reinforcement learning methods, we introduce a mechanism to acquire and utilize a set of widely applicable generalized rules into a reinforcement learning system. The mechanism extracts these rules by a statistical analysis of experienced data from the learning process. By applying these rules, the learning process can be accelerated by reducing search space. Simulation results indicate the effectiveness of the proposed method.

An idea of considering an autonomous mobile robot as a virtual local network system is proposed in developing a remote fault diagnosis system of a mobile robot. Within the developed diagnosis system, a mobile robot is taken as one of the management objects of the network management system for autonomous mobile robots, and the simple network management protocol, which is widely used in network management system, is applied and adapted to the developed system as the communication protocol for exchanging diagnosis information. Moreover, by taking advantage of active moving and sensing ability of autonomous mobile robots, an effective fault inference method is also discussed.

Presents a methodology of environmental support for autonomous mobile robots using visual marks with memory storage. The mark makes up for insufficient function of sensing and recognition; self-positioning, positioning of objects, and deciding methods to operate objects. The mark proposed in this paper consists of a landmark part and a memory part. The landmark part is used to estimate the relative position and orientation between robots and the mark, and the memory part gives information about what it is, what tasks there are, and how to conduct the tasks. Task execution using the marks with real robots is described to show the effectiveness of the proposed methodology.

We propose a cooperative transportation method by quadruped robots with vibration in a 3-dimensional environment. In the case of cooperative transportation by legged robots, we must solve two problems; (1) how to avoid exerting excessive internal force on an object and how to deal with sensed values, the position and orientation of the object with vibration because the body of the robot vibrates in walking; and (2) how to decide its velocity that depends on several parameters such as step, walking cycle, and so on. Considering these problems, we construct the cooperation method of the transporting system with vibration. Our method consists of three aspects; firstly we decide how to suitably distribute the object's degree of freedom (DOF) to two quadruped robots. Secondly how to decide the robot velocity is described. Thirdly we create the vibration model aimed at the periodic gait of a quadruped robot by filtering the ingredient of the robot vibration from sensed values of the object deviation from its normal position on the robot. The created model enables us to estimate the deviation of object on the robot and the vibration parameters of the other robot. Moreover, we propose the step prevention motion of quadruped robot where its velocity is close to zero. The effectiveness of the step prevention motion and gait based on the vibration model is verified by some experiments.

In this paper, we propose a heterogeneous robot group for cooperative task execution. The "heterogeneous" robot group is composed of robots which have different motion algorithms. In this paper, we investigate behavior of the heterogeneous robot groups by simulations of multi-agent traveling salesman problem, which we hardly solve by centralized algorithms. Then we propose an algorithm, based on reinforcement learning, to adapt mixture ratio of robots driven by different algorithms. We verify the efficiency of the autonomous heterogeneous robot group by simulations.

Deals with a task-assignment method for cooperative transportation by multiple mobile robots in an open and dynamic area. The problem can be divided into three parts. The dynamic change of tasks, the dynamic change of the robot team composition, and the level of difficulty in finding the optimal solution. The authors propose the following approach: (1) by inputting sensor information to a "task template" and a "task-interaction template", tasks will be dynamically generated; (2) the planner consists of two parts, the task-assignment planner and the motion planner (the latter is made independently for each task); and (3) the task-assignment problem is solved by using a linear programming method based on the execution priority described in the "task templates". The effectiveness of the proposed method is verified by a cooperative transport simulation.

In this paper, we propose a method of an object manipulation by multiple mobile robots using sticks as tools. In the conventional cooperative work by multiple mobile robots, manipulation technique based on force-control has been proposed. However, mobile robots are moving by position-control, and motion errors can easily arise. Then, we build the manipulation technique, which is suitable for mobile robots by position-control. We propose the manipulation method without using sensor information, and consider the motion errors of mobile robots and the indefinite element of environment from the planning stage. We compute the conditions in which the object gets unstable during manipulation, and generate the motion of each mobile robot with these analyses. We verify the effectiveness of our proposed motion planning method through simulations and experiments.

