Our goal is to copy human dexterity of manipulation with force feeling to robot manipulation. We adopt Jenga game as our first target task. This paper describes our strategic implementation how robot removes blocks from Jenga tower. In this paper, we make Jenga kinematics model. First we show kinematics model of stable Jenga tower. Another kinematics model is of transition period of removing block. Using these kinematics models, robot manipulator chooses the most safe block to remove. But of course, there exist difference of acting force between ideal model and actual Jenga tower. So the robot judges the acting force against ideal one whether the block can be removed or not. When it is judged not to remove, the robot changes to next candidate to remove. According this force based manipulation strategy, the robot has achieved to remove more than twelve blocks form Jenga block tower. During competition with human, the robot can remove a block after more than twenty blocks are removed.

In order for a legged mobile manipulator to work, it is essential to change its posture for stable standing in response to the force at the end effector. We define 'firm standing' as keeping a stable posture in working without moving the positions of the point of contact of legs and end effector. We analyze firm standing and show required degree of freedom for it. We also experiment on it by using redundant d.o.f. in 2-D space.

There are some researches which perform complex manipulation tasks to control dynamic characteristic of an object (sliding, rolling, hitting, etc). They are called dynamic manipulation. We introduced tossing manipulation as one of it. In this paper, tossing manipulation is expanded to catch the tossed object without impact. We call it passing manipulation. A pair of 1 D.O.F manipulators is used as a tossing manipulator and a catching manipulator. First, the tossing manipulator swings its arm to roll/slide an object on it, and passes the object. Second, the object is thrown to the catching manipulator. Third, the catching manipulator swings its arm to contact the object without impact, and manipulates it on the arm. Finally the object stands still on the catching arm. This paper shows a kinematic model of passing manipulation. The kinematic model is analyzed, a method to search the optimal passing is described, and the optimal solutions are simulated. Finally the experiment is materialized, and perform the passing manipulation with iterative learning control.

Proposes a flexible assembly system, where autonomous manufacturing devices and production management agents communicate with one another to accomplish production tasks. This architecture allows the system to arrange manufacturing devices independently of the type of products assembled and to assemble multiple products in parallel and asynchronous progress. This system also supports plug and produce, a system function that realizes easy reconfiguration. Thus, the system can return quick responses to breakdowns and changes in production quantities. This paper also presents an index for the general evaluation of reconfigurable manufacturing systems. With this index and the plug-and-produce function, the system can be reconfigured appropriately to adapt to changes in the manufacturing environments.

Graspless manipulation is to manipulate objects without grasping by just pushing, tumbling and so on. One of the main difficulties in graspless manipulation is planning. To reduce the load of computation, we adopt a two-step approach: 1) construction and simplification of contact state graph at geometry level; and 2) planning of manipulation at mechanics levels. In this paper, we focus the latter and propose an algorithm to plan mechanically feasible manipulation. It generates digraphs that represent C-subspaces for all the contact states, and unites them into one big graph, which we call a manipulation-feasibility graph. The manipulation plan can be obtained by searching the graph. This algorithm is implemented for planar graspless manipulation by multiple robot fingers, and the planned results obtained are shown.

This paper proposes a flexible assembly system that manages production by negotiating with autonomous manufacturing devices. With this mechanism, physical layout of the system is independent of the type of products to be assembled, and the system can asynchronously process multiple parallel production tasks. This system also supports plug and produce: a system function that enables easy reconfiguration. Thus, the system can be quickly accommodated to breakdowns and changes in production quantities. This paper also deals with an index for general evaluation of the manufacturing systems. With this index, we can plan appropriate improvement of the system layouts. Then we reconfigure the system using plug and produce function, in order to accommodate it to the changes in the manufacturing environments.

There are some researches which perform complex manipulation tasks with low degree-of-freedom manipulator by sliding, rolling, releasing etc. They are called dynamic manipulation. In this paper, tossing manipulation is introduced as a form of dynamic manipulation. A 1-DOF manipulator swings its arm to roll/slide an object on it, and then tosses it to locate to goal position. This paper shows an analysis and a simulation of kinematic model of tossing manipulation. To locate an object to desired position, inverse problem is solved with various optimizations. Finally some experiments and their consideration are discussed.

