双腕ロボットによる料理作業実現のための食材の切断に関する研究～抽象操作記述と対象認識に基づく動的軌道生成～

本研究の目的は，双腕ロボットシステムを用いて人のように料理作業を行うことを題材とし，その作業の中でも基本的な操作のひとつである，食材の切断操作を実現することである．具体的には，食材の大まかな位置と，それを切断するという命令が与えられたことを前提として，対象の食材を実際に切断する為に必要な対象食材の位置姿勢の認識と，切断操作を実行するときの手先軌道の動的な生成を実現することである．認識する食材や切断操作には多くの種類があるが，本研究では特定の対象や操作を対象とした作り込みではなく，それらを作り込む際に利用できる一般化された再利用性の高いスキルとして操作を実現することを目指した. また，抽象的な作業記述を操作という単位に分割し，抽象的な操作の記述から具体的なロボットの動作軌跡を実環境に適応させて生成するための実装をスキルとして定義し，さらにスキルの内部の記述をスキル層，応動層，動作層に分けて構造化することで，抽象作業記述から動的にロボットの動作生成を行う過程を体系立てた．本研究では，食材の切断という課題の達成に必要なセンシングの要件，操作の要件を明らかにし，これに基づいた食材認識手法と切断操作の手法を提案し，これらの実行要件を満たすロボットシステムを構築し．提案手法の有効性を実験によって検証した．食材の認識では，ロボットの手先に装備したRGB-Dカメラを用いて複数の視点から得られた点群情報を統合することで食材全体の形状取得し，安定的に位置姿勢を検出する手法を提案した．食材の切断操作では，切断中に刃先に加わる反力のフィードバックを利用して動的に軌道生成を行い，食材の大きさや硬さに関する個体差や実行環境の違いを適応的に吸収する手法を提案した．これらの手法を，構築したロボットシステムに実装し，実際に食材の切断に成功した．電気通信大学201

Modelling and Simulation of a Manipulator with Stable Viscoelastic Grasping Incorporating Friction

Design, dynamics and control of a humanoid robotic hand based on anthropological dimensions, with joint friction, is modelled, simulated and analysed in this paper by using computer aided design and multibody dynamic simulation. Combined joint friction model is incorporated in the joints. Experimental values of coefficient of friction of grease lubricated sliding contacts representative of manipulator joints are presented. Human fingers deform to the shape of the grasped object (enveloping grasp) at the area of interaction. A mass-spring-damper model of the grasp is developed. The interaction of the viscoelastic gripper of the arm with objects is analysed by using Bond Graph modelling method. Simulations were conducted for several material parameters. These results of the simulation are then used to develop a prototype of the proposed gripper. Bond graph model is experimentally validated by using the prototype. The gripper is used to successfully transport soft and fragile objects. This paper provides information on optimisation of friction and its inclusion in both dynamic modelling and simulation to enhance mechanical efficiency

Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter

When operating in unstructured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects from cluttered bins, shelves, or tables. Mechanical Search describes the class of tasks where the goal is to locate and extract a known target object. In this paper, we formalize Mechanical Search and study a version where distractor objects are heaped over the target object in a bin. The robot uses an RGBD perception system and control policies to iteratively select, parameterize, and perform one of 3 actions -- push, suction, grasp -- until the target object is extracted, or either a time limit is exceeded, or no high confidence push or grasp is available. We present a study of 5 algorithmic policies for mechanical search, with 15,000 simulated trials and 300 physical trials for heaps ranging from 10 to 20 objects. Results suggest that success can be achieved in this long-horizon task with algorithmic policies in over 95% of instances and that the number of actions required scales approximately linearly with the size of the heap. Code and supplementary material can be found at http://ai.stanford.edu/mech-search .Comment: To appear in IEEE International Conference on Robotics and Automation (ICRA), 2019. 9 pages with 4 figure

Pushing with a physics-based model

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Page numbering occurs only at the beginning of each chapter, the contents and the bibliography. Cataloged from PDF version of thesis.Includes bibliographical references (p. 67-[70]).Humans often push when grasping or lifting is inconvenient or infeasible, because pushing requires fewer contacts and fights against only a fraction of the object's weight. However, pushing results are hard to predict, because the physical parameters that govern the pushing motion are difficult to measure. We derived a physics-based box pushing model and implemented a feedback-based pushing pipeline using the model. Experimental results show that our pushing model has fair predictive power and our pushing pipeline can reliably push the target to the goal. We compared our physics-based method to a minimalistic baseline pushing method and showed that our method is more accurate and reliable.by Huan Liu.M.Eng