We propose an algorithm for unforeseen obstacle avoidance by an autonomous sweeping robot. Sweeping means a motion that a robot covers a 2-dimensional area. When a mobile robot finds an unforeseen obstacle, it not only has to avoid collision but also sweep whole area including around the obstacle. In the proposed algorithm, a robot determines local area where path for sweeping should be arranged according to its limited sensing ability. By connecting the paths inside and outside the local area, the robot obtains a new total path. The local path re-planning saves heavy calculation to generate whole path again at online.

We propose an operation method to control mobile robot groups by a single human user, and build a system in which the human user and robots achieve tasks cooperatively. As for the commanding aspect, we divide orders from the user into four levels and consider the relationship between orders and autonomy of robots required. The main features of our method include: 1) the human user commands not just an individual robot, but groups of robots; and 2) our system has autonomy in proportion to user command levels and complements lack of command for the group (sometimes commands for the group is not sufficient for individual robot control). Regarding the monitoring aspect, we introduce a CCD camera system into our system and display information on a CRT display which shows the image of the work area. The user can monitor a remote environment and simultaneously obtain the information needed during operation. We verify the efficiency of our method and system by experiments.

The authors develop an interface system for service robots. In order to assure adaptability to lightly structured environments and easy programming of new tasks, we propose an automatic adaptive motion generation methodology and introduce it in the context of an interface system for an assistive rehabilitation robot. The implementation of the robot interface system using VRML and Java shows the effectiveness of the proposed method.

Service robots are extensively installed with end-effectors, which require high energy to achieve a specific task. Robots require high-power energy sources that can meet several goals simultaneously. Given the technology of today a battery-driven robot cannot work for long on a single charge. To develop a new powerful battery technology may take many years. In this paper we present a dual-energized scheme called Hybrid Power Supply (HYPOS) that integrates the function of batteries and household electric system to supply power to a robot. This technique can be realized by the existing technology and is applicable to a variety of indoor applications. The paper includes the description of the prototype robot and the experiments that illustrate the effectiveness and practicalness of this proposed system.

We are going to analyze on the constraint among contacting points between robots and an object that is manipulated by the robot group. Constraint can be obtained by solving pseudo inverse of matrix filled by force data measured by all robots supporting one object. The result provides additional information for obtaining a relative positioning information to the object to ordinary constraint based on geometrical reasons. This analysis on the constraint has been verified by sensing system. The model explained here can be used for estimating contacting points, fitting coordinate system among robots, and making active sensing strategy for detecting the change of environment.

This paper deals with motion planning of cooperative transfer with regrasping by a group of car-like mobile robots. In order to realize this transfer task by actual car-like robots, three aspects should be considered: the nonholonomic characteristic of car-like robots, how to control the force and avoid too large a force by position-controlled robots, and control cycle of the system. Based on algorithms from previous works, the authors propose the following approach: 1) designing robot hand mechanism; 2) constructing hybrid planning architecture composed of centralized and decentralized system including an object motion observer. A simple transport experiment by an actual robot system is made to show the effectiveness of this mechanism. Validity of the proposed method is verified by a transport simulation.

In transporting various objects by multiple mobile robots we need to change the formation of robots and posture of an object. This planning depends on the mass and center of gravity of an object. Here we discuss the accuracy of the estimation of the mass and the position of the center of gravity of an object. Particularly we propose a method for estimating the height of the center of gravity which cannot be estimated by only one measurement. Validity of this strategy is verified by means of a real robot system.

In this paper, we propose an off-line planning algorithm for cooperative tasks of multiple mobile robots. Sweeping means a motion that a robot covers a 2-dimensional area by its effector. Sweeping of a whole work area is fundamental and essential task of mobile robots. For efficient cooperation, setting appropriate burden onto each robot is very important because interference of robots and overlaps of their effecters make efficiency low. The cost of sweeping depends on both sweeping ability of a robot and a shape of a work area to be swept. Evaluation and distribution of the cost are most important issues. In the proposed algorithm, the cost is evaluated by means of length on which robot should move. We introduce both edges of the configuration space and Voronoi diagram so as to compute paths in the whole area. We generate the a tour for traversing all the paths by applying the algorithm of the Chinese Postman Problem. According to the cost evaluation, appropriate paths of the tour are assigned to each robot. The efficiency of the proposed algorithm is verified by simulations and an experiment.

