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.

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.

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.

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.

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.

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.

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.

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.

Recently, designers have been required solving comprehensive problem becoming greater and more complicated. In relation to this background, we have proposed Universal Abduction Studio (UAS); a computer environment that synthetically supports creative design. However, it is difficult for a designer to manually acquire multiple domain knowledge required for UAS. Therefore, we propose a Web-based knowledge database construction method for supporting design by UAS in this paper.

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.

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.

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.

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.