Integrated Grasp and Motion Planning using Independent Contact Regions

Traditionally, grasp and arm motion planning are considered as separate tasks. This might lead to problems such as limitations in the grasp possibilities, or unnecessary long times in the solution of the planning problem. This thesis presents an integrated approach that only requires the initial con guration of the robotic arm and the pose of the target object to simultaneously plan a good hand pose and arm trajectory to grasp the object. The planner exploits the concept of independent contact regions to look for the best possible grasp. In this document, two di erent methods have been considered to search for good end-e ector poses. One biases a sampling approach towards favorable regions using principal component analysis, and the other one considers the capabilities of the robotic arm to decide the most promising hand poses. The performance of the methods is evaluated using di erent scenarios for the humanoid robot SpaceJustin. In order to validate the paths, some scenarios were replicated in the laboratory and the generated paths were executed on the real robot

Grasp plannind under task-specific contact constraints

Several aspects have to be addressed before realizing the dream of a robotic hand-arm system with human-like capabilities, ranging from the consolidation of a proper mechatronic design, to the development of precise, lightweight sensors and actuators, to the efficient planning and control of the articular forces and motions required for interaction with the environment. This thesis provides solution algorithms for a main problem within the latter aspect, known as the {\em grasp planning} problem: Given a robotic system formed by a multifinger hand attached to an arm, and an object to be grasped, both with a known geometry and location in 3-space, determine how the hand-arm system should be moved without colliding with itself or with the environment, in order to firmly grasp the object in a suitable way. Central to our algorithms is the explicit consideration of a given set of hand-object contact constraints to be satisfied in the final grasp configuration, imposed by the particular manipulation task to be performed with the object. This is a distinguishing feature from other grasp planning algorithms given in the literature, where a means of ensuring precise hand-object contact locations in the resulting grasp is usually not provided. These conventional algorithms are fast, and nicely suited for planning grasps for pick-an-place operations with the object, but not for planning grasps required for a specific manipulation of the object, like those necessary for holding a pen, a pair of scissors, or a jeweler's screwdriver, for instance, when writing, cutting a paper, or turning a screw, respectively. To be able to generate such highly-selective grasps, we assume that a number of surface regions on the hand are to be placed in contact with a number of corresponding regions on the object, and enforce the fulfilment of such constraints on the obtained solutions from the very beginning, in addition to the usual constraints of grasp restrainability, manipulability and collision avoidance. The proposed algorithms can be applied to robotic hands of arbitrary structure, possibly considering compliance in the joints and the contacts if desired, and they can accommodate general patch-patch contact constraints, instead of more restrictive contact types occasionally considered in the literature. It is worth noting, also, that while common force-closure or manipulability indices are used to asses the quality of grasps, no particular assumption is made on the mathematical properties of the quality index to be used, so that any quality criterion can be accommodated in principle. The algorithms have been tested and validated on numerous situations involving real mechanical hands and typical objects, and find applications in classical or emerging contexts like service robotics, telemedicine, space exploration, prosthetics, manipulation in hazardous environments, or human-robot interaction in general

Grasp Synthesis in Cluttered Environments for Dexterous Hands

We present an algorithm for efficiently generating collision-free force-closure grasps for dexterous hands in cluttered environments. Computing a grasp is complicated by the high dimensionality of the hand configuration space, and the high cost of validating a candidate grasp by collision-checking and testing for force-closure. When an object is placed in a new scene, we use a novel cost function to focus our search to good regions of hand pose space for a given preshape. The proposed cost function is fast to compute and encapsulates aspects of the object, the scene, and the force-closure of the ensuing grasp. The low cost candidate grasps produced by the search are then validated. We demonstrate the generality of our approach by testing on the 3-fingered 4DOF Barrett hand and the anthropomorphic 22DOF Shadow hand. Our results show that the candidate grasps generated by our algorithm consistently have high probability of being valid for various hands, objects and scenes. Finally, we describe an implementation on a WAM arm with a Barrett Hand.</p