Tatsuya Nagatani

What can be inferred from a tactile arrayed sensor in autonomous in-hand manipulation?

In this paper, we present a concept of using tactile sensors as sufficient tools in localizing, recognizing an object in robotic in-hand manipulation tasks. Our approach operates on a moderately intensive array data that are obtained from a tactile sensor when a robotic gripper grasps an object that is small relatively to fingers. In stead of using tactile data as an array of discrete numbers, we treat it as a grayscale image. By working with successive images from tactile sensor exploiting image processing tools, we are able to extract rich information about the contact condition between an object and the gripper. Experimental results show that from the processed data, we can realize the grasped objects position/orientation, contact shape, especially the stick-slip condition on the contact surface that is derived for the first time for this sensor. Based on localized displacement phenomenon of a sliding soft object, we proposed an approach to detect the slippage quantitatively, and conducted a wide range of experiment in term of characteristics of objects movement. We also conducted a model for an object-grasping gripper with tactile feedback in various postures of the object, and a corresponding experiment setup to validate computed results. Presented result is expected to be used widely in the community due to its simplicity and reliability.

Multiobjective layout optimization of robotic cellular manufacturing systems

This paper proposes a multiobjective layout optimization method for the conceptual design of robot cellular manufacturing systems. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. In this paper, the design criteria for robot cellular manufacturing system layout designs are clarified, and objective functions are formulated. Next, layout design candidates are represented using a sequence-pair scheme to avoid interference between assembly system components, and the use of dummy components is proposed to represent layout areas where components are sparse. A multiobjective genetic algorithm is then used to obtain Pareto optimal solutions for the layout optimization problems. Finally, several numerical examples are provided to illustrate the effectiveness and usefulness of the proposed method.

Derivation of optimal robust grasping strategy under initial object pose errors

In a robotic cell, an assembly robot has to grasp various parts robustly even under some uncertainties in their initial poses. For this purpose, it is necessary to design robust grasping strategies for robotic hands. In this paper, we propose a method to derive an optimal robust grasping strategy from a given initial pose error region of a target object. Based on the pushing operation analysis, it is possible to simulate multi-fingered hand grasping and derive a permissible initial pose error region of a target object from which planned grasping is successful. Adopting an active search algorithm proposed by the authors, we can find the optimal grasping strategy efficiently. As an example, we derive the optimal grasping strategy for grasping a circular object by a three-fingered hand.

Simultaneous optimization of layout and task schedule for robotic cellular manufacturing systems

A layout optimization method for multi-robot manufacturing systems was proposed.Task allocation and component positioning were simultaneously optimized.The layout design problem was formulated as a three-objective optimization problem.A multiobjective genetic algorithm was used to solve the optimization problem. Multi-robot cellular manufacturing systems utilize several articulated industrial robots that cooperatively perform a large number of complex operations when assembling products. To enhance the system performance when designing layouts for such robotic cellular manufacturing systems, the positions of robots and other manufacturing system components must be appropriately determined by considering the sequence of tasks the robots conduct during the assembly process. This paper proposes a new multiobjective layout design optimization technique for robotic cellular manufacturing system layouts that can simultaneously determine the positions of manufacturing components and also task scheduling. First, in this paper, sequence-pair representation is used to specify layout design candidates that inherently avoid interference between assembly system components, and the use of dummy components is introduced to represent layout areas that provide necessary and appropriate spacing between manufacturing system components. In addition, task allocations for each robot are considered as discrete design variables. The design criteria for robot cellular manufacturing system layout designs are clarified and the layout design problem is formulated as a multiobjective optimization problem. To solve the optimization problem, a method based on a multiobjective genetic algorithm is proposed and numerical examples are provided to demonstrate the effectiveness of the proposed method.

Robust grasping strategy for assembling parts in various shapes

In a robotic cell, assembly robots have to grasp parts in various shapes robustly and accurately even under some uncertainties in the initial poses of the parts. For this purpose, it is necessary to develop a universal robotic hand and robust grasping strategies, i.e. finger motions that can achieve planned grasping robustly against the initial pose uncertainty of parts. In this paper, we propose a methodology to plan robust grasping strategies of a universal robotic hand for assembling parts in various shapes. In our approach, parts are aligned toward planned configurations during grasping actions, and the robustness of grasping strategies is analyzed and evaluated based on pushing operation analysis. As an application example, we plan robust grasping strategies for assembling a three-dimensional puzzle, and experimentally verify the robustness and effectiveness of the planned strategies for this assembly task. Graphical Abstract

Ecological Interface Design for Teaching Assembly Operations to Industrial Robot

Abstract This study analyzes the work domain of position teaching in terms of means-end relations for the purpose of developing a practical support tool for human workers engaged in industrial robot teaching. A mechanical explanation model is introduced into the analysis to capture the force-displacement relationship inherent in and informative on the work system. The resulting models are used for the analysis of the robot operators’ decisions in search of accurate operation positions, and this in turn helps to clarify a rational operation strategy for making use of an invariant frame of reference in the position search space. After these findings, a prototype GUI is proposed that can provide effective information supports for different granularities of the activity in position teaching. Based on the principles of Ecological Interface Design, the proposed GUI represents the activity-related information in a way that encourages the robot operators’ intuitive and strategic operations.