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.

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.

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.

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.

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.

A method for simplification of teaching in robotic assembly is presented. Generally, an assembly task is a process in which a constraint condition between two workpieces changes. There has been much research on teaching an assembly task to a robot system by a model-based approach. However, it takes a lot of time to revise information of models that depend on the task environment when every new task is taught to a robot in these model-based approaches. We propose a method to simplify teaching by taking an approach where a human teaches a task to a robot without giving models of the task environment and task processes. Additionally, we also propose a method to realize robotic assembly that is robust to errors by executing a task as a transition process of a constraint condition. Experimental results show the effectiveness of our method.

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.

Describes the hierarchical control architecture of real mobile robots for Cellular Robotic System Mark-V (CEBOT Mark-V). A parallel processing control system has been adopted, and by adjusting the role of parallel processes standing for the states of independent primitive behaviors, the change of system organization is realized to adapt the redefinition of plural tasks and the variation of environments. The authors propose a method for decision making of a mobile robot's behavior through integrating multiple behavioral processes. The authors define two relation matrices denoting the relationship among the processes: the priority matrix and the interest relation matrix. The matrices are used to adjust the outputs of behavioral processes and optimize the behavior of mobile robots. To obtain the most suitable priority matrix, the authors introduce a learning-adapting algorithm. The results of simulation and experiment with a real CEBOT Mark-V showed the effectiveness of the proposed matrices and learning-adapting algorithm. On the other hand, instead of simply selecting processes for decision making of the robot's behavior, the integration of multiple processes based on the proposed matrices enhanced the control robustness of robot system.

This paper addresses an optimization method of the group behavior on decentralized autonomous robotic systems. Decentralized autonomous robotic systems refer to multiple robotic systems, such as cellular robotic system (CEBOT). The CEBOT, which has been proposed by the authors, consists of a number of robotic units called cells. In the research on the CEBOT, the same as decentralized robotic systems, the optimization of the behavior of each robot is one of the most important issues, since it is difficult to predict the behavior of the each robot in multiple robotic systems. In order to optimize the behavior in a dynamic environment, the authors propose a concept of self-recognition for the decision making of the behavior in a group. According to the proposed concept, this paper shows the learning and adaptation strategy for the group behavior, and presents some simulation results.>

This paper deals with a self-organization of the group of multiple robotic systems, such as the cellular robotic system. Especially, in this paper, the authors present the self-organization of the hierarchical structure of the cellular robotic system as one of the group robotic systems. The structural organization is simulated based on the idea of interdependence between autonomous robots, which is derived from the model of group organization of humans. That is, the groups are organized and any leaders emerge from the interdependence in the individuals. This paper represents simulation results of self-organization of hierarchical structure on the basis of a given task model.>

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

In the process of a cooperative search in a distributed robotic system, the local information and local knowledge, as well as the limitation of sensing range and communication range must be considered. The authors present a distributed genetic algorithm with a supplement operation as a method for cooperative searching. The distributed genetic algorithm is a search algorithm modified for a distributed robotic system. As an example of a cooperative search, path planning for configuring the structure of a cellular robot is considered. The cellular robot or cellular robotic system (CEBOT) is a distributed robotic system which consists of a large number of autonomous robotic units called cells. The process of cooperative path planning using the proposed genetic algorithm with communication between the cells is presented. The influence of the local sensing and local communication is considered.

A primitive approach to the self-organization of the structural configuration of the CEBOT is described. CEBOT consists of a large number of autonomous robotic units called cells. Assuming that there are sufficient cells in the environment to configure the cellular robots, the cells, which have information about a structure, use random walk to search for other cells to configure the structure. Since each cell organizes a cellular module/robot distributively, these cells must cooperate and negotiate to achieve the configuration of the goal structure. The behavior of the cells is examined on the basis of the selfish criterion. The entropy of structure configuration is proposed as an evaluation criterion in the global environment. The simulation represents the influence of the selfish behavior and the sensing range for the configuration of the cellular robots.>

The authors consider a grouping issue for a cellular robotic system (CEBOT) with multiple master cells. The CEBOT consists of a large number of autonomous robotic units called cells, that is, the system is one of group robotic systems. To evaluate the grouping effectiveness, the authors use the network energy. They consider each master cell as a coordinator in each sub-network. To evaluate the grouping network, they propose the cross-dependence matrix, which presents the relation between the cells and the task steps depending on a given task. The simulation results present the grouping examples of the cellular network. By describing the grouping issue, the authors also describe the effect for the group structure of an organization or a society.>

A control strategy for a network of cellular robots (CEBOT) is proposed. The strategy is based on the selection of a master cell and assumes centralized control of the CEBOT network. The selection is made using a network energy, in which an energy function is calculated based on the information flow in the CEBOT network and the state space of the communication transition probability. The authors describe both a method of mastering, which implies the determination of the master cell of the network based on the information flow between cells, and a method of mastering by a local energy sensitive estimation as a method of finding the master cell for the CEBOT network. It is assumed that the network has a binary tree structure for control and communication. The master cell is defined as a window for interaction between users and the CEBOT. Simulation results on the mastering are shown.>

Describes the communication architecture for 'cellular robotic system (CEBOT),' which is based on the selection of the master cells. CEBOT is one of the distributed intelligent systems. It is a 'dynamically reconfigure robotic system,' and the network is constructed by a large number of units called 'cells'. The authors deal with the number and the allocation of the master cells in the network, in which each cell is active stochastically. They describe the cross-dependence among cells on executing the task. This idea is based on the state of each agency, as in the field of artificial intelligence. The estimation of the number and the allocation of the master cells is based on the 'network energy'. In order to estimate the communication probability among the master cells, the idea of cross-dependence among cells is applied. The simulation results of the number and the allocation of the master cells, in the example, of the organized network are given.>