We aim at handling an unknown object by cooperating multiple mobile robots. The most important problem in this paper is obtaining a weight and mass center position of an object. Another important problem is to determine appropriate grasping points before handling. This method is proposed to avoid bad situations, such as making an object fall down, or some robots overloaded in handling. The algorithm we propose in this paper is a first step of grasping objects, which is "determination of initial grasping points". Initial grasping points are given by only a geometrical shape of an object. The robots are placed where the possibility that robots can lift the object with no trouble. This evaluation is based on the normal distribution model proposed in this paper. We are also going to discuss how to avoid and regrasp when initial grasping has failed. We show a simple numerical example of initial grasping and re-grasping, in planar object model.

This paper surveys former studies on task planning of multiple mobile robots. Task planning algorithms are categorized into four from two issues: dimensions of tasks demands (point-reaching tasks and region-sweeping tasks), and the number of iteration of the task (one-time tasks and iterative tasks). Individual studies are surveyed based on the above categorization.

This paper is focused on solving the multiple robots motion planning problem in a robust way, whereas we say that a motion plan is robust if it can be used in spite of small variations in the motion context. After a critical review of their previous research on generating multiple robots motion plans (1995), the authors classify several "sources of instability" useful for establishing criteria for robustness. These sources include variations of the environment model, variations in the number of robots and paths, and variations in the motion parameters of the moving items. We define various "impact factors", that evaluate how much a variation affects a given plan. For example, a new path (for a robot) can be associated with a collision impact factor, that estimate how many collisions among moving objects may occur along that path. Hence, to evaluate the motion plan robustness, we define how quality values for motion plans are modified by the impact factors.

In this paper, we propose an off-line planning algorithm for cooperative tasks of multiple mobile robots. Sweeping means a motion that a robot covers a 2-dimensional area by its effector. Sweeping of a whole work area is fundamental and essential task of mobile robots. It is more effective if a robot can move an obstacle during a sweeping task as if we clean our room with relocating chairs. We can also consider that this is the simplest task which includes both point-to-point motion and sweeping. We model sweeping and relocation, and propose an algorithm to find appropriate path for robot and way of relocation of each obstacle. We apply the LT graph to solve the problem, which describes both motions of robots and blockades by obstacles in path-time space. We verify the efficiency of the algorithm through simulations and experiments.

This paper deals with an arrangement planning problem of mobile robots for handling unknown objects. To improve the flexibility of multiple a mobile robot system, these systems are now, expected to handle various kinds of objects. The authors propose a closed algorithm to judge whether a group of robots (robot group) can handle an object or not, by means of geometrical data of an object only. For this algorithm we also propose a method to generate a robot arrangement to maximize possibility of grasping an object with no trouble. These methods are based on the quantitative relationships between an arrangement of robots, load data to robots, and unknown mass and mass center of an object. Effectiveness of the proposed method is verified by a simple simulation.

One of the major emerging problems of distributed mobile robotic system is what kind of inter-robot communication to use, because of increasing robots integrated in the system. This paper aims to give an analytical view of this issue. The efficiency of local and global communication is compared based on the analysis of information transmission time to multiple robots. We will show that local communication is effective in environments where cooperative tasks are executed by multiple mobile robots in distributed fashion.

