Kazuyuki Nagata

Delivery by hand between human and robot based on fingertip force-torque information

Presents a delivery by hand between a multifingered hand and a human based on force-torque sensing. Of all cooperated tasks between humans and robots, the most important and basic factor is delivery of objects by hand. To insure positive delivery, the robot should open its hand only after making sure that the object has been received by the human. The human releases the object only after confirming that the robot can actually grasp the object stably. The features of the hand-delivery system introduced in the paper include that (1) the robot recognizes successful transfer of the object to a human when the human changes the grasping condition, (2) the human guides the position and orientation of the grasped object to a stable grasp, and (3) the robot, in response to the above guide, regrasps the object to realize a stable grasping condition. The paper introduces evaluation for stable grasp, and presents a new technique for sensor based regrasp manipulation and delivery by hand between a human and robot based on force-torque sensing which reflects the result of stability evaluation. Finally, experimental results are shown to demonstrate the effectiveness of the proposed scheme.

Planning reorientation of an object with a multifingered hand

A multifingered hand can reorient an object by regrasping it in a hand. This paper presents a planner which plans a sequence of repositioning of fingers for horizontal rotation of an object for a desired angle. The authors previously developed an algorithm for computing contact positions of fingers where they can maintain equilibrium. The algorithm is used to compute a new contact position of a removed finger where there is a finger which can be removed next. Based on these computations, the authors develop an algorithm for searching for a sequence of repositioning of fingers. Examples show that the algorithm can efficiently find sequences which require the number of repositioning steps.<<ETX>>

Manipulation by a parallel-jaw gripper having a turntable at each fingertip

This paper presents dextrous manipulation by a non-articulated multifingered hand. To achieve dextrous manipulation by a non-articulated multifingered hand, a parallel-jaw gripper which has a turntable at each fingertip has been developed. This hand has only one free rotation at each fingertip, but can achieve various manipulations. The design of this hand and manipulation using this hand are easier than with an articulated multifingered hand. In this paper, the mechanism of the new hand is presented and the manipulations possible with this hand when an object is placed in the lower pair are analyzed. The required conditions for achieving these manipulations are calculated.<<ETX>>

Pick and place planning for dual-arm manipulators

This paper proposes a method for planning the pick-and-place motion of an object by dual-arm manipulators. Our planner is composed of the offline and the online phases. The offline phase generates a set of regions on the object and the environment surfaces and calculates several parameters needed in the online phase. In the online phase, the planner selects a grasping pose of the robot and a putting posture of the object by searching for the regions calculated in the offline phase. By using the proposed method, we can also plan the trajectory of the robot, and the regrasping strategy of the dual-arm. Here, the putting posture of the object can be planned by considering stability of the object placed on the environment. The effectiveness of the proposed method is confirmed by simulation and experimental results by using the dual-arm robot NX-HIRO.

Interpretation of grasp and manipulation based on grasping surfaces

A method is proposed to classify and describe grasping and manipulation. It is based on the three functions of grasping surfaces, namely object-supporting, pressing and wrapping functions. The method provides a simple description for grasping and manipulation and it can express their physical fundamentals. The description is illustrated through example grasp and manipulation by a human hand. Because it is independent from specific hardware, the obtained interpretations of tasks by a human hand can be easily applied to robotic hand systems. The new method can be expected to change current approaches to designing robot hands.

Picking up an indicated object in a complex environment

This paper presents a grasping system for picking up an indicated object in a complex real-world environment using a parallel jaw gripper. The proposed grasping scheme comprises the following three main steps: 1) A user indicates a target object and provides the system with a task instruction on how to grasp it, 2) the system acquires geometric information about the target object and constructs a 3D environment model around the target by stereo vision using the information obtained from the task instruction, and 3) the system finds a grasp point based on grasp evaluation using the acquired information. As an example of the scheme, we examined the picking up of a cylindrical object by grasping at the brim. An important and advantageous feature of this scheme is that the user can easily instruct the robot on how to perform the object-picking task through simple clicking operations, and the robot can execute the task without exact models of the target object and the environment being available in advance.

Probabilistic approach for object bin picking approximated by cylinders

This paper proposes a method for bin-picking for objects without assuming the precise geometrical model of objects. We consider the case where the shape of objects are not uniform but are similarly approximated by cylinders. By using the point cloud of a single object, we extract the probabilistic properties with respect to the difference between an object and a cylinder and consider applying the probabilistic properties to the pick-and-place motion planner of an object stacked on a table. By using the probabilistic properties, we can also realize the contact state where a finger maintain contact with the target object while avoiding contact with other objects. We further consider approximating the region occupied by fingers by a rectangular parallelepiped. The pick-and-place motion is planned by using a set of regions in combination with the probabilistic properties. Finally, the effectiveness of the proposed method is confirmed by some numerical examples and experimental result.

Grasp planning for parallel grippers with flexibility on its grasping surface

This paper proposes a method for planning a grasping posture for a parallel gripper attached at the tip of a robot manipulator. In order to robustly grasp several objects with various shapes, we consider a gripper having flexible sheets attached at the finger surface. We show that, by constructing a set of triangular mesh of the grasped objects polygon model, we can plan the grasping posture taking the flexibility of the grasping surface into consideration. We also show that we can define several parameters used when planning the grasping posture for each set. The effectiveness of the proposed method is verified by numerical examples and experimental results.

Acquisition of an object model by manipulation with a multifingered hand

A multifingered hand with touch sensors has grasping and manipulating functions, and has the function of a sensor probe. We have been studying the merging of these two functions, the acquisition of an object model by manipulation with a multifingered hand, and determining the next finger action based on the acquired information. By manipulating objects while acquiring the object models, we expect to be able to manipulate many types of objects and unknown objects. This paper discusses a multifingered hand system for acquiring an object model while manipulating the object, by using the contact point information between the fingertips and object.

Towards snap sensing

Automating snap assemblies is highly desirable but challenging due to their varied geometrical configurations and elastic components. A key aspect to automating snap assemblies is robot state estimation and corrective motion generation, here defined as snap sensing. While progress is being made, there are yet no robust systems that allow for snap sensing. To this end we have integrated a framework that consists of a control strategy and control framework that generalises to cantilever snaps of varying geometrical complexity. We have also integrated a robot state verification method (RCBHT) that encodes FT data to yield high-level intuitive behaviours and perform output verification. Optimisation procedures and Bayesian filtering have been included in the RCBHT to increase robustness and granularity. The system provides belief states for higher level behaviours allowing probabilistic state estimation and outcome verification. In this work, preliminary assembly failure characterisation has been conducted and provides insights into assembly failure dynamics. The results, though still in simulation, are promising as the framework has effectively executed cantilever snap assemblies and robust robot state estimation with parts of varying complexity in two different robotic systems.