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.

This paper mainly discusses the characteristics of free sliding motion in releasing manipulation. Releasing manipulation is such a manipulation, in which an object is given initial velocities by striking and slides on a surface to stop at the pre-assigned destination under the action of friction. After the properties of releasing manipulation are given out based on our previous work, the trajectory, the relationship of the sliding motion and the object shape, and the motion monotonicity of any one object with arbitrary shape are intensively discussed. Several important results are originally obtained. Finally, a simple and effectual approach of determining the necessary initial velocity of the object is presented with the above obtained results.

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.

The properties of releasing manipulation are given. To improve the precision of object posture and decrease trial numbers, two iterative learning control schemes, learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in an experiment-oriented way. These two methods are based on a linearized model. The experimental results show that these methods are effective. After discussing the characteristics of these control methods, we postulate that in the case of where the system does not have enough knowledge, LCBCC is the only choice and to learn system knowledge, after enough experience has been acquired, LCBOP is better than LCBCC, form the view point of convergence rate and precision.

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.

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.

This paper focuses on the manipulation of multiple objects, for example, to rake up many objects on a table, lift them up and put them in order, which is difficult to perform for a single manipulator system. This kind of operation becomes possible or much more effective by the cooperation of two manipulators. The lifting up operation of multiple objects by putting them between the two end-effectors is discussed. The use of visual or touch-sensory feedback for the operation, as humans do, cannot prevent the loss of balance for the manipulator system. For the purpose of safe operation, the kinematic analysis and a method of predetermining the pushing force are proposed. The experimental results indicate the effectiveness of the proposed method using simulations with various estimated errors.

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.

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.

We discuss various kinds of manipulation in this paper. Conventional manipulation is made when an object is rigidly grasped by a robot hand. Previously, different kinds of manipulation, in which a manipulated object is not fixed onto a robot hand, were studied. They have dexterity but uncertainty. In this paper, we give a classification of manipulation, and introduce graspless manipulation and releasing manipulation which are some dexterous manipulation methodologies. Finally, we propose a concept of "cost saving manipulation" which aims at a selection of the easiest and the most suitable series of various manipulation methods.

This paper deals with a motion planning of mobile robots during the cooperative transport of a large abject by a group of multiple mobile robots (a robot group). In spite of the complexity of the relationship between the several kinds of requirements for this problem, motion planning should be done in real time according to each robot's torque limit to apply its force to the object. Based on the algorithm of Ota et al. (1995), the authors propose the following approach: (1) derive handling force applied to the object by each robot, and (2) determine the role for each robot (to handle the object or to regrasp it) by each robot's torque limit and optimization of a penalty index indicating performance for obstacle avoidance, stable grasping, and reduction of force. Effectiveness of the proposed method is verified by a transferring simulation.

The stability of an object manipulated by multiple robots (serial-link or wire-driven mechanisms) in contact tasks is investigated. We show that resultant force/moment applicable to the object through the contacts is represented as a polyhedral convex region in force/moment space. Then we propose a stability measure and a method of calculating it approximately by linear programming technique. We also present a linear programming-based algorithm to judge the possibility of excessive internal force in contact tasks by position-controlled robots.

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.

In this paper, first we propose a new kind of manipulation-releasing manipulation, that is, by some means, the manipulator gives the object initial translational and rotational velocities to make the object slide on a surface by its inertia under the action of friction. Second, we give the basic equations and analyze the motion of the object. Third, based on numerical calculation, we summarize the motion characteristics, especially for a rectangle. Finally, we perform experiments to verify the validity of this manipulation.

Graspless manipulation refers to operations in which a robot attempts to change the position and posture of an object without grasping it. Typical examples of graspless manipulation which have been studied so far are pushing and tumbling. This paper proposes a new method of graspless manipulation, pivoting, in which robot fingers maneuver an object in the same way that person moving furniture, as if an object walks on the floor by using appropriate vertices as virtual feet. This paper introduces the concept of this pivoting operation, describes its characteristics and analyzes it mathematically, points out the need for visual guidance for the precise performance of pivoting, and presents experiments with pivoting manipulation in connection with visual guidance.