The authors aim at grasping and handling a large object by cooperating multiple mobile robots with various sizes, moving cost, and load capacity. In this paper the authors focus on the problems of deciding an appropriate grasping arrangement before handling operations. The authors propose this method to avoid bad situations, such as making an object fall down, or some robots overloaded in grasping and handling. The algorithm proposed includes two optimization problems; one is the "decision of initial robot arrangement," the other is the "decision of final robot arrangement." The difference between these optimizations is that the mass center of the object is recognized or not. The penalty index is defined to minimize the energy the system consumes and to maximize an index of stability. The authors have confirmed that the robots moved to the optimal arrangements in computer simulations and experiments.

When many mobile robots should achieve cooperation, a local communication system is considered appropriate from the standpoint of the cost and capacity of communication. This paper presents the optimization of the efficiency of local communication in environments where many mobile robots send out information stochastically. The optimal communication area is derived by minimizing the transmission waiting time calculated using the probability of successful information transmission. Computer simulations have been undertaken to verify the analytical results.

We address the problem of planning the motion of multiple autonomous robots, by analysing their behaviour in space and time. Proper representations have been studied for both domains, with a particular emphasis on the definition of some performance indexes to weight the goodness of a path concerning motion and time performances. Sublinear algorithms have been used for planning in space while reasoning in the temporal domain is based on a proper subdivision of the time axis that leads to polynomial algorithms.

Proposes an algorithm of dividing a work area into small pieces in order to make multiple mobile robots cooperate efficiently. Searching of a whole work area is the most fundamental and the most essential task of mobile robots. The authors expect that the cost of searching is shared by cooperation of multiple robots. In the proposed algorithm, searching motions of robots are represented by paths. Both edges of the configuration space and the Voronoi diagram are introduced so as to compute paths in the whole area. The authors generate a tour of the paths to traverse all the paths using the algorithm of the Chinese postman problem. The cost is estimated as the total length of the paths. By means of the cost evaluation, the appropriate paths are assigned to each robot. The efficiency of the proposed algorithm is verified by simulations.

This paper deals with a motion planning of mobile robots during transferring a large object cooperatively by a group of multiple mobile robots (a robot group). This problem has the following characteristics: 1) a real time planning is essential because mobile robots move in an open and dynamic area in comparison to articulated robots; 2) both the object and the robot group need to avoid collision against obstacles; and 3) the object needs to be grasped stably by the robot group. The authors propose the following approach to solve this complicated problem: 1) regrasping strategy of the object is introduced in order avoid obstacles and to transfer the object stably; and 2) the motion planning is divided into two steps: the object and that of the robot group. The former step is accomplished by extending the virtual impedance method, which was proposed by authors. The latter step is realized by using nonlinear programming method, that optimizes a penalty index indicating the performance for obstacle avoidance and for stable grasping. Effectiveness of the proposed method is verified by a simulation of transferring a large circle-shaped object with three robots.

Local communication system is considered appropriate when many mobile robots should achieve cooperation, from the standpoint of cost and capacity of communication. This paper presents a design method of the optimal communication range for efficient local communication system. The analyses of optimization are made by minimizing the information transition time, first in the case of transmission to an arbitrary robot, and next, to multiple robots. Computer simulations have been undertaken to verify the analytical results.

For the cooperation in a large system with many mobile robots, local communication system is considered appropriate in terms of the cost and capacity of communication. The behavior of robots has a respectable effect on the efficiency of communication in such a local communication system and it is essential to know what kind of behavior robots should take to realize efficient information transmission for cooperative tasks. We introduce a simple group behavior for the purpose of improving the communication efficiency. This paper analyses the effect of group behavior on the communication performance, and derives differential equations describing information diffusion among robot groups. The optimal group size to transmit information to a desired number of robots is obtained front these equations. The effectiveness of the analysis is verified by computer simulation. We also show a self-organization algorithm for group forming designed for local communication system.>

A motion planning method for robot groups is presented. First, groups are classified into a group on common partnership (P-group) and a group in shape (cluster) based on types of common factors among members of the same group. The concept of cohesion is proposed to express changeability of clusters. Next, a motion planning method with dynamic grouping, which changes number of cluster in the group based on cohesion, is presented. The approach is implemented using the virtual impedance method. The effectiveness of the method is verified by simulation results.